Understanding the Spread of Infectious Diseases 9781536189179, 1536189170

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INFECTIOUS DISEASES AND MICROBIOLOGY

UNDERSTANDING THE SPREAD OF INFECTIOUS DISEASES

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INFECTIOUS DISEASES AND MICROBIOLOGY

UNDERSTANDING THE SPREAD OF INFECTIOUS DISEASES

ANDREW J. HINERMAN EDITOR

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NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the Publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data ISBN: 978-1-53618-H%RRN

Published by Nova Science Publishers, Inc. † New York

CONTENTS Preface Chapter 1

Chapter 2

Index

vii Coronaviruses: Understanding the Spread of Infectious Diseases and Mobilizing Innovative Solutions Committee on Science, Space, and Technology Infectious Disease Modeling: Opportunities to Improve Coordination and Ensure Reproducibility United States Government Accountability Office

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PREFACE Outbreaks of infectious diseases—such as Ebola, Zika, and pandemic viruses—have raised concerns from Congress about how federal agencies use modeling to, among other things, predict disease distribution and potential impacts. Chapter 1 discusses emerging infectious diseases, in light of the recent coronavirus outbreak, and the modeling tools used to detect, predict, and understand the spread of such diseases. Chapter 2 examines the extent to which HHS used models to inform policy, planning, and resource allocation for public health decisions; the extent to which HHS coordinated modeling efforts; steps HHS generally takes to assess model development and performance; and the extent to which HHS has addressed challenges related to modeling. Chapter 1 - This is an edited, reformatted and augmented version of Hearing before the Committee on Science, Space, and Technology, House of Representatives One Hundred Sixteenth Congress Second Session, Serial No. 116–71 dated March 5, 2020. Chapter 2 - Outbreaks of infectious diseases—such as Ebola, Zika, and pandemic influenza—have raised concerns from Congress about how federal agencies use modeling to, among other things, predict disease distribution and potential impacts. In general, a model is a representation of reality expressed through mathematical or logical relationships. Models of infectious diseases can help decision makers set policies for disease

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control and may help to allocate resources. GAO was asked to review federal modeling for selected infectious diseases. This chapter examines (1) the extent to which HHS used models to inform policy, planning, and resource allocation for public health decisions; (2) the extent to which HHS coordinated modeling efforts; (3) steps HHS generally takes to assess model development and performance; and (4) the extent to which HHS has addressed challenges related to modeling. GAO reviewed documents and interviewed HHS officials, state officials, and subject matter experts. GAO identified practices commonly used to assess infectious disease model performance and reviewed 10 selected modeling efforts to see if they followed these practices.

In: Understanding the Spread … Editor: Andrew J. Hinerman

ISBN: 978-1-53618-892-9 © 2021 Nova Science Publishers, Inc.

Chapter 1

CORONAVIRUSES: UNDERSTANDING THE SPREAD OF INFECTIOUS DISEASES AND MOBILIZING INNOVATIVE SOLUTIONS Committee on Science, Space, and Technology Thursday, March 5, 2020 House of Representatives, Washington, D.C. The Committee met, pursuant to notice, at 9:03 a.m., in room 2318 of the Rayburn House Office Building, Hon. Ami Bera [Chairman of the Committee] presiding.

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This is an edited, reformatted and augmented version of Hearing before the Committee on Science, Space, and Technology, House of Representatives One Hundred Sixteenth Congress Second Session, Serial No. 116–71 dated March 5, 2020.

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COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY, U.S. HOUSE OF REPRESENTATIVES, HEARING CHARTER, CORONAVIRUSES: UNDERSTANDING THE SPREAD OF INFECTIOUS DISEASES AND MOBILIZING INNOVATIVE SOLUTIONS March 5, 2020; 9:00 a.m.; 2318 Rayburn House Office Building

Purpose The purpose of the hearing is to discuss emerging infectious diseases, in light of the recent coronavirus outbreak, and the modeling tools used to detect, predict, and understand the spread of such diseases. The Committee will discuss how some infectious agents spread from animals to humans and how predictive modeling can help control and mitigate the effects of emerging diseases. The Committee will also explore how investments in U.S. research may help combat epidemics and pandemics. Given that COVID-19 is an emerging, rapidly evolving situation, please note that some information is subject to change.

Witnesses 

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Dr. Suzan Murray, Program Director, Smithsonian Global Health Program, Smithsonian’s National Zoo & Conservation Biology Institute. Dr. John Brownstein, Chief Innovation Officer, Boston Children’s Hospital; Professor, Harvard Medical School. Dr. Peter Hotez, Professor and Dean, National School of Tropical Medicine, Baylor College of Medicine; Co-Director, Texas Children's Hospital Center for Vaccine Development.

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Dr. Tara Kirk Sell, Senior Scholar, Johns Hopkins Center for Health Security; Assistant Professor, Johns Hopkins Bloomberg School of Public Health.

Key Questions 

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What factors contribute to the emergence of new infectious diseases, and how can we learn from past outbreaks to inform next steps? How can we apply predictive modeling to anticipate present day and future geographic distributions of infectious diseases? What are cutting-edge tools that can help decision-makers understand and manage the effects of emerging infectious diseases? How can investments in U.S. research contribute to global preparedness and response to emerging infectious diseases? What steps can we take to mitigate harmful social stigmas surrounding infectious diseases?

Background Since 1980, outbreaks of emerging infectious diseases have been occurring with greater frequency and have been causing higher numbers of human infections.1 Nearly 75% of all emerging infectious diseases identified in humans during the 21st century have been caused by zoonotic pathogens,2 meaning the pathogen spreads from animals to humans, often Katherine Smith et al., “Global Rise in Human Infectious Disease Outbreaks,” Journal of the Royal Society Interface, volume 11 (August 2014); Stephen Morse et al., "Prediction and Prevention of the Next Panemic Zoonosis," The Lancet, vol. 380 (December 1, 2012), pp. 1956-1965; and A. Marm Kilpatrick and Sarah Randolph, "Drivers, Dynamics, and Control of Emerging Vector-Borne Zoonotic Diseases," The Lancet, vol. 380 (December 1, 2012), pp. 1946-1955. 2 Smithsonian’s National Zoo & Conservation Biology Institute, Global Health Program. 1

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through a vector (e.g., a mosquito).3 Each year, zoonotic pathogens cause an estimated one billion cases of human illness, including 15 million deaths.4 An epidemic is an unusual, often sudden, increase in the number of cases of a disease above what is normally expected. An outbreak carries the same definition but is typically used for a more limited geographic area. A pandemic refers to an epidemic that has spread over several countries or continents, usually affecting many people. Changing ecosystems, economic development and land use, climate and weather, and international travel and commerce are all examples of ecological, environmental, and social factors that will increase the emergence and spread of infectious diseases in the future.5 Coronaviruses are a large family of zoonotic viruses that cause respiratory illness ranging from the common cold to more severe diseases like MERS (Middle East respiratory syndrome) and SARS (severe acute respiratory syndrome).6 There are seven coronaviruses known to infect humans, including the novel coronavirus (COVID-19) first identified in Wuhan City, Hubei Province, China in December 2019.7 The most common symptoms among confirmed COVID-19 patients include high fever, cough, and shortness of breath.8

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Katherine Smith et al., “Global Rise in Human Infectious Disease Outbreaks,” Journal of the Royal Society Interface, volume 11 (August 2014); and Stephen Morse et al., "Prediction and Prevention of the Next Pandemic Zoonosis," The Lancet, vol. 380 (December 1, 2012), pp. 1956-1965. William Karesh et al., "Ecology of Zoonoses: Natural and Unnatural Histories," The Lancet, vol. 380 (December 1, 2012), pp. 1936-1945; Barbara Han et al., “Rodent Reservoirs of Future Zoonotic Diseases,” Proceedings of the National Academy of Sciences of the United States of America (PNAS), vol. 112, no. 22 (June 2, 2015), pp. 7039-7044; and Wu XiaoXu et al., “Impact of Global Change on Transmission of Human Infectious Diseases,” Science China, (April 19, 2013). Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. Johns Hopkins Center for Health Security, Coronaviruses: SARS, MERS, and 2019-nCoV, January 21, 2020. Centers for Disease Control and Prevention. Human coronavirus types. January 10, 2020. The virus has been named “SARS- CoV-2” and the disease it causes has been named “coronavirus disease 2019” (abbreviated “COVID-19”). Chaolin Huang, Yeming Wang, and Xingwang Li, et al., "Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China," The Lancet, January 24, 2020.

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Global Effects of COVID-19 The size of the COVID-19 outbreak has created a public health crisis with significant international dimensions. As of March 2, 2020, COVID-19 has been detected in 60 locations internationally, including in the United States.9 While the overwhelming number of cases and deaths have occurred in China, significant outbreaks are now arising in other countries such as South Korea, Italy, and Iran. The global spread of the COVID-19 virus prompted the World Health Organization (WHO) to take action by declaring a “public health emergency of international concern” on January 30, 2020, only the sixth time in the organization’s history that it has declared a public health emergency since it gained the authority to do so in 2005.10 The WHO’s declaration is advisory in nature and cannot compel any nation to undertake any specific policy or action. Nevertheless, it is viewed as an important signal of severe concern from the world’s leading international public health organization, and it may galvanize further responses to the outbreak at the national and sub-national level.

Figure 1. Centers for Disease Control and Prevention, “Coronavirus Disease 2019 (COVID-19),” COVID-19 Situation Summary. 10 Sui-Lee Wee, Donald G. McNeil Jr., and Javier C. Hernandez, “W.H.O. Declares Global Emergency as Wuhan Coronavirus Spreads,” New York Times, January 30, 2020. 9

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The impacts of COVID-19 will extend broadly throughout the U.S. and global economies. Depending on the size of the eventual outbreak and the length of time that it persists, the U.S. economy could suffer significant disruption due to a decline in tourism from China and elsewhere, decreased demand for American exports, the disruption of global supply chains for American companies, and disruptions to daily life in the United States.11 The financial sector has taken note of these concerns, as the S&P 500 index experienced its worst week since the 2008 financial crisis last week, although it rebounded somewhat on March 2, 2020.12 The technology and automotive sectors could be particularly vulnerable due to the potential for shortages to occur among critical parts for their production lines.13 The U.S. international public health response to COVID-19 has centered around the goal of overseas containment through the imposition of severe travel restrictions on foreign nationals from China and Iran, the issuance of heightened warnings for U.S. citizens traveling to South Korea and Italy, and the use of mandatory quarantines for American citizens returning from some affected areas.14 The United States has also sent two public health experts to China as part of a WHO team of international disease experts deployed to assist the Chinese government’s response.15 Finally, the United States has offered an additional $100 million in support of the WHO’s international response efforts to study the virus and contain the outbreak.16 Due to the outbreak of the virus within the country, the United States is increasingly shifting its focus to confronting the effects of COVID-19 domestically. On February 26, 2020, President Trump named Vice President Pence to coordinate the government’s response to COVID-19. The White House Paul Davidson, “How is the coronavirus in China casting a widening shadow across the US economy,” USA Today, February 20, 2020. 12 New York Times, “Asian Markets Seesaw, Bonds Rise as Coronavirus Fears Linger,” March 1, 2020. 13 Id. 14 Rob Stein, “U.S. Coronavirus Quarantine and Travel Limits: Needed Protection or Overreaction?” NPR, February 3, 2020. 15 Steven Lee Myers and Edward Wong, “Coronavirus Worsens U.S.-China Ties and Bolsters Hawks in Washington,” New York Times, February 19, 2020. 16 Reuters, “U.S. announces aid for China, other countries impacted by coronavirus,” February 7, 2020. 11

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submitted a $2.5 billion plan to Congress to address the outbreak. Senate Minority Leader Chuck Schumer proposed to increase the President’s emergency request substantially, to $8.5 billion in new funds, including $3 billion for a public health emergency fund, $1.5 billion for the Centers for Disease Control and Prevention (CDC), $1 billion for vaccine development, and $2 billion for reimbursing states and cities for efforts they have so far made to monitor and prepare for potential cases of the virus.17

COVID-19: A Rapidly Evolving Situation In an effort to contextualize COVID-19 as the outbreak is rapidly evolving, attempts have been made to explain the threat through comparisons to other well-known outbreaks, like the seasonal flu, SARS, and H1N1. For example, the CDC has confirmed two COVID-19-related deaths as of March 2, 2020,18 while this year’s seasonal flu has killed more than 18,000.19 However, it is important to note that such comparisons are complicated while the virus continues to spread. Not all those who have contracted the virus have been diagnosed, and most of those who have been diagnosed have neither died nor recovered yet. When the H1N1 influenza pandemic broke out in the spring of 2009, the mortality rate appeared to be 10%. However, as time progressed, it became clear that there were many cases of people whose infections were so mild that they didn’t seek medical help. Ultimately, the death rate of H1N1 was below 0.1%.20 Like with any other outbreak, outcomes of COVID-19 cases will vary based on the resources available to the impacted communities. H1N1, for Jordain Carney, “Schumer requesting $8.5 billion in emergency funding on coronavirus,” February 26, 2020. 18 “Coronavirus Disease 2019 (COVID-19),” Centers for Disease Control and Prevention, March 2, 2020. 19 “Weekly U.S. Influenza Surveillance Report (FluView),” Centers for Disease Control and Prevention, February 15, 2020. 20 Emily Baumgaertner, “How deadly is the new coronavirus? Scientists race to find the answer,” LA Times, February 12, 2020. 17

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example, had a death rate over four times higher for American Indians and Alaska Natives than for all other racial and ethnic groups combined. Reasons for this include a high prevalence of chronic health conditions, poverty, and delayed access to healthcare.21 In the Hubei province, medical resources are stretched very thin, exacerbated by a lockdown that is slowing the delivery of protective wear for hospital staff.22 Over 3,000 medical workers have now been infected with COVID-19 in China, largely in the Hubei province.23 Meanwhile, according to the CDC, there have been 43 cases of COVID-19 confirmed in the United States as of March 2, 2020 including 26 cases of transmission to people who had not recently been to China or had known contact with someone who had. There have been 48 confirmed cases among individuals repatriated to the United States from Asia, including three from Wuhan and 45 from the Diamond Princess, a cruise ship on which 695 people were infected.24 Epidemiologists believe that, despite the Chinese government’s lockdown of areas surrounding Wuhan, COVID-19 will infect more people in the United States and around the world.25 Like SARS and MERS, it will be more dangerous for elderly patients and those with existing cardiovascular disorders.26 Beyond that, it is difficult at the moment to make predictions about how contagious or deadly COVID-19 will be outside China.

“Deaths Related to 2009 Pandemic Influenza A (H1N1) Among American Indian/Alaska Natives – 12 States, 2009,” Center for Disease Control MMWR Weekly, December 11, 2009. 22 Chris Buckley, Sui-Lee Wee, Amy Qin, “China’s Doctors, Fighting the Coronavirus, Beg for Masks,” New York Times, February 14, 2020. 23 “China says more than 3,000 medical staff infected by COVID-19,” Channel News Asia, February 24, 2020. 24 Coronavirus Disease 2019 (COVID-19), Centers for Disease Control and Prevention. 25 James Hamblin, “You’re Likely to Get the Coronavirus,” The Atlantic, February 24, 2020. 26 Katarina Zimmer, “Why Some COVID-19 Cases Are Worse than Others,” The Scientist, February 24, 2020. 21

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Using Technology to Detect, Predict, and Understand the Spread of Infectious Diseases During outbreaks of novel viruses—especially ones with pandemic potential—public health leaders use epidemiological models to detect, predict, and control the spread and impact of disease.27 These models can assist in answering critical questions, such as: ‘When will the disease reach its peak?’ or ‘How transmissible is the disease?’ or ‘Who in the population should be prioritized for vaccination or treatment?’.28 The researchers using such models require quality data but are limited by time; as time passes and the outbreak progresses, more data become available to analyze.29 Traditional models or techniques that track outbreaks often use manually coded data, like confirmed infections and hospitalizations. However, it can take a long time to collect and verify this data. For example, a physician might identify a cluster of patients with a new set of similar symptoms and contact the CDC for further follow-up and testing. The CDC (or one of its designated laboratories) would then analyze and verify patient specimens before making recommendations and issuing an official alert. While necessary, this process can delay critical policies and interventions during the early stages of an outbreak. One of the key differences between the SARS outbreak in 2003 and COVID-19 is the greater availability and amount of non-traditional data like social media posts, Google Search queries, and online news reports. Researchers are now using artificial intelligence (AI) applications to identify and track outbreaks faster and more precisely. HealthMap, for

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Manoj Gambhir, et al., “Infectious Disease Modeling Methods as Tools for Informing Response to Novel Influenza Viruses of Unknown Pandemic Potential,” Clinical Infectious Diseases, 2015;60(S1):S11-9. Typically, descriptive modeling tries to estimate what probably occurred or is occurring now, while predictive modeling predicts cases in the future. Government Accountability Office, “Emerging Infectious Diseases: Actions Needed to Address the Challenges of Responding to Zika Virus Disease Outbreaks,” May 23, 2017. Manoj Gambhir, et al., “Infectious Disease Modeling Methods as Tools for Informing Response to Novel Influenza Viruses of Unknown Pandemic Potential,” Clinical Infectious Diseases, 2015;60(S1):S11-9.

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example, is a tool that collects and analyzes online informal sources to generate visualizations that show how and where communicable diseases like COVID-19 are spreading.30 The WHO uses HealthMap as part of its Epidemic Intelligence from Open Sources initiative, facilitating early detection of global public health threats.31 While advancements in AI could help predict infectious disease outbreaks before they happen, these methods are considered a supplement to, and not a replacement for, traditional surveillance and diagnostic processes. Decision-makers could use a hybrid approach to allocate resources faster and contain the spread of an outbreak more effectively.

Halting the Spread of Misinformation around Infectious Disease Outbreaks Researchers generally define misinformation as information that is false or misleading but promulgated with sincerity by a person who believes it is true. Disinformation, on the other hand, is shared with the deliberate intent to deceive. The Subcommittee on Investigations & Oversight of the House Committee on Science, Space, and Technology held a hearing on this important topic on September 26, 2019, particularly focusing on the tools needed to combat these threats.32 The outbreak of global viruses is often followed by the spread of misinformation about the virus, such as its origins, causes, and government response. The WHO has even labeled this outbreak an “infodemic,” meaning there is “an over-abundance of information – some accurate and some not – that makes it hard for people to find trustworthy sources and reliable guidance when they need it.”33 There have been multiple reports

Will Knight, “How AI is Tracking the Coronavirus Outbreak,” WIRED, February 8, 2020. Alejandro De La Garza, “Coronavirus Researchers Are Using High-Tech Methods to Predict Where the Virus Might Go Next,” TIME, February 11, 2020. 32 “Online Imposters and Disinformation,” hearing before the Subcommittee on Investigations and Oversight, H. Comm. On Sci., Space, and Tech. (September 26, 2019). 33 World Health Organization, “Novel Coronavirus (2019-nCoV) Situation Report -13,” February 2, 2020. 30 31

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documenting the international spread of public health disinformation on COVID-19.34,35,36 Stigma is a central theme of public health misinformation. Johns Hopkins’ Center for Health Security describes stigma as something that comes from an impulse to assign blame during an outbreak of infectious disease. People are often trying to answer basic questions, such as: ‘Where did this come from?’ and ‘How is it spreading?’. To understand and avoid illness, people often create a mental distinction between “us” (the uninfected) and “them” (the infected). According to Johns Hopkins, this phenomenon can contribute to an inaccurate picture of health risk and reflect preexisting social differences and prejudices. A whole country or group of people may be singled out as the source of the problem—rather than the pathogen. Misinformation about infectious diseases is hardly a new phenomenon, but the spread of misinformation is accelerated by social media. Several social media companies have taken steps to mitigate misinformation around COVID-19. In late January, Facebook released a statement saying that their “third-party fact-checkers are continuing their work reviewing content and debunking false claims” related to COVID-19.37 Around the same time, Twitter launched a prompt for individuals searching #coronavirus to receive credible information from the CDC.38 Similarly, users searching “coronavirus” on YouTube are met with a link to the WHO guidance on COVID-19.39 Unfortunately, despite these efforts, misinformation surrounding the virus persists.40,41 34

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Malaka Gharib, “Fake Facts Are Flying About Coronavirus. Not There’s a Plan To Debunk Them,” NPR, February 21, 2020. Beatrice Dupuy and Arijeta Lajka, “Not Real News: An outbreak of virus-related misinformation,” AP News, February 28, 2020. Makena Kelly, “The World Health Organization has joined TikTok to fight coronavirus misinformation,” The Verge, February 28, 2020. Kang-Xing Jin, “Keeping People Safe and Informed About the Coronavirus,” Facebook, January 30, 2020. Jun Chu and Jennifer McDonald, “Helping the world find credible information about novel #coronavirus,” Twitter, January 29, 2020. Ryan Broderick, “YouTube Has Been Cracking Down on Coronavirus Hoaxes, But They Are Still Going Viral,” BuzzFeed News, February 12, 2020. Tony Romm, “Fake cures and other coronavirus conspiracies are flooding WhatsApp, leaving governments and users with a ‘sense of panic’,” Washington Post, March 2, 2020.

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Investments in Research and Development to Prevent and Respond to Outbreaks Recent infectious disease outbreaks have highlighted certain strengths and weaknesses of the international research and development (R&D) response. For example, there is broad consensus that global research efforts were hampered by insufficient collaboration and transparency during the Ebola epidemic in 2014-2015, which led to a slow and uncoordinated response.42 According to the National Academies of Science, Engineering, and Medicine, the mobilization of a rapid and robust research response during the next epidemic will depend not just on what happens during the epidemic, but on what happens before or between epidemics.43 There are numerous ways the United States can work with its international partners on priority research that can curtail ongoing outbreaks and prepare for future ones. For example, the WHO R&D Blueprint is a global strategy and preparedness plan that outlines research actions which can help identify key knowledge gaps and accelerate the development of critical scientific information.44 The WHO activated its R&D Blueprint in early January 2020 in response to the COVID-19 outbreak. Some of the Blueprint’s recommended actions include: 

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R&D for Emerging Pathogens: Understanding where zoonotic viruses originate and how they are transmitted from animals to humans is a key research priority identified by the WHO. U.S. research on the human and ecological drivers of disease spillover could help detect novel pathogens likely to cause severe outbreaks

Tony Romm, “Millions of tweets peddled conspiracy theories about coronavirus in other countries, an unpublished U.S. report says,” Washington Post, February 29, 2020. World Health Organization, “An R&D Blueprint for Action to Prevent Epidemics,” Plan of Action, May 2016. National Academies of Sciences, Engineering, and Medicine 2017, “Integrating Clinical Research into Epidemic Response: The Ebola Experience,” Washington, DC: The National Academies Press. World Health Organization, “A research and development Blueprint for action to prevent epidemics.”

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and facilitate faster and more effective responses to public health emergencies across the globe. R&D for Diagnostics, Therapeutics, and Vaccines: The WHO suggests taking advantage of all available technological innovations to improve survival and recovery. Further, the WHO recommends close collaboration among researchers to expedite the development of tests that quickly identify sick people, but also to optimize the use of currently available treatments and evaluate candidates for new drugs and vaccines. U.S. research in this area could help develop health technologies that would control the effects of disease and increase global preparedness between crises. The House Committee on Science, Space, and Technology held a hearing on vaccine innovation on November 20, 2019.45 R&D for Social Science: According to the WHO, integrating social scientists into outbreak responses helps communities accept and adhere to public health measures aimed at limiting disease transmission.46 Fear, anxiety, and stigma can drive sick people to hide their symptoms to avoid discrimination, prevent some individuals from seeking health care immediately, and discourage others from adopting healthy behaviors.47 U.S. research on how to combat misinformation during outbreaks could improve prevention and control measures and strengthen global public health communication.

Chairman BERA. This hearing will come to order. Without objection, the Chair is authorized to declare recess at any time. Good morning, and welcome today’s hearing on ‘‘Coronavirus: Understanding the Spread of the Infectious Disease, and Mobilizing Innovative Solutions’’. I’ll recognize myself for an opening statement, and then I’ll recognize the “Fighting Flu, Saving Lives: Vaccine Science and Innovation,” hearing before the H. Comm. On Sci., Space, and Tech. (November 20, 2019). 46 World Health Organization and Global Research Collaboration for Infectious Disease Preparedness, “2019 Novel Coronavirus Global Research and Innovation Forum: Towards a Research Roadmap,” February 2020. 47 World Health Organization, “Coronavirus disease 2019 (COVID-19) Situation Report – 35.” 45

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Ranking Member for his opening statement, then we’ll introduce the witnesses. Again, thank you for being here. Obviously, this is an incredibly timely topic. COVID–19 is not the first pandemic we’re going to ever deal with, and it certainly is not going to be the last one, but it is incredibly important that we come together as a nation, and as a planet, to get ahead of this, address it, and, you know, come up with the treatment for it. If we think about, you know, the basis of global health security, it’s a threepronged approach, containment, mitigation, and then treatment. This is the third hearing that I’m chairing on this subject, and the first hearing focused on the containment strategy. That was actually the first hearing that Congress held. Conclusion of that was the initial strategy of trying to contain this disease with travel bans, et cetera, was likely not going to be successful, very difficult. You know, I think what China did was ambitious, it bought us some time but most of us in the public health world—and I’m a physician by background, and ran a large public health system— recognize that we would likely see community cases. It would be very difficult to stop the spread of this disease. The second hearing we had, which was last Thursday, was on mitigation, largely looking at testing. And this was last Thursday, after the first community spread case hit my home county of Sacramento, where a patient was hospitalized at the University of California Davis Health System, where I used to practice. What we discovered was, you know, the testing criteria were probably too rigid, that we were missing a lot of community tests, and we also started to discover the ability to test folks, the availability of test kits, et cetera, was largely not there. I’m pleased, you know, to hear the Vice President yesterday. Things are ramping up, but we probably did lose quite a bit of time, and we are likely going to see many more community cases, probably in all of our congressional districts. So we still, you know, there’s a lot to be learned from kind of the bureaucratic breakdown that prevented us from rapidly getting those tests out there. Today’s hearing is focused on treatment based on science, and what we can learn from how this virus initially developed, what we can learn

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from looking at the Chinese response. We now have a big data set. How did they manage folks? You know, China is a communist country, so they were able to do things that we can’t do as a democratic nation. You know, we respect individual rights and individual freedoms here, but there’s still a lot that we can learn from how they did surveillance, et cetera, especially given the breadth of contact tracing that we likely are going to have to do based on the community cases that we’re going to see all across the United States. We won’t have enough epidemiologists, the CDC (Centers for Disease Control and Prevention) won’t have enough personnel, so what can we learn in how China and Korea—and if you’re looking at the data that’s coming out of Korea now, their aggressive approach to testing, and community-based testing. They were doing 15,000 tests a day, may have actually mitigated and reduced how bad the response could’ve been. So I think that’s going to be incredibly important. We’re also going to look at the science of, you know, how is it spread? How efficiently is it spread? You know, how long can this virus live as a fomite on inanimate objects? So, you know, I think this is an incredibly timely hearing. I think this is, you know, this is the Science Committee, so I’m glad that we’re looking at the science of this, and the science basis of treatment, and, again, I appreciate the witnesses that are here that are bringing their scientific expertise to help us better understand this disease. [The prepared statement of Chairman Bera follows:] Good morning and welcome to today’s hearing on Coronaviruses: Understanding the Spread of Infectious Diseases and Mobilizing Innovative Solutions. I want to thank Ranking Member Lucas, the Members of this Committee, and our witnesses for joining us today to discuss the scientific tools and research investments we need to better detect, predict, and understand the spread of emerging diseases. While the Chairwoman is not able to join today, I’m proud to hold the gavel and appreciate her strong commitment to public health. As a doctor, the former Chief Medical Officer of Sacramento County, and a member of the CSIS Commission on Strengthening America’s Health Security, I have been a strong advocate of American leadership in global health. Congress’ job is to exercise oversight over the federal

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government’s response to COVID-19. That is precisely what I have been doing, both as the Vice Chair of the Science, Space, and Technology Committee and as the Chairman of the Foreign Affairs Subcommittee on Asia, the Pacific, and Nonproliferation. In addition to this hearing, I have chaired two other Congressional hearings on the coronavirus outbreak, sounded the alarm when the White House disbanded the office in charge of preparing for pandemics, and sought to include funds to combat coronavirus over a month ago through other legislation. Viruses have caused some of the most dramatic and deadly disease outbreaks in human history. Novel viruses of animal origin-like SARS and MERS-have been emerging at an alarming rate over the last two decades. People are traveling more internationally and living in more densely populated areas. We are expanding into new geographic areas through deforestation, mining, and agricultural land use. Humans are coming into closer contact with animal species that are the perfect hosts of infectious agents, making it easier for viruses to jump from animals to humans. Disease outbreaks caused by new viral infections are a growing public health concern for the global community, as viruses show no respect for national boundaries. The effect of COVID-19 on our communities will depend on how the virus spreads, the severity with which people get sick, and the measures we have available to control its impact. I’d like to drive home the point that these questions can all be answered by a rapid and robust research response. Yet recent outbreaks have highlighted the strengths and weaknesses of our research and development response, both domestically and internationally. We need additional research to expedite the development of diagnostic tests to quickly identify those that are sick and push those testing capabilities to every state. Not only will this protect our public health personnel on the front lines, but it will also give them the tools to combat the disease head on.

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Thanks to my role with the Foreign Affairs Committee, I am also aware of the importance of social science in guiding our response and actively combating the spread of misinformation around infectious disease outbreaks. Fear, anxiety, and stigma can drive sick people to hide their symptoms to avoid discrimination, prevent some individuals from seeking health care immediately, and discourage others from adopting healthy behaviors. Integrating social scientists into our outbreak response helps communities accept and adhere to public health measures aimed at limiting the spread of disease. Research and development actions are an integral part of the response to an outbreak. Scientists are using innovative technologies like artificial intelligence to detect and predict the spread of disease more effectively. Others are conducting research to optimize the use of currently available treatments and evaluate candidates for new drugs and vaccines. It is apparent now more than ever that our best scientists should be leading our response. For the last 14 months, this Committee has worked tirelessly to ensure that decision-making is driven by science. Now is the time to listen and trust science and use it to react calmly and smartly to COVID-19. It is critical that we are not swayed by misinformation and avoid the stigmatization of vulnerable groups. This issue has hit close to home. The first reported death from COVID19 in California occurred in Roseville, California, which borders my district. Sacramento County is now monitoring several potential cases of COVID-19 transmission. The hospital where I used to attend in and teach medical students is treating a patient with the disease. My heart is with those who are currently suffering. I continue to believe that the risk to the American people is low at this time. But this disease is global in scope and it is impacting our communities and our economy. Tackling it will require our communities, our government, and our international partners working together. With American leadership, we can do it. But it will require proper planning, coordination, and resourcing. It’s not too late.

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I look forward to hearing from our witnesses today on how we can best support our nation’s scientists as they deploy new health technologies and develop scientific information critical to controlling and mitigating the effects of emerging infectious diseases. With that, I will turn it over to the ranking member, Mr. Lucas. Chairman BERA. With that, the Chair now recognizes the Ranking Member, Mr. Lucas, for his opening statement. Mr. LUCAS. Good morning, and thank you, Dr. Bera, for holding this important hearing as we deal with an emerging and rapidly evolving situation with the spread of coronavirus, COVID–19. According to the Centers for Disease Control at this time, most people in the United States have little immediate risk of exposure to the virus, however, public health experts also advise us a pandemic is likely, so we must gather the facts and be prepared. Today I hope our expert witnesses can provide important information we can share with our constituents. I also hope we can learn what tools are needed to detect, predict, and prevent the next pandemic. COVID–19 was first identified in Wuhan, China in December of 2019. Since then the World Health Organization has reported over 90,000 confirmed cases, and over 3,000 deaths spread throughout 76 countries. In the United States, the CDC has reported at least 152 confirmed cases and 11 deaths. We know that for most individuals the illness is not serious, but we’re still getting information on the death rate. The impact on vulnerable populations is particularly concerning, though, and my thoughts are with the individuals and families that have been affected. This is not the first global pandemic in modern times, and I’m quite certain it won’t be the last. Just over 100 years ago the world faced one of the deadliest pandemics in history, the 1918 avian flu pandemic, also known as the Spanish flu. It killed an estimated 50 million people worldwide, including over 600,000 people in the United States. Since 1980, outbreaks of emerging infectious diseases have been occurring with greater frequency and have been causing higher numbers of human infections than in the past. The vast majority of these infections are initially caused by the spread of the disease from animals to humans. A SARS (Severe Acute Respiratory Syndrome) outbreak in 2003 and an

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avian flu outbreak in 2006 were wakeup calls for the American public health system, and Congress made considerable investments in improving our Nation’s capacity to detect and respond to pandemics. We would be in a much worse position today without those investments. I’m confident that the U.S. Government has the tools necessary to deal with this. We have the best scientists in the world with NIH (National Institutes of Health), CDC, and in our universities. Their work has yielded considerable advancements in health technology, disease surveillance, and predictive modeling, as well as medicine, drugs, and vaccine development. With the integration of technology like artificial intelligence (AI), and the greater availability of data, researchers are now able to identify and track outbreaks faster. Last Congress, we also modernized the Pandemic AllHazards Preparedness Act to set up a framework to deal with precisely this type of outbreak. While significant progress has been made, gaps remain, and a severe pandemic like the novel coronavirus could be devastating to the global population. As the human population has grown, so has the livestock, swine, and poultry populations needed to feed us. This expanded number of hosts provides increased opportunities for viruses from birds, cattle, and pigs to spread, evolve, and infect people. To better understand how zoonotic diseases like avian flu, swine flu, Ebola, Zika, SARS, and now coronavirus spread and operate, we must invest in basic research to learn more about the interconnection between people, animals, and plants in shared environments. Yesterday the House passed a supplemental appropriations bill to address the response to COVID–19 and the development of a vaccine. I supported the bipartisan bill, and I hope my colleagues and I can work together on a long-term strategy to prepare for any global pandemic we may face in the future. Our top priority is the health and welfare of the American people. I’m pleased the President has created the Coronavirus Task Force. This interagency group is working to monitor, contain, and mitigate the spread of the novel coronavirus, while ensuring the American people have access to accurate and up-to-date health and travel information.

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The best thing Americans can do right now is to follow the guidance of CDC. Many of their recommendations are simple ones you learned from your mother. Wash your hands, wash your hands, do it thoroughly and frequently, cover your mouth to cough or sneeze, avoid touching your face, stay home if you are sick. I want to thank the witnesses for taking the time to come here to share their expertise and insights with us during this crucial time to help keep Americans safe, healthy, and secure. And, with that, I yield back the balance of my time, Mr. Chairman. [The prepared statement of Mr. Lucas follows:] Good morning and thank you Chairwoman Johnson for holding this important hearing as we deal with an emerging and rapidly evolving situation with the spread of the coronavirus COVID-19. According to the Centers for Disease Control (CDC), at this time most people in the United States have little immediate risk of exposure to the virus. However, public health experts also advise us a pandemic is likely, so we must gather the facts and be prepared. Today I hope our expert witnesses can provide important information we can share with our constituents. I also hope we can learn what tools are needed to detect, predict, and prevent the next pandemic. Covid-19 was first identified in Wuhan, China in December 2019. Since then the World Health Organization has reported nearly 90,000 confirmed cases and over 3,000 deaths spread throughout 76 countries. In the United States, the CDC has reported 152 confirmed cases and 11 deaths. We know that for most individuals the illness is not serious, but we are still getting information on the death rate. The impact on vulnerable populations is particularly concerning though, and my thoughts are with the individuals and families that have been affected. This is not the first global pandemic in modern times, and I am certain it won’t be the last. Just over a hundred years ago the world faced one of the deadliest pandemics in history - the 1918 avian flu pandemic, also known as the “Spanish flu.” It killed an estimated 50 million people worldwide, including over 600,000 people in the United States.

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Since 1980, outbreaks of emerging infectious diseases have been occurring with greater frequency and have been causing higher numbers of human infections that inthe past. The vast majority of these infections are initially caused by the spread of disease from animals to humans. A SARS outbreak in 2003 and an Avian flu outbreak in 2006 were wake-up calls for the American public health system, and Congress made considerable investments to improve our nation’s capabilities to detect and respond to pandemics. We would be in a much worse position today without those investments. I am confident the U.S. government has the tools necessary to deal with this. We have the best scientists in the world at NIH, CDC, and in our universities. Their work has yielded considerable advancements in health technology, disease surveillance and predictive modeling, as well as medicine, drugs, and vaccine development. With the integration of technology like artificial intelligence and the greater availability of data, researchers are now able to identify and track outbreaks faster. Last Congress, we also modernized the Pandemic AllHazards Preparedness Act to set up a framework to deal precisely with this type of an outbreak. But while significant progress has been made, gaps remain, and a severe pandemic like the novel coronavirus could be devastating to the global population. As the human population has grown, so has the livestock, swine and poultry populations needed to feed us. This expanded number of hosts provides increased opportunities for viruses from birds, cattle and pigs to spread, evolve, and infect people. To better understand how zoonotic diseases like avian and swine flu, Ebola, Zika, SARS, and now COVID-19 spread and operate, we must invest in basic research to learn more about the interconnection between people, animals, and plants in shared environments. Yesterday the House passed a supplemental appropriations bill to fund the response to COVID-19 and the development of a vaccine. I supported the bipartisan bill. But I hope my colleagues and I can work together on a long-term strategy to prepare for any global pandemic we may face in the future.

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Our top priority is the health and welfare of the American people. I am pleased the President has created the Coronavirus Task Force. This interagency group is working to monitor, contain, and mitigate the spread of the novel coronavirus while ensuring the American people have access to accurate and up-to-date health and travel information. The best thing Americans can do right now is follow the guidance of the CDC. Many of their recommendations are simple ones you learned from your mother, wash your hands thoroughly and frequently, cover your cough or sneeze, avoid touching your face, and stay home if you are sick. I want to thank the witnesses for taking the time to be here to share your expertise and insights with us during this crucial time to help keep Americans safe, healthy, and secure. I yield back the balance of my time. Chairman BERA. Thank you, Mr. Lucas. If there are members who wish to submit additional opening statements, your statements will be added to the record at this point. At this time I’d like to introduce our witnesses. First we have Dr. Suzan Murray. Dr. Murray is the Program Director of the—for the Global Health Program at the Smithsonian’s National Zoo and Conservation Biology Institute. Next is Dr. John Brownstein. Dr. Brownstein is the Chief Innovation Officer at Boston Children’s Hospital, and a Professor at Harvard Medical School. Third I welcome Dr. Peter Hotez, who will be introduced by the Chair for the Subcommittee on Energy, Lizzie Fletcher of Texas. Mrs. FLETCHER. Thank you very much, Mr. Chairman. It’s truly a privilege and a pleasure to introduce an internationally recognized physician/scientist in global health, neglected tropical diseases, and vaccine development who is also my neighbor, and a true leader in our community in Houston, Dr. Peter Hotez. Dr. Hotez is Professor and Dean at Baylor College of Medicine, and Co-Director of Texas Children’s Hospital Center for Vaccine Development. As head of Texas Children’s Center for Vaccine Development, he leads a team of product development partnership for developing new vaccines for a variety of diseases, including other human coronaviruses, like SARS and MERS (Middle East Respiratory

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Syndrome), diseases affecting hundreds of millions of children and adults worldwide, while championing access to vaccines globally and in the United States. Dr. Hotez, welcome. We are glad to have you here today. Chairman BERA. And lastly we have Dr. Tara Kirk Sell. Dr. Sell is a Senior Scholar at Johns Hopkins Center of Health Security, and is an Assistant Professor at Johns Hopkins Bloomberg School of Public Health. You will each have 5 minutes for your spoken testimony. Your written testimony will be included in the record for the hearing. When you have completed your spoken testimony, we’ll begin with questions. Each member will have 5 minutes for questioning. Dr. Murray, you may proceed.

TESTIMONY OF SUZAN MURRAY, PROGRAM DIRECTOR, SMITHSONIAN GLOBAL HEALTH PROGRAM, SMITHSONIAN’S NATIONAL ZOO AND CONSERVATION BIOLOGY INSTITUTE Dr. MURRAY. Thank you very much. Congressman Bera, Ranking Member Lucas, and all Members of the esteemed Committee, thank you for calling this hearing, and inviting me to participate. My name is Dr. Suzan Murray, and I’m the Director of Smithsonian’s Global Health Program, based out of the National Zoological Park and Conservation Biology Institute. Our program utilizes experts in wildlife medicine, human medicine, public health, conservation, biology, and epidemiology to study and respond to health issues at the human/animal interface. We utilize a multidisciplinary approach to investigate emerging infectious diseases that threaten both human and animal life, and we build in-country capacity to train the next generations of health specialists. In short, this is the reason right now that our program was created.

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Human health, wildlife, and environmental health are inextricably linked, and closely depend upon each other. In order to safeguard the survival of all species, it’s critical that we examine health across a continuum of species, and have research and decisions firmly rooted in scientific knowledge. Understanding the current viral threats, the patterns and drivers of disease emergence, and the human behaviors that contribute to such emergence, will best allow us to not only respond to this outbreak, but the next one, and the one after that, because we do know they’re coming. Already we have identified many of the drivers of disease emergence and spread, including land use change, increased human/ wildlife interaction, and the globalization of travel and markets. Time and history have repeatedly shown us that it is much more humane, efficient, and economical to prevent disease rather than to identify, respond, diagnose, treat, and attempt to contain an outbreak. Through increased understanding of the as-yet undiagnosed viruses, the drivers of emergence, and the risk factors associated with various behaviors, we can develop the early warning systems, prepare for—prepare rapid response teams, and provide critical data and information to the vaccine industry to better prepare for the next outbreak. Just as critical, we must educate local medical professionals, and the people living in the communities at the greatest risk of outbreaks. By preventing the spread of pathogens at the source, we can avoid the global consequences that we are experiencing now. For example, over the last 10 years, and working with partner agencies, our team has collectively identified over 1,200 novel mammalian viruses. So that’s, you know, 1,200 is a lot of viruses. It’s only a small amount of the ones that are out there. One hundred sixty-one of these belong to the same family as COVID–19. In this time we also strengthened the capability for virus detection and characterization in 60 labs, and—in which pandemics are most likely to originate. We’ve also trained over 6,000 people in more than 30 countries at the frontline of defense against emerging diseases. At this moment, the world is focused on the novel coronavirus, COVID–19, as it should be. While it’s essential that we do everything we can to respond to this global crisis, it’s also the time we

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need to be thinking of emerging viruses. The next global pandemic is not a matter of if, but when and where. To quickly identify and contain such infections, health and disease must be evaluated across species, and on a global scale. While he might not have imagined it in this context, Ben Franklin was right when he said an ounce of prevention is worth a pound of cure. When it comes to outbreaks, the costs of responding to a crisis can dwarf the up front investment in research and education. Beyond a clear moral obligation to protect human life, there are staggering financial benefits from focusing on preventative measures. For example, the human and economic toll from the West African Ebola outbreak was massive. More than 11,000 people lost their lives, and well over $4 billion was spent globally. In case of the SARS epidemic of 2004, the estimated global financial impact was between $30 and $50 U.S. billion dollars, and the current COVID impact, while still evolving, and a dynamic situation, is expected to be on orders of magnitude higher. Advancements in the detection of novel pathogens show that the most efficient way to respond to and contain an outbreak is through the coordinated wildlife and human surveillance. While we estimate there are 1.7 as yet unknown viruses, about half of which can affect human people, and some lead to new pandemics. As of now, there are no coordinated programs to work in high risk regions to identify these unknown viruses, get their genetic sequences into labs, and identify ways to reduce risk of them emerging. Our best defense against spreading diseases that make their way into the human population is through research and education. While we cannot stop every disease outbreak, we can reduce their frequency, and build the capacity for a rapid global response when they do occur. Thank you once again for this hearing, and your interest in this pressing and important topic. I look forward to answering any questions you might have. [The prepared statement of Dr. Murray follows:]

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SMITHSONIAN INSTITUTION, WRITTEN TESTIMONY OF DR. SUZAN MURRAY, DVM, DACZM, DIRECTOR, SMITHSONIAN GLOBAL HEALTH PROGRAM, SMITHSONIAN NATIONAL ZOOLOGICAL PARK AND CONSERVATION BIOLOGY INSTITUTE, BEYOND CORONAVIRUS: UNDERSTANDING THE SPREAD OF INFECTIOUS DISEASES AND MOBILIZING INNOVATIVE SOLUTIONS, HOUSE COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY, U.S. HOUSE OF REPRESENTATIVES, MARCH 5, 2020 Chairwoman Johnson, Ranking Member Lucas, and Members of the Committee, thank you for calling this hearing, and inviting me to participate. My name is Suzan Murray, and I am the Director of Smithsonian’s Global Health Program, based out of the National Zoological Park and Conservation Biology Institute. This program utilizes experts in wildlife medicine, human medicine, public health, conservation biology, epidemiology, virology and molecular biology to study and respond to issues emerging at the human/animal interface. We utilize a multidisciplinary approach to investigate emerging infection diseases that threaten both human and animal life, and we build in country capacity and train the next generation of health specialists. Human health, wildlife health, and environmental health are often described as inextricably linked and closely dependent upon one another. In order to safeguard the survival of all species, it is critical that we examine health across a continuum of species and have research and decisions firmly rooted in scientific knowledge.

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Understanding the current viral threats, the patterns and drivers of disease emergence, and the human behaviors that contribute to such emergence will best allow us to not only respond to this outbreak, but the next one and the one following that because we know this is coming. Already we have identified many of the drivers of disease emergence and spread, including land use change, increased human/wildlife interaction, and globalization of travel and markets. Time and history have repeatedly shown us it is much more humane, efficient, and economical to prevent disease, rather than identify, respond to, diagnose, treat, and attempt to contain an outbreak. Through increased understanding of the as-of-yet undiscovered viruses, the drivers of emergence, and the risk factors associated with various behaviors we can develop early warning systems, prepare rapid response teams, and provide critical data and information to the vaccine industry to better prepare for the next outbreak. Just as critical, we must educate local medical professionals and the people living in communities with the greatest risk of outbreaks. By preventing the spread of pathogens at the source, we can avoid the global consequences we are seeing now. For example, over the last 10 years, working with partner agencies, we have identified more than 1,200 novel wildlife-borne viruses, 161 of which belong to the same family as the COVID-19 virus. In that time, we strengthened the capability for virus detection and characterization in 60 labs in regions where pandemics are most likely to originate, and trained over 6,600 people in more than 30 countries to be our front line of defense against emerging diseases. At this moment, the world is focused on the novel coronavirus, COVID-19. While it’s essential that we do everything we can to respond to the emerging global crisis, now is also a time when we should be thinking about future emerging viruses. Research published in the Proceedings of the National Academy of Sciences has found that spillover of new viral infections from animals to humans is occurring with increased frequency, a direct result of the increased interaction people now have with wildlife and their products.

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The next global pandemic is not a matter of if, but when and where. To quickly identify and contain such infections, health and disease must be evaluated across species on a global scale. While he might not have imagined it in this context, Ben Franklin was right when he said “An ounce of prevention is worth a pound of cure.” When it comes to outbreaks, the costs of responding to a crisis can dwarf the up-front investment in research and education. Beyond the clear moral obligation to protect human life, there are staggering financial benefits from focusing on preventative measures. For example, the human and economic toll from the West African Ebola epidemic was massive—more than 11,000 people lost their lives and over $3.6 billion dollars was spent globally, with nearly $2.4 billion spent by the U.S., in response. A global economy compounds the economic impacts of disease through travel, trade, and financial networks. In the case of the SARS epidemic of 2004, the estimated global financial impact was between $30 and $50 billion, Ebola was $10 billion, and the current COVID-19 impact, while still an evolving and dynamic situation, is expected to be even higher. It is obvious through market reaction by now that this outbreak has already created an international financial impact. Advancements in the detection of novel pathogens show the most efficient way to identify, respond to, and contain an outbreak is through coordinated wildlife and human surveillance. We estimate there are 1.7 million unknown viruses, around half of which could infect people, and some lead to new pandemics. As of now, there are no coordinated programs to work in high risk regions to identify these unknown viruses, get their genetic sequences into our labs, and identify ways to reduce risk of them emerging. Our best defense against spreading diseases that make their way into human populations is through research and education. While we cannot stop every disease outbreak, we can reduce their frequency and build the capacity for a rapid global response when they occur. Thank you once again for having this hearing and for your interest in this pressing and important topic. I look forward to answering any questions you may have.

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Suzan Murray, DVM, DACZM. Director, Smithsonian Global Health Program BS, Amherst College; DVM, Tufts University; Board-Certified Diplomate, American College of Zoological Medicine (DACZM)

Figure 2. Suzan Murray

Dr. Suzan Murray is a board-certified zoo veterinarian at the Smithsonian Conservation Biology Institute and serves as both the program director of the Global Health Program and as the SCBI’s chief wildlife veterinary medical officer. She leads an interdisciplinary team engaged in worldwide efforts to address health issues in endangered wildlife and combat emerging infectious diseases of global significance, including zoonotic diseases. Dr. Murray also acts as the Smithsonian liaison to the Foreign Animal Disease Threat and Pandemic Preparedness subcommittees of the White House’s Office of Science and Technology. Dr. Murray’s work focuses on providing clinical care to free-ranging wildlife, pathogen detection, advanced diagnostics, training of international veterinarians and other health professionals, capacity building, and collaboration in infectious disease research at the human-wildlife-domestic animal interface. She previously served as chief veterinarian for the Smithsonian’s National Zoo and has a wealth of clinical knowledge and experience with wildlife and zoo animals both free-ranging and in human care.

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Dr. Murray earned a bachelor’s degree from Amherst College in 1984 and completed her veterinary degree in 1991 from Tufts University. After a surgical internship, she completed a residency in zoological medicine at the Smithsonian’s National Zoo in 1995 and became a Diplomate of the American College of Zoological Medicine (DACZM) in 2000. Dr. Murray has been either the principle investigator or co-principle investigator on several research grants including Morris Animal Foundation, Smithsonian Endowment, Smithsonian Women’s Committee, and James Bond Funds.

TESTIMONY OF JOHN BROWNSTEIN, CHIEF INNOVATION OFFICER, BOSTON CHILDREN’S HOSPITAL AND PROFESSOR, HARVARD MEDICAL SCHOOL Dr. BROWNSTEIN. Congressman Bera, Ranking Member Lucas, and distinguished Members of the U.S.—— Chairman BERA. Dr. Brownstein, could you turn your mic on? Dr. BROWNSTEIN. That would help. Congressman Bera, Ranking Member Lucas, and distinguished Members of the U.S. House of Representatives Committee on Science, Space, and Technology, thank you for inviting me today to speak with you. Today I’ll describe ways that novel technologies like artificial intelligence can help detect, monitor, and predict emerging infectious diseases. I’ll also discuss how non-traditional sources can supplement existing epidemiological techniques. But as I describe the good news about such advances, I don’t want to sugarcoat the bad, for the current Federal investments in disease surveillance are inadequate and transient. We urgently need Federal and local investment in new technologies for public health surveillance and response. Such investment will augment the capacity of public health to implement new ways to monitor the health of populations. It will deepen our understanding of community-based morbidity and mortality. It will also save lives.

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This is the goal of my team at Boston Children’s Hospital, where we develop innovative surveillance technology, where we use freely online information to provide insights for both public health agencies and the general public. We did this for the H1N1 influenza pandemic, H7N9, avian influenza, Ebola in West Africa, and now COVID–19. These platforms, and our research, have ultimately played a critical role in that innovative surveillance technologies can help detect, monitor, and ultimately mitigate the impact of these diseases. Our inaugural project, HealthMap, which is available to the public, brings together disparate sources from a variety of data streams to help provide a unified view of the world of infectious diseases. To do that we use AI, machine learning, natural language processing, all to organize that information and make it available. Here’s an example. On December 30, 2019 the platform alerted us to an unknown viral pneumonia. That turned out to be one of the earliest signals of the current COVID–19 outbreak. Using AI in modeling of epidemics is one of the areas of research offering vast insights into the potential burden of disease, and where it spreads. Machine learning models can predict where a given virus may arrive next. That lets us inform public health organizations about how to respond. Predictive modeling can also be used with data like prior disease history, weather, travel patterns, laboratory data, symptom surveillance. All, together through AI, help us exchange information, conduct surveillance, and measure public response to the events and response. It is also critical to support sentinel surveillance of disease. Sentinel surveillance allows public health officials to identify signals early, impacts, and disease burden in the community. One such example is Flu Near You, which is a crowdsourcing platform for symptom surveillance in the U.S. It offers two advantages. One, it identifies individuals who may be ill, but not seeking medical attention, and it’s in real time. Our team has now augmented this tool to improve with COVID–19 surveillance.

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To date, there is no evidence supporting widespread transmission of COVID–19 in the U.S., but does suggest that sustained transmission in the community level will be occurring. Current global situation suggests that this outbreak will become a pandemic. It threatens the people—the health of the people of the United States and globally. The COVID–19 outbreak also demonstrates some reasons for optimism. It demonstrates what we can accomplish when the scientific and humanitarian disciplines unite around a common goal. We understand that each outbreak might require a slightly different approach to monitoring response, but there are key updates and metrics that we need in every single outbreak. There are questions that we must ask, how many new cases are there? What is the geographic spread? Are healthcare workers infected? We can help answer these questions by using both digital disease platforms, along with traditional surveillance. We aggregate data from a variety of these sources in real time. There’s an epidemiological expression that expresses what we want, prioritizing sensitivity over specificity. In English this means that—risking some false positives to uncover more of those who are sick. These platforms do that. They aggregate everything available to provide stakeholders with a snapshot of the current view of the situation. Those within the realm of infectious diseases often say it is not a matter of if, it’s a matter of when. We continually need support for initiatives to make an impact both domestically and globally through infectious disease monitoring and surveillance. By investing in our neighbors, and promoting health initiatives outside of our borders, we help reduce the threat of an outbreak reaching the United States. There’s another essential step to being prepared, long term support of the Centers for Disease Control and Prevention, and for local Departments of Public Health. The CDC’s Influenza Surveillance Systems are the backbone of flu surveillance for this country. Augmenting this surveillance system with novel programs like HealthMap provides us with additional information. It allows the public health authorities, clinicians, researchers, and the general public to stay alert of what’s happening.

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And this is why I urge this Committee to make sure the United States provides sustained investment in the fundamental needs of disease detection and surveillance. That means investments domestically and around the world. Non-traditional data sources and enhanced data processing through AI and machine learning have proven their worth. They support traditional surveillance, they aid in the developing of a clear path, and a picture of an existing or potential infectious disease threat to human health. You have shown through your thoughtful leadership on these issues in the past, and now we need your help again, for with your continued support, we cannot only strengthen the public health community, we will protect the lives that we serve. Thank you again, and I look forward to your questions. [The prepared statement of Dr. Brownstein follows:]

WRITTEN TESTIMONY BEFORE THE U.S. HOUSE OF REPRESENTATIVES COMMITTEE ON SCIENCE, SPACE AND TECHNOLOGY, HEARING ENTITLED: “BEYOND CORONAVIRUSES: UNDERSTANDING THE SPREAD OF INFECTIOUS DISEASES AND MOBILIZING INNOVATIVE SOLUTIONS” Dr. John Brownstein, PhD Chief Innovation Officer, Boston Children’s Hospital Professor, Harvard Medical School Thursday, March 5, 2020

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Chairwoman Johnson, Ranking Member Lucas and distinguished members of the U.S. House of Representatives Committee on Science, Space and Technology, thank you for inviting me to speak with you today. I will be addressing the topic of “Beyond Coronaviruses: Understanding the Spread of Infectious Diseases and Mobilizing Innovative Solutions” and sharing with you how novel technologies, like Artificial Intelligence (AI), can help predict, detect and monitor emerging infectious diseases while also discussing how nontraditional data sources can supplement existing epidemiological techniques. It is my goal to share how these technologies have changed the ways we manage public health crises or potential crises and what the future can hold, with proper investments and planning. I am a Professor at Harvard Medical School and the Chief Innovation Officer at Boston Children’s Hospital. I also direct the Computational Epidemiology Lab at the Boston Children’s Hospital’s Computational Health Informatics Program. My research aims to have translational impact on the surveillance, control and prevention of disease. My team develops innovative infectious disease surveillance platforms that use freely available online data to provide real-time insights for both public health officials and the general public. Throughout the 2009 H1N1 influenza pandemic, H7N9 Avian Influenza, Ebola in West Africa, Zika in the Americas and now COVID-19, these platforms, and our research, have highlighted the critical role that innovative surveillance can have on outbreak detection, monitoring, and mitigation. Our inaugural project, developed in 2006, is called HealthMap. It is a publicly available platform, which brings together disparate data sources, including online news aggregators, eyewitness reports, expert-curated discussions and validated official reports, to achieve a unified and comprehensive view of the current global state of infectious diseases. The system automates data acquisition, filtering and characterization of information, using machine learning algorithms and natural language processing. The system ingests and classifies its data independently, without human intervention. However, we choose to keep a human in the loop-having infectious disease analysts review the content to correct and

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refine the automated classifications. The analysts ensure that subtle signals of an outbreak are captured. For example, on March 14th 2014 the HealthMap system brought in a French alert reporting cases of "mystery hemorrhagic fever" that had killed eight in Guinea. This was the earliest signal in West Africa of what would become the largest Ebola outbreak in history. On December 30, 2019, we were alerted to an unknown viral pneumonia, which was one of the earliest signals in the current COVID-19 outbreak. The HealthMap platform has been integrated into the Epidemic Intelligence from Open Sources (EIOS) platform developed and maintained by the World Health Organization. This system supports efforts for event-based surveillance globally, and has proven to be a valuable resource during emerging disease outbreaks. The HealthMap platform is just one of many that highlight the utility and need for timely, sentinel outbreak signals. Artificial Intelligence (AI) can be used for public health preparedness measures to control the spread of disease, particularly during an emerging disease outbreak. Earlier disease detection provides health leaders with the tools to adequately prevent or prepare against the threat of an emerging disease. The use of AI in modelling epidemics is one area of research that can provide vast insight into the potential burden of disease, and where it spreads. For example, machine learning models can predict where a given virus may arrive next, and inform public health organizations how to prepare in response. Predictive modelling techniques can utilize information like prior disease history, weather and travel patterns, laboratory testing, symptom surveillance and more. These forecasting tools have the power to provide insights on health outcomes and disease progression. AI use in healthcare systems can provide novel insights on an emerging outbreak, as well. AI has been used to identify patterns in images, scans or records that emulate the disease of interest, providing earlier signs of infection. Increased use of AI for disease surveillance measures can hopefully provide a more rapid response between countries to control epidemics. AI technologies provide value in information exchange, surveillance measure, public response to emerging and seasonal

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outbreaks, and education on disease threats, all of which are critical needs to prevent or contain an outbreak. It is also critical to support sentinel surveillance measures of disease. Sentinel surveillance allows public health officials an ability to identify signal trends, and impacts to disease burden in a community. One such example of this is Flu Near You48, a crowdsourced symptom surveillance tool in the U.S. where users submit health reports weekly. The project, created in partnership with my research lab at Boston Children’s Hospital, Ending Pandemics and the American Public Health Association, allows researchers, and local, state and federal public health agencies to access the submitted symptom data to understand disease patterns at the community level. This system captures individuals who may not be seeking medical attention, and is updated in real time. In response to the ongoing novel coronavirus outbreak, our team has added additional questions that may pick up early signals of this virus here in the US, including questions on diagnosis and travel history. As we collect reports in the Flu Near You system, we are able to detect spikes in symptoms over a set amount of time. We can also retroactively look back at symptom reports to learn about potential community spread after a case is confirmed. Systems like Flu Near You are extremely valuable tools to fill in gaps of information, and provide early signals of disease impacts at a community level. With each outbreak comes its own difficulties. We experienced the lack of online local news media during the West African Ebola outbreak, we have built platforms to allow field epidemiologists on the ground confirm or deny rumored outbreaks. We understand that each outbreak might require a slightly different approach to monitoring or response. However, there are key updates and metrics, which every outbreak could benefit from: how many new cases, is there geographical spread, are healthcare workers infected, and/or are there new testing or treatment methods available. The use of digital disease detection platforms is complementary to traditional surveillance - aggregating data from a variety of sources in real-

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time. Prioritizing sensitivity over specificity, these platforms provide stakeholders with a snapshot view of the current situation - aggregating everything that’s available. On December 31, 2019, the World Health Organization (WHO) China Country office was informed of cases of pneumonia of unknown etiology located in Wuhan City, Hubei Province of China49. Within 4 days, 44 cases of disease were identified, though no infectious agent or cause was known. At the time, the only link between cases was a seafood market that all cases reportedly visited. On January 7, 2020, Chinese scientists released sequencing that determined that the cause of illness was a novel coronavirus, later named COVID-1950. Since the first alert of COVID-19, the number of confirmed cases in China and globally expanded quickly, leading to WHO holding three emergency meetings of the International Health Regulations Emergency Committee (IHR) on January 22, 23 and 30, 20203. IHR, led by WHO Director General Dr. Tedros Adhanom Ghebreyesus, determined that the COVID-19 outbreak is a Public Health Emergency of International Concern (PHEIC) on January 30, 2020 due to the threat of disease globally3. As of 10am CET on March 2, 2020, 78,811 confirmed cases of COVID-19 have been reported to WHO globally, including 2,462 deaths related to the disease51. The number of cases reported daily in China have continued to decrease, showing promising results in an effort to contain the disease within the country52. Based on evidence provided by the WHOChina joint mission, WHO reports that the epidemic peaked and plateaued between January 23 and February 2, 2020 in China and has been steadily declining since that time5. WHO currently has the global risk level for COVID-19 as very high. Outside of China, 8,774 cases have been confirmed in 64 countries4. In the 49

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https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200121-sitrep-12019-ncov.pdf?sfvrsn=20a99c10_4. http://www.diseasedaily.org/diseasedaily/article/world-health-organization-covid-19-outbreakpublic-health-emergency. https://www.who.int/docs/default-source/coronaviruse/20200302-sitrep-42-covid-19.pdf?sfv rsn=d863e045_2. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-mediabriefing-on-covid-19--24-february-2020.

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United States, 62 cases of COVID-19 have been confirmed, including 2 deaths4. To date, there is evidence supporting local transmission of COVID-19 in the United States, but does not suggest sustained transmission at the community-level at this time. Additionally, the epidemiologic curve for the outbreak is showing an increase in case reports outside of China over time (Figure 3)4. This suggests that COVID-19 has expanded beyond imported cases associated with travel to Hubei, China and has sustained transmission in new regions. Sudden increases in cases since February 21, 2020 in Italy, the Republic of Korea and the Islamic Republic of Iran are deeply concerning for human health and signal that a continued global response is necessary4. Great strides have been taken to understand COVID-19 since its discovery. The public health community has united to work quickly to understand the virus and its impact. WHO has been able to determine that the fatality rate is between 2% and 4% in Wuhan, China and 0.7% outside of Wuhan5. In most cases of mild disease, recovery time is approximately two weeks and in cases of severe disease, recovery takes three to six weeks5. While COVID-19 is not currently circulating in the United States, the risk of sustained transmission is still high. The current global situation suggests that this COVID-19 outbreak has the potential to cause a pandemic, threatening the health of people in the United States and globally6. The continued COVID-19 response has demonstrated what can be accomplished as scientific and humanitarian disciplines unite for a common goal. In order to stay persistent in combating COVID-19 and future outbreaks, we need more continuous support for public health initiatives and investment in programs aiding in the detection and monitoring of infectious diseases. Within the realm of infectious diseases, we often say, “it is not a matter ‘if’ the next pandemic occurs, but a matter of ‘when’.” Global emerging and re-emerging infectious diseases are a constant threat to human health. Infectious disease monitoring and surveillance are critical for preventing the spread of disease. We need continual support for initiatives that strive to make an impact both domestically and globally. We live in an

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interconnected world and it is our duty to protect human health. By investing in our neighbors and promoting health initiatives outside our borders, we are also helping reduce the threat of an outbreak within the United States. It is critical that, as a nation, we take a stance in promoting global health and health security. We have already seen the impacts that COVID19 has had, in two months time, on travel, trade, and economies. It highlights the need to support and empower local health departments in preparedness. Long term support for the Centers for Disease Control and Prevention and for local health departments are critical to preparedness efforts. This support should not only include funding, but also oversight and direction to ensure that systems employed utilize the most effective tracking tools, such as AI and other data driven methods and that the findings of publicly funded surveillance translate into action and tools to combat these global challenges. We need to be diligent in our continual response to infectious diseases. The CDC’s Influenza surveillance systems are the backbone of flu surveillance in this country. Augmenting the current system with novel programs like HealthMap provides additional information that can help public health authorities, clinicians, researchers and decision makers learn more and react faster to seasonal and novel outbreaks. With every outbreak, whether it is Ebola, Zika or even influenza, we have a dangerous cycle that exists. The outbreak is announced and captures the attention of politicians and media, where all the alarms are raised. The entire world becomes united with concern and amazing strides are taken, but only for a relatively short period of time. Eventually, we become complacent and as the headlines fade, so does the investment in infectious disease response, both in time and financially. But this cycle needs to change and that can start with you today. If we, the United States, are proactive instead of reactive in the investment of public health and surveillance initiatives, we can hopefully prevent the next COVID-19, or at the very least, reduce its global scale. It is time that we make a continued effort to ensure all public health systems are prepared and equipped to handle any infectious disease threat it may encounter.

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Federal government investments have already shown to be successful in protecting human health. Among them, USAID has proven this success through the PREDICT project, which has prepared us for the next pandemic through its wildlife sampling. Through this work, we are able to detect potential zoonotic diseases before humans are infected. But with this achievement, we are clear that we need more support from our federal government to keep innovating and creating novel technologies for the surveillance and detection of infectious diseases globally. Amidst this crisis, we are starkly aware of the need for continual investment so that in the times of peace, we can be preparing for the next event. In short, it is my recommendation that the United States continues to invest in the fundamental needs of disease detection and surveillance domestically and internationally. Nontraditional data sources and crowdsourcing tools have proven to give support to traditional surveillance activities and can aid in developing a clearer picture of any existing or potential infectious diseases that threaten human health. By leveraging these tools and resources, we can identify transmission patterns of disease within a community near-real time in order to directly allocate where support is needed to prevent diseases from spreading further. Additionally, the data collected by these tools can allow us to learn about different models of transmission in order to predict the spread of disease in the future. The COVID-19 outbreak reminds us that while we have made incredible advances in preparedness and response activities, there is still a huge amount of work to be done. Investing in novel technologies that support disease detection and existing epidemiological techniques will provide a new era for handling infectious disease outbreaks. It is only with your continued support that the momentum we have gained as a public health community is maintained.

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Thank you for your thoughtful leadership on these issues. I look forward to your questions and wish to continue to be a resource in your important work. **Please note, the COVID-19 outbreak is rapidly changing both in the United States and globally. All relevant COVID-19 case information provided in this testimony reflects the outbreak situation as of Monday, March 2, 2020.

Figure 2. HealthMap COVID-19 map shows confirmed cases of coronavirus globally53.

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Figure 4. HealthMap shows media reports of COVID-19 globally54.

Figure 5. WHO situation report from March 2, 2020 shows epidemic curve of coronavirus cases detected outside of China as increasing over time4. 54

https://www.healthmap.org/wuhan/.

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John S. Brownstein, PhD Chief Innovation Officer, Boston Children’ Hospital Professor, Harvard Medical School

Figure 6. John Brownstein

John Brownstein, PhD is Professor of Biomedical Informatics at Harvard Medical School and is the Chief Innovation Officer of Boston Children’s Hospital. He also directs the Computational Epidemiology Lab and the Innovation and Digital Health Accelerator both at Boston Children’s. He was trained as an epidemiologist at Yale University. Overall, his work aims to have translation impact on the surveillance, control and prevention of disease. He has been at the forefront of the development and application of data mining and citizen science to public health. His efforts are in use by millions each year including the CDC, WHO, DHS, DOD, HHS, and EU, and has been recognized by the National Library of Congress and the Smithsonian. In addition to research achievements, this translational impact comes from playing an advisory role to numerous agencies on real-time public health surveillance including HHS, DHS, CDC, IOM, WHO and the White House. He was awarded the Presidential Early Career Award for Scientists and Engineers, the highest honor bestowed by the United States government to outstanding scientists and the Lagrange Prize for international achievements in complexity sciences. Dr. Brownstein is also Uber’s healthcare advisor and co-founder

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of digital health companies Epidemico and Circulation. He has authored over 200 peer-reviewed articles on epidemiology and public health. This work has been reported on widely including pieces in the New England Journal of Medicine, Science, Nature, New York Times, The Wall Street Journal, CNN, National Public Radio and the BBC.

SHORT BIO John Brownstein, PhD is Professor of Biomedical Informatics at Harvard Medical School and is the Chief Innovation Officer of Boston Children’s Hospital. He directs the Computational Epidemiology Lab and the Innovation and Digital Health Accelerator both at Boston Children’s. He was trained as an epidemiologist at Yale University. Dr. Brownstein is also Uber’s healthcare advisor and co-founder of digital health companies Epidemico and Circulation. Chairman BERA. Dr. Hotez?

TESTIMONY OF PETER HOTEZ, PROFESSOR AND DEAN, NATIONAL SCHOOL OF TROPICAL MEDICINE, BAYLOR COLLEGE OF MEDICINE, AND CO-DIRECTOR, TEXAS CHILDREN’S HOSPITAL CENTER FOR VACCINE DEVELOPMENT Dr. HOTEZ. Thank you very much. Dr. Bera, Congress—Chairman Bera, Ranking Member Lucas, Congresswoman Lizzie Fletcher, thank you for that very generous introduction. I’d also like to acknowledge my fellow Texan, Congressman Pete Olson. It’s an honor to be here. I always get thrilled when I have the opportunity—I’ve been doing this for 20 years—to address Committees in Congress, and it’s still a special thrill for me.

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I’m a vaccine scientist, and a pediatric scientist. I was previously Chair of Microbiology at George Washington University, just down the road, and then a decade ago we moved to Texas to create a new—a unique school for emerging and neglected tropical diseases, and also to create a unique center for vaccine development, and the need was this. There is an urgency to create vaccines for diseases which don’t make money. So we took this on in—with the idea of pioneering not only the interesting science, but also a new business model, and the business model part we haven’t quite figured out yet, because we’re trying to make diseases—vaccines for diseases no one else will make. So we have a schistosomiasis vaccine now in clinical trials, a leishmaniasis vaccine that we hope will advance to the clinic soon, a hookworm vaccine in clinical trials, a new Chagas disease vaccine that’s moving into the clinic. I like to say these are the most important diseases you’ve never heard of. These are some of the most common afflictions of the world’s population, but they mostly occur among people who live in extreme poverty, and so there’s no model to figure out who’s going to pay for them, so, as a consequence, neither the biotechs, nor the big pharmaceutical companies, make those vaccines. And, for reasons that we’ll explore this morning, we also took on, a decade ago, the interesting problem of making coronavirus vaccines, because we recognize these as enormous public health threats, and yet we have not seen the Big Pharma guys and the biotechs rushing into this space. So we partnered with a group at the New York Blood Center and the Galveston National Laboratory to take on the big scientific challenge of coronavirus vaccines. And I say a scientific challenge because one of the things that we’re not hearing a lot about is the unique potential safety problem of coronavirus vaccines. This was first found in the early 1960s, with the respiratory syncytial virus (RSV) vaccines that—and it was done here in Washington with the NIH and Children’s National Medical Center, that some of those kids who got the vaccine, actually did worse, and I believe there were two deaths as—in the consequence of that study.

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Because what happens with certain types of respiratory virus vaccines, you get immunized, and then, when you get actually exposed to the virus, you get this kind of paradoxical immune enhancement phenomenon. And what—how—and we don’t entirely understand the basis of it, but we recognize that it’s a real problem for certain respiratory virus vaccines. That killed the RSV program for decades. Now the Gates Foundation is taking it up again, but when we started developing coronavirus vaccines, and our colleagues, we noticed in laboratory animals that they started to show some of the same immune pathology that resembled what had happened 50 years earlier, so we said, oh, my God, this is going to be problematic. But we collaborated with a unique group that figured out how to solve the problem, that if you narrow it down to the smallest subunit, the piece that—of—what’s called the receptor binding domain, that docks with the receptor, you get protection, and you don’t get that immune enhancement phenomena. So we were really excited about that, and we proposed this to the National Institute of Allergy and Infectious Diseases (NIAID). They funded it, and we wound up actually making and manufacturing, in collaboration with Walter Reed Army Institute of Research, a first generation SARS vaccine. So SARS was the one that emerged in 2003, and then this new one, of course, we call the SARS–2 coronavirus. We had it manufactured, but then we could never get the investment to take it beyond that. And then—so that was really unfortunate, because we had the vaccine ready to go, but we couldn’t move it into the clinic because of lack of funding, because by then nobody was interested in coronavirus vaccines. When the Chinese started putting up the data on bioarchive in January/February, we saw very close homology between the two, and realized that we may be sitting on a very attractive coronavirus vaccine.

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Now we’re working with—again with NIH, and we’ll work with BARDA (Biomedical Advanced Research and Development Authority) and others, to get the funding, but now we’ll have that lag. And these clinical trials are not going to go quickly because of that immune enhancement. It’s going to take time. And so, you know, all—unfortunately, some of my colleagues in the biotech industry are making these inflated claims, you know, you’ve seen this in the newspapers, we’re going to have this vaccine in weeks, or—in this and that. What they’re really saying is they could move a vaccine to clinical trials, but this will not go quickly because, as we start vaccinating human volunteers, especially in areas where we have community transmission, we’re going to have to proceed very slowly, very cautiously. The FDA (Food and Drug Administration) is on top of that. They have a great team in place at the Center for Biologics Evaluation Research (CBER). They’re aware of the problem, but it’s not going to go quickly. We are going to have to follow this very slowly, cautiously, to make certain we’re not seeing that immune enhancement. So, you know, now we’re hearing projections, a year, 18 months, who knows? This is not going to go quickly. The bottom line is, had we had those investments early on to carry this all the way through clinical trials years ago, we could’ve had a vaccine ready to go. So we’ve got to figure out what the ecosystem is going to be to develop vaccines that are not going to make money. The Big Pharma companies are still not going in, some of the biotechs are starting to, because they’re trying to really accelerate their technology, and use it—and hopefully to flip it around for something else that will make money. We need a new system in place, and I’m happy to explore that with you more during the questions and answers. Chairman BERA. Right. Dr. HOTEZ. Thank you. [The prepared statement of Dr. Hotez follows:]

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TESTIMONY OF PETER HOTEZ, MD, PHD; PROFESSOR AND DEAN, NATIONAL SCHOOL OF TROPICAL MEDICINE, BAYLOR COLLEGE OF MEDICINE CO-DIRECTOR, TEXAS CHILDREN’S HOSPITAL FOR VACCINE DEVELOPMENT, TEXAS CHILDREN’S HOSPITAL ENDOWED CHAIR IN TROPICAL PEDIATRICS, BEFORE THE COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY OF THE UNITED STATES HOUSE OF REPRESENTATIVES, MARCH 5, 2020 Beyond Coronaviruses: Understanding the Spread of Infectious Diseases and Mobilizing Innovative Solutions Chairwoman Johnson, Ranking Member Lucas, and Distinguished Members of the Committee: Thank you for the opportunity to make remarks before the committee. Over the years, I’ve testified numerous times to both House and Senate Committees, and it’s still a special thrill for me to be here in Washington DC in order to discuss how science can help shape national policies. I’m a vaccine and pediatric-scientist, and for the last decade I’ve served as Professor of Pediatrics and Dean of the National School of Tropical Medicine, where I’m also Co-Director (together with my 20 year science partner, Dr. Maria Elena Bottazzi) of the Texas Children’s Hospital Center for Vaccine Development. At Baylor College of Medicine and Texas Children’s we develop vaccines for neglected tropical diseases and emerging infections. I sometimes say we make the vaccines no one else will make because they’re intended either for diseases of extreme poverty - such as schistosomiasis, Chagas disease, or leishmaniasis - or for pandemic threats and stockpiling. In addition, in 2015-16 I served as US Science Envoy for

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the State Department and White House focusing on vaccine development capacity building in the Middle East and North Africa. I also write books, and as a parent of an adult daughter with autism I’m both a vaccine and autism advocate, and the author of Vaccines Did Not Cause Rachel’s Autism (Johns Hopkins Univ Press). Relevant to today’s hearing, I want to emphasize how investments in US research and development can contribute to global preparedness and our nation’s response to infectious disease. One of our signature programs at Baylor College of Medicine and Texas Children’s is a coronavirus vaccine program. Through support of NIAID, NIH and in collaboration with Walter Reed Army Institute of Research and the Galveston National Laboratory, we developed, tested, and manufactured a very promising recombinant protein vaccine antigen to prevent SARS (severe acute respiratory syndrome), which emerged in Southern China and caused a pandemic in 2003. We also developed a recombinant protein vaccine antigen for MERS (Middle East respiratory syndrome). And now, we’re developing a new recombinant protein vaccine for the new COVID-19 also known as SARS CoV2, or just SARS-2. In addition, we’re conducting studies to see if our initial vaccine that we developed and manufactured for SARS could be repurposed for this new SARS-2 epidemic/pandemic. Our experience developing coronavirus vaccines has provided some insights on where the gaps are in terms of our nation’s emergency preparedness. I have 5 brief observations I would like to share: 1) We must recognize that pandemic threats such as SARS 2 go way beyond public health. As has been reported in multiple news outlets, the epidemic in Central China has severely damaged the Chinese economy, Asian markets overall, and has even promoted political unrest. The point is we’re looking at tens of billions of dollars in losses to the Chinese economy because there was not adequate investment or interest in coronavirus vaccines. In the US, we have a similar vulnerability.

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Committee on Science, Space, and Technology 2) Let me provide an example. Through NIAID NIH support, we developed and manufactured a promising SARS vaccine. It used standard recombinant protein technology, similar to the technology used to license the current hepatitis B and HPV vaccines. The vaccine was protective against SARS challenge infections in lab animals, work done at the GNL, and appeared to maximize safety by minimizing what’s known as immunopotentiation, a problem that sometimes plagues coronavirus and other respiratory virus vaccines. The problem was this: By the time we completed manufacturing the SARS vaccine there was no longer interest in SARS as a public health threat. There was no transmission of SARS anywhere and we could not attract further public and private investments to carry this through clinical trials and licensure. In the end, industry is not interested in investing in a vaccine, which they would have to stockpile. No one wants to invest in a product designed NOT to be used. However, as the information in January 2020 showed that SARS and SARS 2 were about 80% similar and the two viruses bound to the same human receptor in the lungs, it became clear that there was a possibility that we could repurpose our SARS 1 vaccine to fight SARS 2. NIAID NIH is now helping us with some funds to advance this concept, and we are applying to other organizations such as CEPI and even the British Medical Research Council (MRC). But the point is that if that had investments been made previously, we potentially could have a vaccine ready to go now. Potentially it could have rescued the Chinese economy saving billions of dollars, or even the US economy should SARS 2 gain a foothold in the US as predicted by Dr. Redfield the CDC Director. An investment of a few million dollars for clinical trials and stockpiling of this vaccine, could have saved ten billion or even maybe one hundred billion dollars – a 10,000 to 100,000 to 1 rate of return, and in so doing stabilize our global economy. 3) We must recognize that vaccines for neglected and emerging infections fall through the cracks because they are not a priority for

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pharma and biotechs. We need investments in non-profit and academic based product development partnerships such as our center at our National School of Tropical Medicine at Baylor College of Medicine and Texas Children’s or a handful of others such as PATH in Seattle, the University of Maryland CVD to name a few. Those funds would be used to support what we call a “warm base” of scientists at centers of excellence to produce vaccines needed for the health and global security of our nation. 4) I don’t think all of the funds for this should come from the US Government. In my 2016 book entitled Blue Marble Health: An Innovative Plan to Fight Diseases of Poverty amid Wealth (Johns Hopkins Press) I made the observation that to the surprise of many, most of the global health threats from emerging and neglected diseases actually occurs within the G20 nations, especially the poor living in the G20 nations. However, overwhelmingly the public support for global health innovations, including vaccines, comes from just three sources, the US and UK Governments and to some extent the European Union. Currently these three entities provide 86% of public funding according to Policy Cures GFINDER Report.55 Somehow, we need to get the other G20 nations involved, particularly the BRICS nations, such as China, Russia, Brazil, and India, as well as Japan and others. We need them to step up, and this needs to be prioritized at a future G20 Summit. This issue has not been on the radar screen of the G20 leaders and sherpas, but after what we’ve seen occur in China, I believe our President and the Department of State should make this a priority. 5) Finally, in regards to SARS 2 in America, I’m worried. Without a vaccine in-hand, it will be tough to fight this virus. It’s a fight with one hand tied behind our back. We also the have the problem that it looks increasingly likely that we may need to combat both flu and SARS-2 simultaneously. This has been a bad flu season in America, and according to the CDC it will likely continue to last 55

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Committee on Science, Space, and Technology for weeks or months. Tragically, more than 14,000 Americans, including 100 children, have died so far of flu this season, and while we don’t yet have the final data, it appears that many if not most of those adults and children were not vaccinated despite recommendations by the CDC. We have a problem in this country with an aggressive, organized and well-funded antivaccine movement spreading misinformation about the flu vaccine. I’m also worried about measles. Because of the antivaccine movement last year measles returned to the US after it was eliminated in the year 2000. For instance, the epidemic in NY resulted in more than 50 hospitalizations, including 18 admissions to intensive care units (ICUs). Historically measles peaks in late winter and early spring, in other words around this time. As I wrote recently in a Fox News op-ed56, if our local and state health agencies have to simultaneously fight SARS 2, flu, and measles, we’ll simply lose, and this will have a terrible impact on our nation’s economy.

Therefore, I would be happy to discuss at your convenience ways this Committee could take an even greater role in combating antiscience movements. America faces a number of challenges in the coming weeks. We’re now seeing the start of community spread in pockets across the country. It’s tragic that we won’t have a vaccine ready for this epidemic – and practically speaking, we’ll be fighting these outbreaks with one hand tied behind our backs. The good news is that we have the best research universities and institutions the world has ever seen, and optimistic that we’ll eventually regroup to solve important problems. Thank you for the opportunity to share some thoughts with you today.

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Professor Peter J. Hotez MD, PhD, FAAP, FASTMH

Figure 7. Peter J. Hotez

Peter J. Hotez, MD, PhD is Dean of the National School of Tropical Medicine and Professor of Pediatrics and Molecular Virology & Microbiology at Baylor College of Medicine where he is also the Director of the Texas Children’s Center for Vaccine Development (CVD) and Texas Children’s Hospital Endowed Chair of Tropical Pediatrics. He is also University Professor at Baylor University, Fellow in Disease and Poverty at the James A Baker III Institute for Public Policy, Senior Fellow at the Scowcroft Institute of International Affairs at Texas A&M University, Faculty Fellow with the Hagler Institute for Advanced Studies at Texas A&M University, and Health Policy Scholar in the Baylor Center for Medical Ethics and Health Policy. Dr. Hotez is an internationally recognized physician-scientist in neglected tropical diseases and vaccine development. As head of the Texas Children’s CVD, he leads a team and product development partnership for developing new vaccines for hookworm infection, schistosomiasis, leishmaniasis, Chagas disease, and SARS/MERS/SARS-2 coronavirus, diseases affecting hundreds of millions of children and adults worldwide, while championing access to vaccines globally and in the United States. In 2006 at the Clinton Global Initiative he co-founded the Global Network for Neglected Tropical Diseases to provide access to essential medicines for hundreds of millions of people.

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He obtained his undergraduate degree in molecular biophysics from Yale University in 1980 (phi beta kappa), followed by a Ph.D. degree in biochemistry from Rockefeller University in 1986, and an M.D. from Weil Cornell Medical College in 1987. Dr. Hotez has authored more than 500 original papers and is the author of four single-author books, including Forgotten People, Forgotten Diseases (ASM Press); Blue Marble Health: An Innovative Plan to Fight Diseases of the Poor amid Wealth (Johns Hopkins University Press); Vaccines Did Not Cause Rachel’s Autism (Johns Hopkins University Press); and a forthcoming 2020 book on vaccine diplomacy in an age of war, political collapse, climate change and antiscience (Johns Hopkins University Press). Dr. Hotez served previously as President of the American Society of Tropical Medicine and Hygiene and he is founding Editor-in-Chief of PLoS Neglected Tropical Diseases. He is an elected member of the National Academy of Medicine (Public Health Section) and the American Academy of Arts & Sciences (Public Policy Section). In 2011, he was awarded the Abraham Horwitz Award for Excellence in Leadership in Inter-American Health by the Pan American Health Organization of the WHO. In 2014-16, he served the U.S. State Department as US Envoy, focusing on vaccine diplomacy initiatives between the US Government and countries in the Middle East and North Africa. In 2018, he was appointed by the US State Department to serve on the Board of Governors for the US Israel Binational Science Foundation, and is frequently called upon frequently to testify before US Congress. He has served on infectious disease task forces for two consecutive Texas Governors. In 2017, he was named by FORTUNE Magazine as one of the 34 most influential people in health care, while in 2018 he received the Sustained Leadership Award from Research!America. In 2019 he received the Ronald McDonald House Charities Award for Medical Excellence. Most recently as both a vaccine scientist and autism parent, he has led national efforts to defend vaccines and to serve as an ardent champion of vaccines going up against a growing national “antivax” threat.

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In 2019, he received the Award for Leadership in Advocacy for Vaccines from the American Society of Tropical Medicine and Hygiene. Dr. Hotez appears frequently on television (including BBC, CNN, Fox News, and MSNBC), radio, and in newspaper interviews (including the New York Times, USA Today, Washington Post, and Wall Street Journal). Chairman BERA. Thanks, Dr. Hotez. Dr. Sell?

TESTIMONY OF TARA KIRK SELL, SENIOR SCHOLAR, JOHNS HOPKINS CENTER FOR HEALTH SECURITY, AND ASSISTANT PROFESSOR, JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH Dr. SELL. Good morning, Vice Chairman Bera, Ranking Member Lucas, and members of the Committee. Thank you for inviting me to speak about my research on crowd forecasting and misinformation, this research, in context of COVID–19, and ways to support research that improve outbreak response. Traditional disease surveillance is critical during infectious disease outbreaks, however, this information can be supported with tools to help support decisionmaking. One such tool is crowd forecasting. Crowd forecasting consolidates the diverse opinions of many into hard probabilities for future outcomes. This is helpful in engaging the most likely outcome, but also for understanding the uncertainty about that outcome. Over the past year my research team, in partnership with a group called Hypermind, developed a crowdsourced disease prediction platform, and asked forecasters to make predictions about outbreaks. For instance, we asked about the growth of Ebola in the DRC (Democratic Republic of Congo), the spread of measles in the United States, and how many U.S. counties might see cases of Eastern Equine Encephalitis.

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On most occasions, forecasters provided accurate predictions about 3 weeks ahead of time. Recently we focused our forecasting platform on COVID–19. We asked about the number of countries that would have cases of COVID–19, and the number of cases that would be seen around the world, and in the U.S. For global cases, forecasts showed high confidence that there would be a rapid and explosive spread. On a few occasions our predictions were incorrect. We think this is probably because forecasters didn’t have enough information to make accurate forecasts. Essentially, there’s no magic here. If disease surveillance information is lacking, or is delayed, forecasters don’t have any information to go on. This underscores an essential research need for the current COVID–19 outbreak, that surveillance, both within the U.S. and globally, is essential. Another area of my research, misinformation during disease outbreaks has emerged as a challenge during the COVID–19 outbreak, and highlights the need to transparently and rapidly share information. Health misinformation can be defined as false health-related information, and can range from the promotion of fake cures to rumors about the origin of the outbreak. Misinformation can substantially impede the effectiveness of public health response measures, increase societal discord, reduce trust in governments, leaders, and responders, and increase stigmatization. My team and I analyzed misinformation during the 2014 West African Ebola outbreak, one of the most recent examples of a fear inducing disease event for the U.S. public. Our—in our analysis, we found that about 10 percent of the Ebola related tweets had false or half true information. We also saw that more tweets with misinformation were political, and seemed designed to promote discord. Another finding with parallels to COVID–19 was the infection—or the identification of rumors, often focused on government conspiracies. Although we have been—not been able to do a systematic analysis of COVID–19 misinformation, we have seen the spread of rapid—of false information, including recommendations for false cures that could be harmful, like drinking chlorine dioxide, blaming specific ethnic groups, and conspiracy theories about various governments creating the virus as a bioweapon.

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Response to misinformation requires a nuanced approach, and further research to best determine the ways forward. While the solutions will be complex, one thing that is critical is the prevention of an information void that can be filled with false information. Members of the public need accurate and timely information to help them make sense of what is happening in the outbreak. As I advocated for improved disease surveillance earlier, this shows the need for a better collection and communication of disease information in a transparent and rapid manner. From my experience in conducting research in response to emergent disease outbreaks, I believe that we need to reduce the impediments and disincentives to doing rapid and timely research during these events. One hurdle to overcoming—to overcome is the slow response—or slow process to establish Federal funding streams for research during a response. My research was funded by awards from private groups prior to the outbreak, which provided the flexibility to shift gears toward COVID–19. And while the development of vaccines and countermeasures are critical, social, behavioral, and epidemiological research are also important. The best treatment cannot be effective without knowing where the disease is, and who it is affecting. The best vaccine cannot change the course of an outbreak if people refuse to take it. And the best public health response cannot be implemented if members of the public don’t cooperate. My bottom line message is this, we need to support the systematic collection and rapid dissemination of information about outbreaks. The— as the issue of misinformation grows, a dedicated effort to understanding the best ways to combat it will be needed. Even after the COVID–19 outbreak is over, emerging outbreaks will still be a continuing concern. The Federal research space needs to evolve toward a more rapid approach to meet this threat. Thank you. [The prepared statement of Dr. Sell follows:]

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UNITED STATES HOUSE OF REPRESENTATIVES, COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY, TESTIMONY OF TARA KIRK SELL, PHD SENIOR SCHOLAR, CENTER FOR HEALTH SECURITY, JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH, MARCH 5, 2020 Good morning Chairwoman Johnson, Ranking Member Lucas, and members of the committee. Thank you for inviting me to speak at this hearing and for your interest in supporting research. Although preparedness and response activities garner much of the attention in outbreaks such as COVID-19, research plays a critical role in developing the evidence base for the most effective and useful interventions. My focus in this testimony will be several areas of my research that relate to emerging infectious disease outbreaks—crowd forecasting and misinformation—as well as the ways to support important research to improve responses to diseases like COVID-19. I am an Assistant Professor in the Department of Environmental Health and Engineering at the Johns Hopkins Bloomberg School of Public Health. I am also a Senior Scholar at the Johns Hopkins Center for Health Security. The opinions expressed herein are my own and do not necessarily reflect the views of the Johns Hopkins University. The Center for Health Security’s mission is to protect people’s health from major epidemics and disasters and build resilience. We study the organizations, systems, and tools needed to prepare and respond to these events. At the Center, I direct research on crowd forecasting through our disease prediction platform and communication, including misinformation, during infectious disease outbreaks. My testimony is founded on expertise gained through my research and over a decade of work on pandemic preparedness but not specific epidemiological modeling of the COVID-19 outbreak.

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Additionally, my testimony is based on the situation as it stands today and my analysis of publicly shared information. I am not a part of the onthe-ground public health or clinical response to COVID-19. There is still a great amount of information to be learned that will shape our understanding of COVID-19 in the weeks and months to come.

Crowd Forecasting Using the Disease Prediction Platform Traditional disease surveillance—the collection of information about the numbers of cases and deaths, their locations, and other health-related information—is a critical component of preparedness and response to infectious disease outbreaks. However, these data can be enhanced with additional sources of information about the projected course of disease outbreaks to help support decision making. One potential tool to help support decision making is crowd forecasting, which my team and I have used to establish a prediction platform that provides forecasts focused on infectious disease outcomes. Early crowd forecasting efforts, such as the Iowa Electronic Markets, were started in the late 1980s and were often focused on political outcomes. Essentially, crowd forecasting consolidates diverse opinions, expertise, and informed guesswork of many into hard probabilities for future outcomes or events. This is helpful in gauging the most likely outcome but also for understanding uncertainty about that outcome. For instance, although crowd forecasting might predict the outcome of a political race, perhaps saying that one candidate is more likely to win than the other, it will also provide a probability of that outcome. If the probability that one candidate will win is a 51% probability and the other has a 49% probability of winning, then that outcome is very uncertain. Overall, the results of crowd forecasting should allow people to question or confirm basic assumptions and help raise new questions that should be considered. One of the better-known uses of crowd forecasting is the Good Judgement Project, which was supported by the Intelligence Advanced

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Research Projects Activity (IARPA). This project highlighted the ability of so-called super forecasters, using open source information, to outperform intelligence analysts with access to classified data. The best forecasters were skilled in assimilating information and tapping into multiple sources to understand a range of outcomes. We wanted to test this type of method for infectious disease. With the help of Hypermind, a company that is involved in the IARPA work, we developed the Johns Hopkins Disease Prediction Platform. Using this platform, we asked forecasters recruited from all over the world, including super forecasters from Hypermind’s other forecasting efforts, more than 50 questions about ongoing disease outbreaks. At last count, we had more than 1,000 registered users from 88 countries and more than 500 active forecasters, although not every forecaster answers every question, and some attrition occurred over the course of the project. Forecasters were from a number of different fields; the most common were public health, medicine, and academia, but there were also participants from vector control, the pharmaceutical industry, biotechnology, veterinary medicine, and policy. We considered this range of geographic location and expertise an advantage in potentially developing real-time on-the-ground, crowdsourced prediction data from individuals with high awareness of the health issues in their local communities around the globe. We asked forecasters to make predictions about a range of outbreaks and locations. For instance, we asked about the growth of the Ebola outbreak in the Democratic Republic of Congo, the spread of measles in the United States, how many US counties might see cases of Eastern Equine Encephalitis, and what the most prevalent influenza virus type at the end of 2019 would be. In order to make these questions work on the platform, we needed to have some way to determine the final correct answer once the time period for the question was over. This limited our questions to diseases that had active traditional disease surveillance efforts around them. On most occasions, forecasters accurately predicted the infectious disease outcome we asked about—on average about 3 weeks ahead of the outcome in question.

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Recently, we have focused our forecasting platform on COVID-19. This was possible only because we had previously received the resources and had time to build our forecasting platform and our forecaster pool before the emergence of COVID-19. When the epidemic started, we were ready to ask forecasters about this emerging disease. Early in the outbreak, we asked about the number of countries that would have cases of COVID19 by the end of February and the number of cases that would be seen around the world and the US. For global case counts, forecasts showed high confidence that there would be rapid and explosive spread, which we eventually saw. We also asked questions that compared reported numbers of countries with COVID-19 to forecasters’ estimates of actual on-theground numbers of countries with COVID-19. Although reported case counts are the only official count, our platform showed that the potential lack of reporting, possibly due to poor disease surveillance, was a significant area of uncertainty. This project also underscores an essential research need for the current COVID-19 outbreak—that surveillance both within the US and globally is essential to understanding what is going on with the disease, planning necessary responses, and thinking ahead to what will happen. It is important to note that on a few occasions, we found that our predictions did not match up with the right answers or were very delayed in identifying the correct outcome. We think that this is probably because forecasters still need reliable information about what is going on in the outbreak in order to make accurate forecasts. Essentially, there is no magic here. If disease surveillance information is lacking or is delayed, forecasters don’t have any information to go on.

Misinformation during Infectious Disease Outbreaks Another area of my research, misinformation during disease outbreaks, has emerged as an important challenge during the COVID-19 outbreak. Health misinformation can be defined as false health-related information and can encompass a wide range of messages—from the promotion of fake

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cures to spreading rumors about the origin of the outbreak. Some false information may also be defined as disinformation if it is intentionally false and created to mislead receivers of these messages. Although the existence of misinformation and disinformation is not a new problem, the emergence of new communication platforms and access-enabling technology, such as social media and cell phone apps, that connect networks of people who often share similar opinions and beliefs, has exacerbated and amplified this problem. Misinformation and disinformation can substantially impede the effectiveness of public health response measures, reduce trust in public health leaders and responders, and increase stigmatization or scapegoating of affected communities. A number of researchers have been working in the field of health misinformation. Some have identified health issues, such as vaccines, as areas susceptible to the promotion of public discord. Misinformation spread during the Ebola outbreaks in West Africa and the Democratic Republic of Congo has contributed to violence against healthcare workers, social instability, and increased community transmission. Rumors and conspiracy theories have also fueled distrust of governments during outbreaks at a time when collaboration and cooperation are critical. My research focuses on health misinformation during outbreaks. Specifically, I have led a team analyzing misinformation during the 2014 West African Ebola outbreak, one of the most recent examples of a fearinducing disease event for the US public. We chose this outbreak because of the potential lessons it could teach for future fear-inducing outbreaks, including the current COVID-19 outbreak. In our analysis, we found that about 10% of the Ebola-related tweets we looked at had false or half-true information. For example, this tweet with false information focuses on a debunked rumor: “Renown #NSA #Whistleblower: #Ebola Could Be Staged Event To Pillage Africa's Natural Resources | EPIC @Infowars #News.” Half-true tweets generally included some true information but also suggested something that was not true. For instance, the following tweet correctly notes that a patient was

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being tested for Ebola but suggests that there was an actual case: “There Is an Ebola Patient in a Fairfax County Hospital I'm Going to Canada.” We also saw that more tweets with misinformation were political and seemed designed to promote discord among readers. Discord-promoting tweets were those that aimed to generate a response from and conflict with other Twitter users. Another important finding, and one with parallels to COVID-19, was the identification of several specific types of false rumors. Most often rumors focused on government conspiracies, but they also included mention of rumors that Ebola was airborne—a transmission feature it did not possess. However, we did see an effort on the part of Twitter users to refute this rumor. Although we have not been able to do a systematic analysis of misinformation during the current COVID-19 outbreak, we have seen evidence of a range of different types of false information. These include recommendations for false cures that could be harmful, like drinking chlorine dioxide, and scapegoating and blaming of specific ethnic groups, such as those with Chinese heritage. Other misinformation includes accusations of conspiracies that various governments created the virus as a bioweapon. This outbreak has also highlighted another emergent theme from our research with Ebola: the idea that not all misinformation is the same. As I noted earlier, some health-related information may be completely false or even deliberately false, but there are many cases in which information is partially true or a misinterpretation of facts. This is a misinformation gray area. In the case of COVID-19, early erroneous reports of SARS cases in Wuhan were technically incorrect, but in hindsight, they had an element of truth in them that would have been helpful to understand earlier. In considering this, my team and I have come to believe that the response to misinformation requires a nuanced approach, one that we have yet to find the best formula for. In an effort to chart that course, one of my team members, Divya Hosangadi, is currently cataloguing global misinformation management efforts—from rumor correction programs to criminalization of the publication of health misinformation—and she is reviewing the use of

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these actions in the context of the COVID-19 outbreak. We hope that this research will help us better understand the best interventions for managing misinformation so that policymakers can be ready for the next epidemic. While the solutions to the problem of misinformation are complex and still to be determined, we know one thing that is critical in communication is the prevention of an information void that can be filled with false information. When people are faced with an uncertain situation, they engage in sense-making—that is, pulling in and testing information in order to develop an explanation for what they are seeing. Members of the public need accurate and timely information to help them make sense of what is happening in an outbreak. Just as I advocated for improved disease surveillance earlier, this is another case that shows the need for better collection of information about the disease in a transparent and rapid manner. This information should be provided to both the public and to policymakers—the latter, so that they can use the information to make smart policies and to use their position as influencers to spread true information.

Supporting Research From my experience in conducting research in response to emerging disease outbreaks, I believe that one potential area of improvement in the support of research is to reduce the impediments or disincentives to doing rapid and timely research during these events. For instance, one potential hurdle to overcome is the slow process to establish federal funding streams for research during a response. The research I have described today is funded by grants or gifts from private groups—Open Philanthropy and Founders Pledge—which were put in place prior to the outbreak and provided the flexibility to investigate how existing research streams intersected with COVID-19. Although the federal research space is not devoid of rapid research funding, in my experience, this process takes time. In a rapidly evolving world with fast-moving outbreaks, we need a more

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nimble and agile research support system implemented by the federal government. These impediments are particularly applicable to social and behavioral research, which can often require data collection in the form of interviews, focus groups, and surveys. Several of my past research efforts have taken the form of research contracts. While these provide the opportunity to work closely with excellent technical staff at federal agencies, they also come with a number of hurdles to overcome. One particularly vexing difficulty is managing Paperwork Reduction Act (PRA) requirements, which measurably slow the process of doing research, increase costs in researcher time, and disincentivize research that would require PRA approvals. During this response there has been an emphasis on reducing barriers and speeding the development of vaccines and countermeasures. While this is a critical area for research, the research I have discussed today highlights the need for reducing impediments to social, behavioral, and epidemiological research as well. The best treatment cannot be effective without an understanding of where the disease is and who it is affecting. The best vaccine cannot change the course of an outbreak if people refuse to take it. And the best public health response plan cannot be implemented if members of the public refuse to cooperate.

Recommendations 1) Fund and support the collection of disease surveillance information. Disease surveillance information underpins cuttingedge forecasting efforts that may help predict disease outcomes. This information is critical to understanding the disease, establishing the most appropriate responses, and planning for a range of potential scenarios. 2) Transparently and rapidly share information about disease outbreaks. Misinformation and disinformation breed in an information void. Timely and accurate information should be provided to both the public and to policymakers. Further research

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Committee on Science, Space, and Technology on misinformation and disinformation is needed to help develop an appropriate response strategy. 3) Improve the speed and agility of federal research funding during outbreaks. Outbreaks currently move more quickly than most federal research dollars. Improvements in this area will allow researchers to contribute their expertise to developing effective outbreak response strategies while they are ongoing, rather than after the event has subsided. 4) Remove impediments that disincentivize rapid research during outbreaks. Overwhelming approval requirements can prevent necessary research from occurring in the timeframe necessary to make an impact on an evolving outbreak and may disincentivize research altogether.

Conclusion My bottom-line message is this: The federal research space needs to support the systematic collection and rapid dissemination of information about outbreaks, including case counts and epidemiological information, as an essential component to both outbreak response and research. Transparency in the ways that data are collected, the protocols for diagnostic testing, and potential data gaps is critical to ensure that researchers and practitioners can interpret data correctly. Information voids lead to uncertainty and suspicion, where misinformation can breed. As the issue of misinformation grows, a dedicated effort to understanding the best ways to combat it will be needed. Even after the COVID-19 outbreak is over, emerging outbreaks and associated misinformation will still be a continuing concern. The federal research space needs to evolve to meet this threat. Thank you for your time and attention.

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Biography for Tara Kirk Sell, PhD Dr. Sell is a Senior Scholar at the Johns Hopkins Center for Health Security and an Assistant Professor in the Department of Environmental Health and Engineering at the Johns Hopkins Bloomberg School of Public Health. At the Center, she conducts, manages, and leads research projects to develop a greater understanding of potentially large-scale health events. She also serves as an Associate Editor of the peer-reviewed journal Health Security. Dr. Sell’s work focuses on improving public health policy and practice in order to reduce the health impacts of disasters and terrorism. Her primary research interests focus on health security: the broad intersection of public health and national security. She studies past responses to public health emergencies to discover ways to improve future preparedness and response. From terrorism to pandemics and natural disasters, she employs mixed methods and multidisciplinary approaches to examine how the public, practitioners, and policymakers prepare for and respond to public health emergencies. In turn, she works to build the evidence base to advance policies and practices to minimize impacts of emergent threats. Though seemingly distinct, these topics are all linked by crosscutting preparedness and response needs critical to the improvement of the field of health security. A hallmark of her work is the discovery of scientifically rigorous results while simultaneously interfacing with policymakers, public health practitioners, and the general public to translate research findings into actionable and evidence-based practices. She works collaboratively and purposefully to translate and disseminate findings and recommendations to target audiences in meaningful ways, such as engaging in collaborative work with the CDC to improve public communication or co-developing Event 201, an immersive pandemic scenario to engage new stakeholders such as the private sector in pandemic preparedness. Dr. Sell joined the Center in 2009 as an Analyst and subsequently served as Senior Analyst and Associate. Prior to joining the Center, she maintained a career as a professional athlete. She was a member of the

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USA national swim team for 8 years, and she served as captain for 6 USA national swim teams. In 2004 she broke the world record in the 100 breaststroke (Short Course Meters), and she earned a silver medal at the 2004 Olympics in Athens. Dr. Sell completed her PhD at the Johns Hopkins Bloomberg School of Public Health in the Department of Health Policy and Management, where she was a Sommer Scholar. Her dissertation work focused on public policy responses to emerging epidemics and specifically how the media and policy intertwine in the case of Ebola and the health consequences of these policy actions. She received a BA in human biology and an MA in anthropological sciences from Stanford University. In 2005 she was a Rhodes Scholar finalist. Chairman BERA. Thank you, Dr. Sell. Before we proceed, I’d like to bring the Committee’s attention to a letter that Chairwoman Johnson received in preparation for today’s hearing, letters from Johnson & Johnson (J&J) that highlights their global response to the COVID–19 virus. Without objection, I’m placing this document, and Chairwoman Johnson’s opening statement, in the record. [The prepared statement of Chairwoman Johnson follows:] Good morning and welcome to today’s hearing. We have an excellent panel of witnesses today, all experts in their field. I look forward to a robust discussion of how science can help control and mitigate the effects of emerging infectious diseases, especially in light of this recent coronavirus outbreak. Unfortunately, outbreaks of new infectious diseases are happening more often and infecting more people. Changing ecosystems, economic development and land use, climate and weather, and international travel and commerce are all examples of ecological, environmental, and social factors that are increasing the emergence and spread of disease. The size of the current COVID-19 outbreak has created a public health crisis with significant international dimensions. A successful public health response relies on science- not only through rapid and robust research during an outbreak, but through sustained investments in research and development between epidemics.

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As more people interact with technology in their day-to-day lives, we have new ways of harnessing data. Scientists are developing modeling techniques that use artificial intelligence to predict where viruses may emerge and how far they’ll spread. Policymakers use these programs to inform efforts that seek to prevent and control the spread and impact of disease. We also rely on scientists to develop diagnostic tests and treatment options and evaluate new drugs and vaccines. It is clear how our research and development investments directly impact our ability to prepare and respond to global emergencies. Every decision we make must be rooted in science. The outbreak of global viruses is often followed by the spread of misinformation, especially about how or where the virus originated and the government’s response to control it. A whole country or group of people may be singled out as the source of the problem-rather than the pathogen. This is hardly a new phenomenon, but the spread of misinformation during this current outbreak has been accelerated by social media. The World Health Organization has even labeled this outbreak an “infodemic,” meaning there is so much information out there that it is hard for people to find trustworthy sources and reliable guidance when they need it. Given that COVID-19 is a new disease, it is understandable that its emergence and spread may cause confusion, anxiety, and fear. But if we let these emotions guide us, instead of science, we will see the rise of harmful stereotypes that will prevent people from accessing the health care they need. We have already seen reports of public stigmatization against people from areas affected by the COVID-19 outbreak. Coupled with the health impacts of the virus itself, this is of grave concern. According to the World Health Organization, recent disease outbreaks like SARS, MERS, Ebola, and Zika have highlighted the need to use social science to fight deadly disease outbreaks and epidemics. Additional investments in social science research on combatting misinformation during outbreaks could improve prevention and control efforts and strengthen global public health communication. We need a holistic research and development response now more than ever.

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As the first nurse elected to Congress, I have been dedicated to public health my entire career. Our Committee may not have jurisdiction over the Health and Human Services agencies, but we have long had a role in amplifying the voices of our nation’s best scientists and bringing them to the forefront on an issue. Thousands have been affected by COVID-19. We do not know how many more will be. We must do everything in our power to ensure that science guides our response to this outbreak and prepares us for the future. Thank you all for being here this morning. And I thank Vice-Chair Bera for his leadership on this issue. Chairman BERA. At this point we’ll begin our first round of questions. The Chair recognizes himself for 5 minutes. Dr. Hotez, you touched on some of your research into developing a coronavirus vaccine and, you know, a SARS vaccine. I think it’s incredibly important since, you know, Dr. Sell just talked about information and misinformation, we’ve heard quite a bit about how quick we’re going to get a vaccine, how quickly that’ll be available to the public. And I think just, you know, this morning I woke up to a news alert that said a Cambridge, Massachusetts biotech company had come up with a vaccine that they’ve sent to Dr. Fauci to start looking at testing and so forth. But I think we’ve got to be honest with the public so we don’t give them false hope. And, you know, perhaps—if you could just go through a timeline on what vaccine development is going to look like in the best case scenario, then to clinical trials, and then to potential public availability? Dr. HOTEZ. Sure. Thank you for that question. So I think what we’re going to see over the next few weeks to months is several vaccines will enter into a pipeline of clinical trials. Hopefully ours will be one of them. You mentioned the Moderna vaccine out of MIT (Massachusetts Institute of Technology). Theirs will—certainly will be in there. Probably Inovio’s another one. There’s about five or six—J&J may have one as well. About five or six, maybe a couple more. But then it’s going to go into a bottleneck, and that bottleneck are the clinical trials, phase one, phase two, phase three trials.

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You know, in spite of what the anti-vaccine lobby likes to claim, that vaccines are not adequately tested for safety, in fact, among the pharmaceuticals, vaccines are the single most tested pharmaceuticals we have for safety, and it takes time. And because you have to initially do an injection in normal human volunteers, show that it’s safe, and then you proceed, step-wise, to show that it actually works. And now, because of this immune enhancement phenomena, you have the added complexity because you want to make certain that those volunteers, when they’re immunized in an area of community transmission, don’t actually get worse. And so the FDA and CBER—which, again, you know, I can’t emphasize enough how lucky America is to have that group, some of the best public health vaccine scientists in the world—are going to follow this very closely, step-wise. And that—— Chairman BERA. The best case scenario—— Dr. HOTEZ [continuing]. And that’s not quick, right? That’s going to take—— Chairman BERA. Best case scenario, Dr.—and Dr. Fauci said at least 12 months. Dr. HOTEZ. And he’s definitely right, at least 12 months, but whether that means another year after that, maybe 2 years, it really depends on the safety signals that we’re seeing with these vaccines. Chairman BERA. OK. And the ability of our commercial pharmaceutical sector to quickly ramp up and develop that—the vaccine, and make it commercially available, is that going to be an issue, or do we have that—— Dr. HOTEZ. Yeah, I mean, there’s a lot being—there’s a lot of press releases from the biotechs, and some of them I’m not very happy about, frankly, because I think it’s telling only half the message. You know, there’s—so it took us years to develop our recombinant protein vaccines. It’s an old method, but we know it works, because we’ve had a Hepatitis B vaccine licensed with this technology, the HPV (human papillomavirus) vaccine licensed with this technology. Now you’re seeing next generation platform vaccines, like DNA (deoxyribonucleic acid) and RNA (ribonucleic acid) vaccines. It’s a very exciting technology because you

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can move very quickly into clinical trials. The problem is we don’t have a single licensed vaccine with that technology. So the idea that all of a sudden this is going to work, you know, historically, these have worked very well in mice and laboratory animals, but they haven’t been reproduceable in people. Organizations like Moderna and Inovia say they’ve gotten around it now, they’ve fixed the—they’ve fixed this—— Chairman BERA. Right. Dr. HOTEZ [continuing]. So maybe they have, but, you know, it’s— Chairman BERA. Right. Dr. HOTEZ [continuing]. Still we don’t have—— Chairman BERA. So we’re—— Dr. HOTEZ [continuing]. A lot of experience. Chairman BERA. We’re moving at an incredibly rapid pace right now, but the public needs to understand that, at best, there may be a vaccine in 12 months, it’ll be longer—— Dr. HOTEZ. Yeah. I mean—— Chairman BERA [continuing]. Potentially longer than that. Dr. HOTEZ. I mean, look at what happened with—— Chairman BERA. Yeah. Dr. HOTEZ. [continuing]. Ebola, right? We had, you know, our first Ebola vaccines started being rolled out in 2015 in the epidemic in West Africa. It’s not really until 2019 that we really got it rolling, which, by the way, is one of the most extraordinary public health stories ever told. Chairman BERA. It absolutely—— Dr. HOTEZ [continuing]. And, you know, thanks to BARDA, and all these—— Chairman BERA. Exactly. Let me ask Dr. Sell a question. You talked about information and misinformation. Based on your research as you’re observing this, what are some of the common misinformation that is out there on COVID–19? Dr. SELL. Yeah, so I think that there’s a range of different misinformation. So there’s misinformation about false cures, and there aren’t any cures right now, so all that is false. There’s misinformation about sort of government conspiracies, that someone else started the

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disease, and I think there’s also misinformation about the disease, you know, what characteristics it has. I think there’s a lot that we don’t know, and so there’s that information void that people are just filling with their ideas. Chairman BERA. So it is—it behooves this institution, and each— vested Members of Congress to make sure we’re in tight communication with our constituents back home. With that, let me recognize the Ranking Member, Mr. Lucas, for 5 minutes. Mr. LUCAS. Thank you, Mr. Chairman. And, Dr. Hotez, thinking about Dr. Sell’s comments, let’s begin from the parochial perspective, being your neighbor up north in Oklahoma. As of last night the State Department of Health reports there are no confirmed positive cases of coronavirus in Oklahoma, as of yesterday evening, although one Oklahoman showing symptoms is waiting on the test results from CDC. Can you discuss for a moment what we can share with our constituents back home to not instill panic, and how to stress the importance of reasonable steps, prevent spread? Yes, doctor? Dr. HOTEZ. Peter Hotez. Yeah, I—we—I know Oklahoma pretty well. My son graduated from OU, so—just last year as a petroleum engineer, so he’s—it was a great place. We love Norman. Mr. LUCAS. Absolutely. Dr. HOTEZ. The issue is this, you know, I think, in an attempt to calm public fears, you’re hearing things like it’s a mild illness, this is like flu. It’s not really the case, because this is an unusual virus. For many young people especially it is a mild illness, but we’re seeing some devastating things, and we got a heads up about this from the Chinese. They actually informed us, and we knew it was coming. Nursing homes, look what this virus did in that nursing home in Kirkland, Washington. It rolled through it like a train, right? It’s at least seven deaths so far in a nursing home of about 100 people, so this is like the angel of death for older individuals. We need to go back and support all of our nursing homes—I don’t know what we’re doing wrong, but clearly that nursing home was not prepared for this, and I’m going to guess nursing home in— across Oklahoma are not prepared as well. Also our healthcare providers. We saw

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in Wuhan 1,000 healthcare providers got sick, and we had at least 15 percent severely ill and in ICUs (intensive care units), and that is very dangerous because not only do you subtract those people out of the healthcare workforce, but the demoralizing effect of colleagues taking care of colleagues is going to be—I mean, the whole thing can fall apart if that starts to happen. We saw this with Dallas. So I was on Governor Perry’s task force for infectious disease, and those two ICU nurses, when they got sick, it was really devastating. And finally the Governor had to call the Health and Human Services Secretary, CDC Director, and said, look, I—normal ICUs can’t take care of these patients, we’ve got to get them out of here. So you don’t want to see those kinds of situations. I’m worried about our first responders. We’re already seeing in Washington State how they’re already in quarantine. So does that mean we’re going to have to bring in the National Guard? I think that’s going to be another big issue as well. So those are the three vulnerabilities that I see right now in a place like Oklahoma. Mr. LUCAS. And how should our constituents back home react to that, the average J.Q. Public out there? Dr. HOTEZ. Well, I think the average J.Q. Public needs to hear from its elected leaders, from the Governor, from the public health authorities, on what the plan is. I mean, don’t just get up there and say, this is a flu, this is a mild illness. One, it’s not true, and people in Oklahoma are pretty smart, and they’ll figure that out pretty quickly, and second, explain what the risks are, these are the three vulnerable populations that we have to worry about, and here are the steps that we’re doing to mitigate that. That’s what people will appreciate. Mr. LUCAS. Dr. Murray, as you mentioned in your opening statement, approximately 75 percent of emerging infectious diseases originate in zoonotic pathogens. You estimate that 1.7 million unknown viruses yet to be discovered, around half of which are capable of infecting people. Could you elaborate on the current state of research to improve surveillance in these diseases, and where gaps may exist now as we look toward the future, about addressing future challenges?

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Dr. MURRAY. Yes, thank you very much, and I also appreciate that, while we’re trying our best to address the topic at hand of a lot of ill people, we do need to be thinking of the next virus, and the next virus. I also think that the CDC has done a wonderful job of looking at and studying human health, and, if we’re going to do our best job to prevent future viruses from jumping, I think one of the missing components is indeed wildlife health. If 75 percent of the viruses come from wildlife, it makes sense that we look at that juncture of both wildlife and human health. We also—this virus is termed a novel virus, it’s new. It’s new to the people. I don’t think it’s new to the bats, and that’s—right? That’s an important point. And then some of our other colleagues here have been talking about modeling, and how important that is. Modeling gives us greater information now as to what COVID will be doing within the U.S. and within other countries. We also have groups of modelers who look at the forefront stages, before emergence, and look at the data that we have to try and determine where are the hot zones, what are the risk factors, and, behaviorally, what are people doing to put themselves in danger? Those are really, really important ways for us to get ahead of the curve and catch the viruses before they come out. As part of the team that we’ve been on, which is a USAID (United States Agency for International Development) program called Predict, we have a team of modelers who look at viral emergence, and they’re able to determine for each different virus how— as we collect more and more data, what percentage of the viruses that we know are characterized, and how many more are likely to be out there? Latest estimates are less than 1 percent—well, the viruses we know are less than 1 percent of the viruses that are out there, meaning there’s over 99 percent viruses in wildlife waiting to jump into humans. That’s staggering, and that’s really one of the things that we need to look at. Mr. LUCAS. Thank you, Mr. Chairman. My time’s expired. Chairman BERA. The gentlelady from Oregon, Ms. Bonamici, is recognized for—— Ms. BONAMICI. Thank you——

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Chairman BERA. [continuing]. 5 minutes. Ms. BONAMICI. [continuing]. Dr. Bera, and Ranking Member Lucas. This emergent coronavirus epidemic is a top concern for Oregonians, and I’m glad we’re having this hearing today. In Oregon we currently have three individuals who have tested positive, two of whom are in the district I represent, plus I have an additional couple of constituents still in Japan who had been on the cruise ship there. We know further community transmission is likely. It’s clear, from the tragic deaths in Washington, how this virus can spread quickly, and cause serious harm, and so let’s take a moment to reflect on those who have lost their lives in our neighboring States of Washington, and now we understand there’s a reported death in California as well, all the affected friends and family of those people. We need to take this seriously. I also want to recognize the tireless efforts of our public health officials in Oregon, and the Pacific Northwest, and across the country. I know they’ve been working around the clock to coordinate a response. For the past several days I’ve spoken with our Governor, Kate Brown, and many State and county public health officials, and school superintendents—we had a school closed in Oregon for a couple of days— healthcare providers. And everyone has emphasized the need for robust funding, and I’m glad we passed a bill with strong bipartisan support in the House here yesterday. I hope they get it over the finish line soon in the Senate. But I’ve also heard numerous concerns about the availability of protective equipment, particularly masks. Also staffing challenges, and testing capability. And we know those infected with COVID–19 can remain asymptomatic for several weeks, so healthcare professionals, as Dr. Hotez was talking about, are at even greater risk. There are furloughed healthcare workers in my district. The CDC just expanded its guidance for testing, but there’s still a significant amount of confusion about who should get tested, and how those increasing testing capabilities can best be used to inform and improve our response efforts. And we heard this morning South Korea’s testing 15,000 people a day. Dr. Brownstein and Dr. Hotez, we can’t get an

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accurate picture of the infection if we’re not testing, but until recently, the testing was limited to those who had recently traveled to places with high rates, or those showing symptoms after close contact. So I understand the process of getting the tests out into the field is slow. We had the test sent to the CDC the—on Friday, and then it didn’t come back until Tuesday, and that’s really hard for a community that’s wondering what’s happening. So can you explain whether the scope of the CDC’s guidance—was that based on best practices, or was it inappropriately limited because—a lack of capacity to test, and who should be tested? Dr. Brownstein and Dr. Hotez? Dr. BROWNSTEIN. Of course, it’s hard to delve too deep into what was happening at the CDC at the time, but, of course, increasing testing is incredibly important. We know that this is a mild condition. Oftentimes people might be feeling symptoms, they may not even be interacting with a healthcare provider, and so we don’t actually know the full scope of numbers of cases that are out there. And I think you mentioned a really great point about the impact on the health system. We are really advocating for opportunities to bring concepts like telemedicine, and tools that help at the front line, beyond the point where someone actually has to come in and end up in an emergency department. There’s opportunities to think about tools that actually provide symptom checkers that integrate data from the CDC, but also have virtual visits with providers. This is a real important component, because—— Ms. BONAMICI. Absolutely. Dr. BROWNSTEIN [continuing]. What we expect is an influx of people coming into our health system. I work in a health system. We are very nervous about the flooding of our emergency departments with potential cases, so the opportunities to bring digital tools and innovative solutions, along with the ability to integrate with testing—so home based testing, other opportunities—are really things that we advocate for because of the fact that, again, mild illness, lack of opportunities for someone to come and meet with someone live, and for the fact that we can actually begin to understand the depth of what’s happening in the population, again, those kind of data points are so critical now to understanding——

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Ms. BONAMICI. Absolutely. Dr. BROWNSTEIN [continuing]. The features of this epidemic, and to understand more broadly what’s happening in the community. Ms. BONAMICI. Thank you. Dr. Hotez, as I mentioned, the test was presumptive on Friday, sent to the CDC, it didn’t come back until Tuesday. Can you elaborate on some ideas why we’ve seen such delays in testing? Do you think this recent emergency use authorization will expedite things, and what else can we do to increase the availability and accelerate the testing? Dr. HOTEZ. So four brief points are around that, and thank you for that question. I think the first is testing for respiratory viruses is not trivial, because you get a—oftentimes, and we’ve been seeing this in China, and this is actually not unusual, if you look at the literature on testing for respiratory viruses, you get a negative result, a negative result, a negative result, you put the person on a quarantine, all of a sudden they’re positive. What does that mean? Is it a true false negative, or is it because the test isn’t sensitive enough? So it takes time to really fine tune these diagnostic tests for respiratory viruses. And, in fairness to the CDC, testing—developing a new diagnostic test, just like developing a vaccine in the middle of a public health crisis, developing new technologies for a new agent in a public health crisis, one of the hardest things that we do as a nation. So this—so—and it’ s hard to make that go quickly. I understand we could’ve—we should’ve done better as a country of getting those kits out there. I think we will get up to a million eventually, as I believe the Vice President mentioned, but until we do that, I think we’ve got to prioritize who gets tested, and my recommendation would be that we focus the testing strategically around our protecting our three most vulnerable populations that I mentioned. Our older residents in nursing homes and places of assisted living, they’re highly vulnerable. The mortality among them is—— Ms. BONAMICI. Right.

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Dr. HOTEZ. [continuing]. 10 to 15 percent. The healthcare providers, those who interact with the healthcare providers, and protecting our first responders, because if they go down, then, again, everything collapses. Ms. BONAMICI. OK. Dr. HOTEZ. But then, even after that, I think the other thing that not a lot of people are talking about, even then, this is not adequate, right? If we have to wait hours, or days, for the test result, it’s of limited use to us. What we need is like what we have now for a rapid flu test. We need to get a rapid test for that. Ms. BONAMICI. Thank you. My time’s long expired. I yield back. Chairman BERA. Thank you. Let me recognize the gentleman from Florida, Mr. Posey, for 5 minutes. Mr. POSEY. Thank you, Mr. Chair, for calling this important hearing. I only regret that it conflicts with a Member’s only briefing on almost the exact same topic taking place simultaneously. And thank you, witnesses, for the important work that you do every day, thinking about ways to combat public health threats. There’s a common theme across your testimony, and that’s pretty much when there’s a crisis all eyes turn to you, but when the disease or the crisis moves off the front pages, the public loses interest, then the funding goes away. And you didn’t say this part, but I’ll say this also, that when Washington sees a problem, the habit is to throw billions of dollars at it and say, look, now we’ve done our job, and hope for a good result, and move on to the next issue. And, of course, there’s always the finger pointing and blaming, based on, as you well pointed out earlier, much information and disinformation. That’s really regrettable, and I think the American people are getting a little tired of that, but Dr. Murray, working with partner agencies you state you’ve successfully identified over 1,200 novel wild-born illnesses, including 161 of which belong to the same family as COVID–19. I think most of us in the room are wondering what the risk to humans is from those viruses as well? I have four related questions that I’ll ask you after—— Dr. MURRAY. Thank you very much. I’ll try to be quick in my response. So in addition to identifying the viruses, we also have a team of

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modelers who helps us identify where to look in the world. We also have a team of phylogenists and virology experts who then rank all these viruses. If we had enough money to look at every country, every species, every animal, we would, but we don’t, so we really try and use funds effectively, so we identify the countries in which—are most likely to be a problem, the species that are most likely to transmit lethal diseases to humans, primates, bats, and rodents, and then, of those 1,200 viruses, they’re ranked according to the families that are most likely to cause a problem for human health, and that’s where we spend the majority of our time and resources. Influenzas, coronaviruses, filoviruses, and paramyxo-viruses are some of the most important families. Just to add on to what my colleagues here have said, it is the time from—funds are an issue, and the program that I’m describing is just in the process of being closed down. We’re actually holding our closeout session on March 17 at the Museum of American Indian, in case anybody would like to join us, because we’ll be reporting on a lot of what we’ve done over the last 10 years. My suggestion would be this is not the time to lean out, but it’d be the time that we need to be leaning in. Mr. POSEY. What percentage of the viruses have the potential to jump to humans? Just swag it, I mean. Dr. MURRAY. So of the 1.7 as yet unidentified viruses, about 50 percent of those have the potential to jump to humans, and that’s based on the receptor sites, and where they can attach to the trachea. Of those—but not all of those are going to spread rapidly, and not all of those are going to cause severe disease. So we look at— there’s 50 percent that could jump to humans, and probably only 10 percent or 15 that can cause rapid disease and a pandemic. But until we identify those viruses, the species in which they occur, the reservoir species, and the mode of transmission to humans, we’re really still at a tremendous risk. And then we—the research has shown that these outbreaks are coming more and more frequently, so while everybody—a lot of us have felt like, this is a surprise, the folks in the health community have felt like this isn’t a surprise. We’ve been saying it collectively for the last several years,

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these pandemics are coming. We can tell you in general the countries or the areas, some of the risk factors, and some of the viral families. Mr. POSEY. Well, you answered my next two questions about the percentages already, so, for the final question, how do you think we best prioritize research? You know, is there a good process to set research priorities in place? Dr. MURRAY. I think a lot of what we’re doing right here—and thank you for this hearing. It does bring everybody—a lot of the same folks into the room to help identify some of the issues. From my perspective, the more that we can look at bringing experts from many different fields, from the government, from NGOs (non-governmental organizations), and universities together, then that— and the confluence of human physicians—well, most physicians are human, right? So human physicians, veterinarians, nurse—and nursing staff researchers, I think that’s really what we need to be doing, and looking at not only in the U.S., but in countries—in other countries as well, because—we look at the economy globally. It’s really time for us to look at health globally. So that’s how I would go about establishing research priorities. Mr. POSEY. Thank you. That beats crisis du jour. Dr. MURRAY. Thank you for your questions. Chairman BERA. Thank you, Mr. Posey. The gentlelady from Texas, Mrs. Fletcher, is recognized for 5 minutes. Mrs. FLETCHER. Thank you, Chairman Bera. I want to get right to the questions. I thank all of you for being here, for your testimony. It’s very important. I want to follow up with you, Dr. Hotez, on your opening comments with a question, and then open it up to the panel to weigh in with your thoughts. But, kind of following up on what Mr. Posey asked as well, in your opening comments, or your statement, you mentioned your work developing a vaccine for SARS, and you asked the question what will the ecosystem be for vaccines that don’t make money? And that seems to be an appropriate question for this Committee, and for the Congress of the United States to be tackling. So I would like to ask you what you think that ecosystem should look like, and then get others on the panel to weigh in on that question, and also touch a little bit on what

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Dr. Murray said about kind of the global nature, and something we have discussed before as well, where can we partner with other countries in doing this work, and where can we have a national response and a global response? I’d love to get your thoughts, and then open it up to the panel. Dr. HOTEZ. Well, thank you very much for that question. I mean, there is some good news to this. You know, we—we’re very blessed to have the National Institute of Allergy and Infectious Diseases, headed by Dr. Fauci, who’s been very committed to this problem. And, you know, if it wasn’t for NIAID and NIH, I wouldn’t be—even be here, right? They’ve, you know, really worked hard around trying to fix this problem. The issue is it’s not enough, and it doesn’t—and the problem is, you know, if you talk to Tony—if you talk to Dr. Fauci, he’ll say, look, Peter, I’m not a venture capitalist. I can’t just hand over money. It’s got to go through study sections. And the issue is the study sections—some—oftentimes will get dinged and get turn down from an NIH grant because what we’re— they’ll claim what we’re doing is not innovative, and they’re often right. It’s not innovative. We’re trying to make a recombinant protein vaccine. It’s boring, but it’s absolutely essential. So we have to figure out a way to—for a funding mechanism to be created that will provide steady funding for a base of scientists who are ready and able to develop a vaccine, because this—we’re over-relying on the big pharmaceutical companies. They’re not coming into this space in a big way, with a couple of exceptions. The biotechs, some of them are in it. Most of them are in it not so much for the specific vaccine, but it’s a device to accelerate their technologies. So we’ve got to figure out a mechanism to create a—fund a group of scientists working in an area where they’ll develop vaccines in the non-profit sector. We’ve had Walter Reed Army Institute of Research for years. They’ve been hit very hard. We could restore that. That would be one way. We have this great VRC, Vaccine Research Center, at the NIH, and there’s a couple of others, like ours, the University of Maryland Center for Vaccine Development, our Baylor College of Medicine, one at Texas Children’s, but we need—each one has to be bigger, and each one has to be—and we need more of them as well.

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Dr. BROWNSTEIN. I’ll just add also my thanks to the NIH, because I also wouldn’t be here without support from specifically the National Laboratory of Medicine, and their efforts to really train the next generation of data scientists in health. Specifically around—your question around vaccines, I think it’s really important to think about the comments of Dr. Murray and think about the next event, right? Of course we need to be focused on the current coronavirus, but we’re going to see likely another event, another likely coronavirus event. We saw SARS, MERS. It’s likely that we should be thinking about universal vaccines around coronaviruses, as opposed to maybe something very specific around this event, that ultimately will prepare us for the next pandemic that we’ll see in the future. I think the more that we can be thinking about those next events, and they will occur, the better off we’ll be for the next one. Mrs. FLETCHER. Thank you, Dr. Brownstein—— Dr. SELL. One—— Mrs. FLETCHER. Dr. Sell, you had a—— Dr. SELL. Yeah, I have one thing to add. So you’d asked about the ecosystem for vaccines that don’t make money, right? We have the difficulties with developing those vaccines, and then testing them, but we also—a project at our center led by Nancy Connell, we also have a problem with manufacturing those vaccines at scale, right? So we might be able to have a vaccine, but we can’t make, you know, half a billion doses, or whatever we need, quickly, in enough time to make a difference. And so I think that’s another thing, you know, we can’t just swap over the products in a manufacturing plant. That’s another area that really needs a lot of attention. Mrs. FLETCHER. Thank you. Dr. Murray, I have a few—30 seconds left. I’d love to hear your thoughts. Dr. MURRAY. I agree, again, with my colleagues, in particular with Dr. Brownstein, who was saying about the universal vaccine. I think it’s very well—a very good idea to invest in that. And, again, part of the information we would collect in the field about what types of vaccines, or what type of viruses are out there, will hopefully help inform that. I also

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wanted to add on just—I thought a little bit more about the question from Mr. Posey, and I do think that if we’re going to be looking at research, creating a one health program somewhere that we’re—because we don’t currently have a program that works in high risk areas that incorporates both human expertise and wildlife expertise, and ideally has one foot in the Federal Government, and one foot outside of the Federal Government. It would be great if such an institution were here somewhere in D.C., and perhaps a parastatal institution that’s—already exists. Mrs. FLETCHER. Thank you very much. I have gone over my time, so I will yield back. Chairman BERA. Thank you—— Mrs. FLETCHER. Thank you all. Chairman BERA. [continuing]. Mrs. Fletcher. The gentleman from Texas, Mr. Cloud, is recognized for 5 minutes. Mr. CLOUD. Thank you, Chairman, and thank you all for being here to help us address this very important topic. I appreciate the healthy discussion over some of the misinformation that’s come out sometimes with, you know, political goals in the dispersion of it. I also appreciate you educating us just really on some of the real scientific challenges in addressing a situation like this. I wanted to see, Dr. Murray, in the effort of giving good communication on this, if you can give us, kind of backtracking, it was kind of an understanding of why are doing this, where did this coronavirus come from, how is it unique, how is it spreading? Dr. MURRAY. Thank you very much. I’d be happy to do—and I think I could probably share the answer to that question as well. In terms of what we know, or—that bats, primates, and rodents are the species that are most likely to carry these viruses that transmit to humans, and—the coronas in particular, and our team has already discovered a—several other coronaviruses in China, with 98—97 and 98 percent homology to this virus, meaning—so they’re very closely related. And we also developed these trees so you can determine how closely this virus is related to the other coronas. We found some in Myanmar that are not closely related, and not likely to cause disease.

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So—and we also have behaviorists looking at what are the risks associated with bats? In a lot of countries bats provide a lot of protein, and people do eat bats. But, if you think through it, the risk might not be the person in a restaurant eating a fully cooked bat. Perhaps the risk is the women in the back who are preparing the bat without the gloves, and without the masks, that are—along with children, and then take it home. So trying to understand the cultural norms and human behavior patterns that give—that contribute to these sorts of things. A quick shoutout to OSTP (Office of Science and Technology Policy) from—because we also have a pandemic preparedness forecasting science and technology panel that looks at these sorts of things, and collectively this past year we—at Smithsonian we housed a—or a we hosted a 2 day workshop looking at the—bringing together the soft sciences and the hard sciences, the modelers who look at human behavior, and also the hard scientists that look at what the virus does. So we believe that these—that markets—wildlife markets and the wildlife trade are a really huge risk in general, and the risks are different whether you’re in Africa or Asia. Africa, animals tend to come to the market. The risk is more in bush meat trade for the folks who are there in the forests that are killing the animals, and the meat tends to come to the market already dead, whereas in Asia it’s often live animals that are at the market. So those are— to answer some of your questions about the virus, we believe that it’s a bat related virus, and that it’s—it came in close contact through this—the markets. We still have so much more to learn about this virus in particular, and these—with epidemiologists, and our human health folks as well, and so— there’s still so much we don’t know, but that’s what we know so far. I’d like to yield to any of our M.D. colleagues to see if they have something to add. Mr. CLOUD. Well, if I may, I only have two minutes left. Dr.—— Dr. MURRAY. Sorry. Mr. CLOUD. [continuing]. Hotez, if you can tell us what’s some of the challenges in addressing these treatments and vaccine, also, I’m just going to get all the questions out here. Based on your experience working with

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SARS, and Ebola, and Zika, what are some of the challenges that you’ve seen governments face in the past, what are some of the best practices we’ve learned, and what’s some of the things that we can use toward addressing this? And then if you can answer that, and if any of you want to jump in and finish the time out? Dr. HOTEZ. Yeah, two points. We need more vaccines, and trying to do this in the middle of a crisis is very difficult, right? I mean, we have one—N of one, the—what—the story with Ebola, maybe cholera vaccines in Yemen, so we want to start doing this now. And one of the other problems that I’m seeing is, you know, through NIAID and BARDA, we have incredible mechanism for supporting vaccines, so clearly the U.S. is the global leader in this. We need some of the other countries to start pitching in and help supporting global health technologies. If you look at the funding—public funding globally, you know, the U.S. is by far the No. 1, UK maybe second, the European Union, and then the bottom falls out, so we see a lot of under-achievement among the G20 countries. China’s doing very little. Japan, not much, a little bit. Korea’s starting now. I’m on a board called the Korean Right Fund with the Gates Foundation. Brazil needs to step up. You know, all the BRICS (Brazil, Russia, India, China and South Africa) countries need to step up. So the— we really need to put this on the agenda of a G20 summit to say, look, the U.S., you know, has, you know, globally taken the lead on recognizing this is a huge problem through NAID and BARDA, the other countries need to step up. This needs to be on the topic of a G20 summit. I have a book—I like to write books, so one of the books I wrote is called Blue Marble Health, which actually finds this quite interesting finding. Overwhelmingly, most of the world’s emerging and povertyrelated neglected diseases are not necessarily in the poorest, most devastated countries of Africa. It’s the G20 countries. It’s the poor living among the wealthy, including 12 million Americans that suffer from neglected tropical diseases. So we need the other G20 to show some leadership, and work with State Department and others on this. Chairman BERA. Thank you, Dr. Hotez. The gentleman from California, Mr. McNerney, is recognized for 5 minutes.

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Mr. MCNERNEY. Well, I thank the Chairman, and I thank the witnesses this morning. Very useful, informative. Dr. Sell, how can social science aid us in understanding how to stop misinformation during outbreaks? Dr. SELL. So misinformation during outbreaks is a big problem, and I think it’s a very complex problem. So social science could help us understand what the best messages are to help people understand when the rumors they’re seeing are false. So, to improve our messaging, the type of ways we’re trying to communicate with people, how to convince them of, you know, the facts, rather than to believe in these rumors. But I also think that there’s a—we need to actually develop an entire strategy here. We need to think about all the different stakeholders, right? We have tech companies, they need to be doing work. We have the public. The public—we can’t just say the public—the public should—we think the public should figure out how to determine truth from falsehoods. But we also have government, we have news media, and we have public health. We all need to think about those stakeholders, and everything they can do to deal with this problem. Mr. MCNERNEY. Is there a specific area of research that would help develop those tools? Dr. SELL. I mean, I think that looking into seeing what misinformation is out there, and then also the communications research that I do. I think that it’s looking at what kind of ways we can solve that, and the messages that are necessary, so that’s social science research. Mr. MCNERNEY. OK. Thank you. Dr. Hotez, I’m going to follow up on Ms. Fletcher’s question. How do we incentivize pharma and biotechs to prioritize vaccine development? Dr. HOTEZ. Well, it’s tough, and, you know, I know I’ve been critical of the big pharmaceutical companies today, but I also have some great— some support as well. I mean, you know, what Merck did—Merck and Company did for the Ebola vaccine is an extraordinary story, right? I mean, this—that vaccine ultimately—giving it to 200,000 people in DR Congo in the middle of a war and conflict prevented a catastrophic epidemic that would’ve dwarfed the one in West Africa, and would’ve

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destabilized the entire African continent. So we owe a real debt of gratitude to Merck, and BARDA, and the supporters that made that happen. But if you talk to some of the people at Merck offline, one of the things they’ll tell me is, look we didn’t make—Peter, we didn’t make money on this thing, we actually—in some—depending on how you crunch the numbers, we actually might have lost money because we had to pull people from moneymaking projects in order to put them on this, so it’s really a problem. You know, vaccines are expensive, and they’re expensive because of all the quality control and quality assurance that you have to put in, and all the belts and suspenders you put in to ensure safety. So I, you know, and I’m, you know, and that’s maybe one of the reasons why we’re not seeing the big pharmaceutical companies jump in this time around, because they saw, my God, look what Merck had to do in order to make this happen. So I think we have to look at creating a new type of organization, and maybe working this out in the nonprofit sector here in the United States. Mr. MCNERNEY. Thank you. Dr. Brownstein, I’m pretty excited about the HealthMap platform that you discussed. How is artificial intelligence used in public health preparedness—— Dr. BROWNSTEIN. Yeah. So—— Mr. MCNERNEY [continuing]. To prevent spreads? Dr. BROWNSTEIN. So AI is seeing a real explosion in use in healthcare. Of course we’ve seen advancements in other domains, financial services, entertainment, but of—what we see is there’s opportunities in leveraging AI with large datasets. When we’re dealing with an important event like a public health crisis, there’s a huge amount of data, a lot of information about cases, a lot of misinformation, and being able to sort through all that critical data to get important insights that we can feed to our modelers, our policymakers, even the public, that’s where this kind of—these kind of methodologies come into play. So, if you think about the earliest signs of the COVID–19 event, they are actually through this epidemic intelligence collecting tools, actually some that support the technologies that Dr. Murray was talking about.

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Combing through the web, looking for signs of mysterious illnesses that we could utilize to then pinpoint, and then communicate those to the World Health Organization, and CDC, and other organizations. But more importantly, there’s a vast amount of information globally now being transmitted about cases confirmed, suspected, on trying to understand the response, the recovery, the demographic data of these patients. That is well more capacity than the existing workforce of epidemiologists that exist on this planet, and so what we’re trying to do is augment the work of these public health practitioners through the opportunities that AI brings. So the opportunity to mine that information, organize it, and bring the situational awareness data to the forefront so it can be used effectively. Mr. MCNERNEY. OK. I’m running out of time, so I’m going to ask you for the record, not a verbal response, what the challenges are in expanding AI into this field. So I yield back. Chairman BERA. Thanks. The gentleman from Texas, Mr. Olson, is recognized for 5 minutes. Mr. OLSON. I thank the Chair, and welcome to our four expert witnesses. A special welcome to Dr. Peter Hotez. I’d like to join my Texas colleague, Mrs. Fletcher, in bragging about Dr. Hotez. My colleagues need to know this is not just a man who’s an expert in Texas. He’s a recognized expert in all of America, and globally on pandemic viruses. And that’s why you saw him all day yesterday on national cable, explaining the challenges with the COVID–19 virus. You also saw him doing that with the Ebola, with SARS, with H1N1, and also with Zika. H1N1 was very special back home. That broke out in 2009, and your institution, Texas Children’s Hospital, set up a drive-through vaccine in a parking garage almost overnight to have those vaccines deployed. So, again, thank you for being here. As Bum Phillips would say, you may not be in a class by yourself, but every class you’re in, it don’t take long to call the roll. I want to talk about—— Dr. HOTEZ. Thank you, Congressman. Mr. OLSON [continuing]. Quality treatments and future responses. First, quality treatments. Yesterday it was announced that my home county of Fort Bend was the first site in Texas to have a confirmed case of the

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COVID–19 virus. Don’t know too much. The man was 70 years old, he had traveled overseas, no confirmation if he went to China, Iran, or Italy, and he’s now quarantined in the local hospital. As Dr. Sell mentioned, a lot of people right now are living in fear that this disease is among the people of my hometown, and those fears may cause people to do something that’s not very wise, and sometimes very foolish. We’ve seen photos all across this country of towns reacting to this influenza. We’ve seen empty shelves of grocery stores. We’ve seen empty shelves of bleach. As you said, Dr. Sell, people think drinking bleach can somehow help control this virus, which is just crazy. We’ve seen empty shelves of canned foods. We see at the Home Depots, the Lowe’s, all the masks and stuff needed to protect people are getting swarmed up by people who don’t need them. And, Dr. Hotez, you brought this up yesterday on national TV, how can we make sure the required resources we have to fight back are given to the top priorities, which I think as you mentioned, are probably, first all, the families, the victim, their neighbors, the first responders, the EMS (emergency medical services) vehicles, the cops, the firefighters, and also the doctors and nurses—how can we make sure those people have the first priority to get these scarce resources? Dr. HOTEZ. So you’ve hit on it, right? I mean, that’s exactly right, and thank you for those really generous comments. We need to give our one, two, three, four top priorities of the groups that we’re going to insure, because if they go down, then everything falls apart, and things go badly very quickly. And I don’t know that we’ve really done that yet, so, I think, you know protecting our older individuals in nursing homes, because if— because we’re—we now know, from Kirkland, anytime a virus hits a community, those are the ones who are going to get hit the hardest, and the healthcare providers, and others. The other thing I’ve been saying is—regarding panic has been, look, you will have time. It’s not like you’re going to wake up tomorrow morning and find that the entire Eastern half of the United States is infected. What we’re going to see is multiple communities being affected, and that will cause a lot of concern, but you will have time in order to prepare and figure out what’s happening. And we don’t exactly know. It

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may stop there. You know, there are some who believe there may be seasonality to this virus. We don’t know that at all, because it’s a new agent. So I think it’s—the key is to stay in—our leaders need to stay in contact with the people, hold those White House briefings on a pretty regular basis, but also try not to sugarcoat, right? To be—it’s a real art to be able to give difficult information, but to do it in a way to say, we’re aware of it, here’s what we’re doing about it. And I think, you know, we’ve been through this before. You know, one of the things that I’ve noticed in the 20 years that I’ve been following pandemics, it started with anthrax in 2001, and then SARS in 2003, H1N1 2009, as you pointed out, Ebola 2014, and then we go to Zika, and now this, the same thing happens every time. It takes us a little bit of time to get our arms around it. There are always stumbles in the beginning, and a lot of that has to do with the Federal Government and the State governments have to figure out all over again how to work together, so there always seems to be that new relationship building that has to happen. And then eventually we get it right, and this will happen again. So—and that’s, I think, the other thing that we want to see is the press not piling on too much when these things happen. Mr. OLSON. Good luck with that. Dr. HOTEZ. Yeah, and—well, especially it’s occurring right during the Democratic—it’s, you know, it’s happening in the worst time possible from that sense. And to have that perspective of time, saying, look, this always happens, I mean, it’s the hardest—— Chairman BERA. Thank you, Doctor. Dr. HOTEZ [continuing]. Thing our country does. Chairman BERA. Thank you, Doctor. Mr. OLSON. Yeah, I hear the gavel banging. I have some questions for the record on stockpiling vaccines. Thank you very much. Chairman BERA. Let me recognize the gentleman from Illinois, Mr. Casten. Mr. CASTEN. Thank you, and thank you all for coming. I want to follow, if I could, a little bit on the questions Dr. Bera asked at the start about vaccine development. Dr. Hotez, thank you for clarifying that we’re

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not going to have this vaccine for a year or so. Can you just share a little bit some of the risks of bringing the vaccine to market too early? Dr. HOTEZ. Thank you for that. Yes, well, the risk is compromising safety. This, you know, the—remember what we’re doing, we’re going to be doing. We’re going to be immunizing healthy people, right, so vaccines always have a higher safety bar because you’re injecting well people. These are often not individuals who are ill, and you’re trying to accelerate some technology for compassionate use. So—and our FDA, our CBER, has one of the best track records in the world in ensuring safety, and we have one of the best monitoring systems in the world ensuring safety. I mean, we have these four systems in place, the vaccine events, adverse reporting system, we have—but—and many times people think that’s the only thing we have. We have a redundant system of four tracks that follow this. So we know how to do this. We know how to ensure that vaccines could be developed and tested safely. Don’t try to pressure FDA, CBER, into doing something that breaks with that, because, you know, if we start rolling out a vaccine too quickly, and it’s shown that a number of those individuals are getting worse because of this vaccine, which we know can happen with certain respiratory virus vaccines. We’ve seen it with RSV, we’ve seen it with—in laboratory animals with other coronavirus vaccines, then people will lose confidence, and not only confidence in coronavirus vaccines, but our whole vaccines—— Mr. CASTEN. Sure. Dr. HOTEZ [continuing]. And safety network—— Mr. CASTEN. So—— Dr. HOTEZ [continuing]. So—— Mr. CASTEN. So with a, you know, with an unvaccinated population, given that some of the early data, you know, is—seems to suggest that those who are most at risk are those—the elderly, immunocompromised, we’re not going to have a—— Dr. HOTEZ. And healthcare workers. Mr. CASTEN. Yeah. So we’re not going to have a vaccinated population. Presumably other complications that people have may be at

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risk. As you look through sort of our broader healthcare ecosystem, do you see other medications that we may be where, you know, where increasing focus on some of these non-coronavirus drugs may be the thing that is ultimately going to hurt people? Are there other places we should be looking in the ecosystem right now? Dr. HOTEZ. Well, remember, vaccines are the highest bar there is, so even though that’s going to take, you know, whatever time it is, there are other technologies out there that we could be — that’ll get deployed more quickly. I think we’ll probably have antiviral—— Mr. CASTEN. Just—— Dr. HOTEZ [continuing]. Drugs a little—— Mr. CASTEN. Sorry, I don’t—I’m asking a sort of different question, and maybe it’s my own lack of knowledge. If I already—let’s say, as an example, I’m taking immunosuppressants because I just had a liver transplant—— Dr. HOTEZ. Um-hum. Mr. CASTEN [continuing]. The—and all of a sudden I come down with coronavirus, I may not—coronavirus may not be the thing that does me in, but this other thing does. So if we look at the populations that are most at risk from getting a bad flu, are there other sort of drugs and pharmacologicals that that community is disproportionately taking that we should be concerned about, or maybe a little focus there might protect some of these folks? Dr. HOTEZ. I don’t know—I’ll have to think about that a little bit more, but you’re right. I mean, I think, you know, we don’t have — remember, this is a new virus agent, and there are differences in the U.S. and the Chinese population. We haven’t seen a lot of data of people with immunosuppressive drugs, so—— Mr. CASTEN. OK. Dr. HOTEZ [continuing]. I don’t think we really know what that—— Mr. CASTEN. Yeah, I just used that as an example. I—— Dr. HOTEZ. So people on Humira, and—I don’t—— Mr. CASTEN. Yeah. My concern is just all these people who might be needing insulin, might be needing statins, other things. Shifting with the

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little bit of time I have left, Dr. Sell, I appreciate your comments on not spreading misinformation, and just, with the little time we have left, all of us going to be back in our districts next week. We all have, you know, certain platforms that we can speak to. Given what you researched on Ebola, and without, you know, making this a political conversation, as you look at what’s going on right now, are there specific pieces of misinformation that trouble you, and if you were in our shoes, what would you love to see us saying to the country this weekend? Dr. SELL. You bring up something that’s very important, because influencers, like you, have the—one of the biggest roles in spreading the truth about the disease. That’s actually borne out by the research. So I think, when you go home to your constituents this weekend, I think people might be afraid, and I think this is a concerning disease. We can’t sugarcoat it. We have to say, this is serious, we need to think of it, and think about the ways that we can prepare. People—research has shown that people really want to know more about the actions that they can take, rather than the risks that they have to worry about. So, you know, the CDC has a lot of advice out there, wash your hands, use respiratory etiquette. I think people also want to think about how they can be prepared, how they might take care of a loved one, if a loved one is sick, but not serious enough to be in the hospital, to—and we’re limiting how many people we’re trying to take care of in hospitals, to how we might care for sick people at home, and think about, you know, stockpiling prescription meds, and things that you might need, and you don’t want to be at the store when there’s, you know, a lot of sick people or whatever. I think that actions are really what people need to hear right now. Mr. CASTEN. Thank you. I yield back. Chairman BERA. The gentleman from Ohio, Mr. Gonzalez, is recognized for 5 minutes. Mr. GONZALEZ. Thank you, Mr. Chairman, and thank you, for our witnesses. Dr. Hotez, you have a great background. I’m going to sing Dr. Sell’s praises for a moment. It’s not every day that we get an Olympic

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athlete in our midst, especially one that had a world record at one point. Do you still have it, by the way? Dr. SELL. No. Someone took it a—— Mr. GONZALEZ. Someone—OK. Dr. SELL [continuing]. Years ago. Mr. GONZALEZ. Still unbelievably impressive. I don’t think any of us have world records in our history. Could be wrong. Certainly for nothing as impressive as what you did. But of all the accomplishments and things I respect most about Dr. Sell, it’s the fact that she has my wife’s unyielding admiration and appreciation, that means the most to me, as a college teammate of yours. So I want to start by asking about the role that diagnostics play in forecasting accuracy. I just left a briefing, where it’s very obvious that we did not, and still probably do not, have the number of diagnostics available, with respect to coronavirus today. So, when it comes to your forecasting accuracy, what role does having robust diagnostics play in the process? Dr. SELL. Well, that’s a great question—and thank you very much for the introduction. Diagnostics have an incredible role to play because the way that you look for information out there about the disease determines what you’ll find, right? So if you’re only looking for people who have a travel history, you’re never going to say, we have community transmission, because every case you find will have a travel history. And so I think that being able to use rapid diagnostics, like the flu test, or these other things, is really important so that we can note those more mild cases, and we know the range of disease, and where it is. Mr. GONZALEZ. Great. Dr. BROWNSTEIN. From a modeling perspective—— Mr. GONZALEZ. Yeah. Dr. BROWNSTEIN [continuing]. Having an accurate understanding of what’s happening in the community is incredibly important, right? Because we’re essentially seeing some of the more severe cases. It might lead to overestimates of case fatality. We don’t actually know what’s happening at the community level because we don’t have the testing. So we’re going to essentially be biased in our understanding of disease, and not actually have

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a direct understanding of things like household transmission, what we’re seeing in terms of the level of spread that’s happening. So this is why having enough diagnostic capacity to do it at a population scale is so critical, and why we see incredible advances in Korea and other places. Mr. GONZALEZ. Yeah. And I think that, you know, one of the things that is troubling for a lot of folks, certainly for me, is you see different case fatality rates depending on the country, right? And my estimation of that is because we don’t know the N, and everybody’s using a disparate, you know, South Korea they’re testing all the time. It seems almost like drivethrough test kits, whereas here it’s unclear to me how many people we’ve actually tested. I don’t think it’s north of 1,000. I could be wrong on that. So that’s been a little troubling. I guess follow up question on the model piece, if we had been testing on the order of, say, South Korea, how much further along do you think we would be, and how much closer to being able to more effectively prepare and prevent a major outbreak would we be if we had the better testing capabilities? I’ll start with Dr. Sell. Dr. SELL. I’ll be quick, so the others can answer, but I think if we had better testing capabilities, I think we would have had the motivation to get moving a little bit quicker. Mr. GONZALEZ. Yeah. Dr. SELL. And—especially in places where we might see disease so that we could keep it out of those nursing homes and hospitals. So I think that’s—would’ve been helpful. Mr. GONZALEZ. Great. Dr. Brownstein. Dr. BROWNSTEIN. Yeah, exactly the same thing. The more detailed information we have on the ground, the better off we are to respond. Models are only as good as the data that we feed them, of course, and so, if we have richer information about what’s happening, we have that testing, we can understand what is happening at the community level, and think about things like social isolation, and other mitigation efforts that could slow the spread of the coronavirus. Mr. GONZALEZ. Thank you. And then, with my final minute, Dr. Sell, I want to go back to the question that Mr. Casten was asking, with

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respect to false information. Obviously, since 2014 and Ebola, the platforms that we use, the way we communicate, has changed quite a bit. Have you noticed a stark difference of any kind between how misinformation was spread in 2014 versus how it’s spread today? What sort of lessons can we learn from that? Dr. SELL. This is an opinion without an analysis behind it, but I think that the spread of misinformation has been much more rapid. We know that in some cases it’s been coordinated, and I think that it spreads across multiple platforms very quickly. We have these echo chambers, and we had echo chambers in 2014, but this information just bounces within people who have the same belief systems, and so it’s very hard to change that. Mr. GONZALEZ. OK. Thank you, and I yield back. Chairman BERA. The gentleman from Illinois, Mr. Foster, is recognized for 5 minutes. Mr. FOSTER. Thank you, Mr. Chairman, and to our witnesses. I’ve been sitting here trying to synthesize from your testimony what a coherent plan to actually, you know, do something over the next decades that would really move the ball on this, and so the first step, it seems to me, is to actually characterize the up to 1.7 million potentially transmissionable viruses, and I think there may be hope for developing technology so we can see the sort of, you know, 1.7 million sounds like a big number, but with technology development you might be able to bring the cost down. And then to potentially do things to mitigate transmission from the animal reservoirs. And, you know, there are things like gene drives, and other things. They just did—they’re talking about releasing mosquitoes that can’t transmit certain—that sort of approach might be important. And secondly, to simply identify the concerned sequences across broad classes of these. There was an example of this, actually, in my district, Argonne National Labs, where they recently solved a protein called NSP– 15, which is conservative on coronaviruses. It is apparently involved in the replication of the virus as a very attractive drug target that—actually do something that would sort of persist over a time longer than Congress’s Attention Deficit Disorder to actually, you know, stay focused on a handful

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of attractive targets, or a large number of attractive targets, and develop these for drugs, you know, both as treatments and vaccines. And here I perceive there’s a real difference, that you can potentially do things quickly for treatments, but the vaccine problem is much tougher because of the clinical trial bottleneck. I don’t know if there are any great breakthrough ideas to—so that if you have thousands of potential viruses, and everything about them understood, but you haven’t done the clinical trials on—and you identified targets, but you still need clinical trials, are there any ways to accelerate that, or any potential technologies out there? I—that seems like an unsolved problem, from your testimonies. And then, fourth, developing high volume, general purpose manufacturing that’s on standby, which is something Dr. Sell mentioned. This seems like it’s something where you can throw money at the problem. You know, if there are really general purpose technologies out there, and we, you know, there’s a lot of overlap with this—frankly, with money we’re spending on bioterror defense, and it may be that it’s the exact same equipment that you need. And so I’d be interested in—well, first off, have I missed any big parts of this? Are there significant things—I think the rapid detection is something you mentioned that’s sort of a parallel track from this. Dr. BROWNSTEIN. If I may add just one other component to this, which is this idea of a national or international service around disease forecasting, right? We’ve done this for the weather, right, like a national service for weather, where we collect data from NOAA and make predictions. That does not exist today in disease forecasting, and if there was investments to be made in addition to important pipelines around manufacturing, it would be developing a way to predict the—sort of the next coronavirus-like pandemic. Mr. FOSTER. Yes, Dr. Hotez? Dr. HOTEZ. Yeah. I think you pointed out a very good bottleneck, that, you know, that clinical testing does take time. There has been a lot of effort to apply innovation toward streamlining clinical safety testing. Sometimes we call it systems vaccinology. The idea is we can do more things in parallel, rather than sequentially. And, in fact, that was already

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started with the Ebola vaccine in DR Congo. We did a lot of things in parallel, so it really went through and got—we got information on its efficacy and its safety in record time. And I think, if it wasn’t for this particular safety signal around this immune enhancement problem, we may—we might have broken a record, because there is an appetite to figure out how to streamline vaccine safety testing, it’s just that there’s just—unique, quirky feature about coronavirus vaccines, and some other respiratory virus vaccines. So I think you will see innovation and streamlining clinical trials, I’m just not sure this is the one to do it with. Dr. SELL. I had one other addition. I think that, you know, we—when we come up with these tools, they’re interesting, and the exist out there, but we really need a way to sort of integrate them into practice, and that— so I think practice-focused research at public health agencies and the CDC is really important to making sure that we actually move research into actually making a difference on the ground. Mr. FOSTER. And have there been, you know, big studies that actually come up with, here’s the holistic plan, here’s rough budgets? You know, are—is this something where it was done 15 years ago by the National Academies, and ignored by Congress, or is that— there actually the need for, OK, let’s just sit down and, in an international context, come up with a plan that has those elements that I mentioned and others? Dr. Murray? Dr. MURRAY. Yes, if I can answer part of that? The—to answer the first part of your question, there is a group that is newly formed, the Global Virome Project, that is looking at the 1.7 million as yet unknown viruses. Their goal is to identify and characterize all of that in—much in the same way as the Human Genome Project started out, and provided a wealth of information. We have had, for the last 10 years, a global program looking at human and animal health, as well as syndromic surveillance in country, laboratory building. That’s the one I was describing that’s just in the process of shutting down now. I would suggest that this is not the time for the U.S. to be pulling out, but, if we have a program that’s doing it, if anything else, we need to

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continue and expand, and incorporate more of the type of folks we have here. And part of that program also had what Dr. Sell was working on—— Mr. FOSTER. I’m sorry, I guess I’m—— Chairman BERA. Yeah. We’re—— Mr. FOSTER [continuing]. Exceeding time here. Chairman BERA. We’re going to try to get one last question in, since they called votes on us. The gentleman from Virginia, Mr. Beyer, is recognized for five—— Mr. BEYER. Mr. Chairman, thank you very much, and thank you all so much for being here. This—incredibly important topic. And, Dr. Hotez, it’s nice to see you again, 30 years after first coming across your incredible landmark work on the hookworm vaccine, so, good luck. I want to start by submitting a letter I—yesterday supported by 60 Members of Congress sharing my concern about the ineffective White House response, the lack of a chain of command, sharing conflicting information, et cetera, so—and how we stand ready to improve it. So, if there’s no objection, Mr. Chairman? And, Dr. Sell, first, with apologies, I hate asking yes or no questions because they tend to be gotcha questions here, so please know that, time allowing, there will be time for paragraph questions later, but I’d like to make just some—a quick point, so five yes or no questions would be helpful—— Dr. SELL. OK. Mr. BEYER [continuing]. And then we’ll move. First, the World Health Organization says that the death rate from coronavirus is over 3 percent of those infected. Do you have any reason to believe that the actual figure is a fraction of 1 percent? Dr. SELL. A fraction of 1 percent? Mr. BEYER. Yeah. Dr. SELL. Yes. Mr. BEYER. OK. Thank you. Would you say that the World Health Organization statistics on the spread of the novel coronavirus are false? Dr. SELL. No. Mr. BEYER. Will we have a vaccine soon, or within a few months?

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Dr. SELL. No. Mr. BEYER. Are we likely to get a quick cure? Dr. SELL. By cure do you mean a treatment? Mr. BEYER. Well—— Dr. SELL. I have to say possibly, because there’s drug trials. Mr. BEYER. OK, great. And should Americans who have the coronavirus symptoms, or believe themselves to be sick, go to work and risk spreading the disease? Dr. SELL. No. Mr. BEYER. Would you generally agree that all those statements are false? The panel. Let me go on—the—Dr. Sell, one last question, would you say that it would endanger American lives to spread disinformation that would cause people to go to work, and potentially spread the coronavirus because the public was misled about the dangers of this deadly disease? Dr. SELL. Misleading the public about a disease is wrong. Mr. BEYER. And so the sad part here is that these statements, which most scientists—well, every scientist testifying today, would agree endanger American lives were actually made by our President to large audiences in the last 3 days. Scientists just told me that Trump’s coronavirus statements about a soon—quick vaccine, a quick cure, it’s OK to go to work, that all these things are endangering American lives. And, to be clear, the CDC advises anyone exhibiting symptoms of coronavirus, such as a fever, coughing, or shortness of breath, stay home from work, avoid public areas as much as possible, and seek medical attention. The Tuesday briefing from Vice President—was not televised. He came here and talked I think four different times. On Monday we heard reports that the CDC stopped disclosing the stats on how many Americans are being tested. At a time of high uncertainty in the face of a likely pandemic, should the American administration more transparent or less? Maybe Dr. Hotez? Or Dr. Sell? Dr. SELL. I’ll just be quick. The administration should be transparent. They should be clear about what they know. They should tell the truth, be clear about what they don’t know, what they’re doing to try to find out

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those missing pieces of information, and be clear about what the course is, and what information might change that course. Mr. BEYER. Great. Thank you. Dr. Brownstein, we’ve heard a claim that focusing on testing is no longer needed once the disease has spread, you know, that it’s in the community, that testing is moot. We’ve also heard the test—sentiment from many that they’d rather over-test folks than under-test folks. Do you think that testing will still be valuable when it starts to spread into a community? Dr. BROWNSTEIN. Yeah. I think it’s important to actually have an accurate picture, because the dynamic of this virus is going to change as it moves from community to community, and understanding the impact that it’s having at scale is going to be critical. And so, just like we do this for the influenza on a seasonal basis, where we test for flu to understand what the underlying illness is, the idea of doing this at scale for coronavirus makes a lot of sense. Mr. BEYER. Dr. Hotez, you’ve done so much work on vaccines over the decades, and you testified earlier quite well about it. What’s the best the American people can hope for, in terms of a quick vaccine, or a soon vaccine, or—— Dr. HOTEZ. Well, you know, I think it’s really important to remember that vaccines are not quick, and that has a lot to do with vaccine confidence in the United States, because, as you know, we have a very aggressive antivaccine movement here in this country, and, as of the last couple of years, it’s affecting public health, right? Measles came back in 2019 because of the anti-vaccine movement. Historically, when we’ve had measles epidemics, it peaks now, late winter, early spring, so we may be battling two epidemics. We still have 16,000 Americans who’ve died of flu, including 100 kids most who were not vaccinated. So I think it’s really important not to tell the American public that we will have a quick vaccine, because that’s not how it works. We have to reassure the public that we don’t give out vaccines unless they’re thoroughly tested, and they are the most thoroughly tested pharmaceuticals we have for safety.

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Mr. BEYER. And, Mr. Chairman, as I yield back, I just want to thank Dr. Hotez too for leading the fight against the anti-vaxxers, and that misinformation. Chairman BERA. Thank you, Mr. Beyer. Before we bring this hearing to a close, I want to thank all of our witnesses for testifying before the Committee today. The record will remain open for 2 weeks for additional statements from Members, and for any additional questions the Committee may ask of the witnesses. With that, the witnesses are excused, and this hearing is now adjourned. [Whereupon, at 10:45 a.m., the Subcommittee was adjourned.]

APPENDIX I: ANSWERS TO POST-HEARING QUESTIONS Responses by Dr. Suzan Murray

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either through natural infection and recovery or human intervention such as vaccination. In the case of COVID-19, now that it has reached a pandemic level, it is certainly here to stay. Coronaviruses like SARS- CoV-2 have genomes that are subject to genetic mutation, both prior to and during disease emergence, which is why this viral family currently is and has been under intense study as a pandemic threat. The concept of One Health recognizes the interconnection and interdependence between human, animal and environmental health. In the Unites States and globally, health professions, scientific disciplines, sectors and countries working together in collaboration to implement this One Health concept is recognized as critical to address complex global challenges. The interconnections between human, animal and wildlife populations are complex and dynamic. We know if that it takes an equally complex collaborative approach across health disciplines, soft and hard sciences, sectors and governments to address disease outbreaks as they

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occur, and preferentially identify them in wildlife species to prevent spillover events from occurring. Early detection before a spillover event occurs is the ideal, and helps local health, animal and wildlife authorities mitigate the factors and risks that lead to disease emergence in human populations. Globalization of trade and travel, interdependence of humans on each other for information, goods, food and resources, increasing urbanization, climate change and many other factors are known to directly contribute to disease outbreaks like COVID-19. There is much we have learned from previous outbreaks that is already being applied in response to the COVID-19 pandemic and just as importantly, to future epidemics and pandemics we know are coming. We know that prevention is more economically feasible, efficient, and a shared ethical imperative versus the horrific costs to life, liberty and economies that we've already seen with COVID-19. Advancements in the detection of novel pathogens show the most efficient way to identify, respond to, and contain an outbreak is through coordinated wildlife and human surveillance. Our best statistical models estimate there are 1.7 million unknown viruses across all species globally, roughly half of which may have the potential to infect people. These numbers give us pause, as we know that even a small percentage of these viruses will lead to new pandemics. While many agencies work on pandemic prevention as of now, there are no coordinated programs to work in high risk regions to identify these unknown viruses,get their genetic sequences into our labs, and identify ways to reduce risk of them emerging. Our best defense against disease emergence and spread is investment and coordination of a One Health approach to research and education.

Submitted by: Representative Bill Foster (IL-11) 1) Is there a correlation between the death rate of COVID-19 and the percentage of the population with existing pulmonary and respiratory issues? I say this because I assume that the percentage of the population in China with these issues is far less than the

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Committee on Science, Space, and Technology United States, and the death rate in the U.S would potentially be far-greater based on this premise.

Response: This is an important question, and while I would defer more specific guidance on this question to my human physician counterparts, we do know that people who are immunocompromised, those with preexisting respiratory disease, and I or chronic diseases are at increased risk of more serious illness and death. Epidemiological information available to date shows that the effective death rate is much higher in older individuals due in part to higher rates of chronic diseases (including pulmonary and respiratory diseases and symptoms) in older populations, both here in the United States and around the world.

Submitted by: Representative Paul Tonka (NY-20) Dr. Murray, climate change presents a clear and present danger to human health. Human activities are driving unprecedented changes in the Earth's climate and causing the emergence of novel viruses that spread from animals to humans in regions where dense human populations and biodiversity interact. Land use change, for example, is one of the leading causes of disease emergence. 1) Can you elaborate on how current and future land use, like deforestation, mining, and oil extraction, are fundamentally changing our environment and facilitating increased contact between animals and humans? 2) What research and development actions are needed to better detect pathogens and prevent the transmission of diseases from animals to humans? 3) Analyzing the effects of climate change on the emergence of infectious diseases will require collaboration between physicians, climatologists, biologists, and social scientists. Are we prepared to do that?

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Response: Prospective extraction of natural resources, such as oil, minerals and timber, leads to the expansion of human activities into ecologically intact natural habitats. In the tropics, where these habitats are characterized by an abundance of wildlife, people who work in extractive industries often reply on wild animals and livestock as sources of protein, creating interfaces at which novel pathogens can pass between wildlife, livestock and humans. Supply chains associated with extractive industries link these communities to larger urban areas, providing a route by which a novel pathogen that has already infected people, or is present in wildlife and livestock products, can enter the broader human population. By these mechanisms, human activities in previously pristine environments can bring people into contact with microbial diversity harbored in wildlife, for which humans are naive. Through expanding existing efforts, scientists and policymakers can be successful at identifying high-risk interfaces at which pathogens are more likely to cross from animals into humans. Priority research focus areas should remain on increased understanding of the processes by which pathogens spillover from wildlife into livestock and humans at these interfaces, the risk factors for this occurring, and how these risks can be mitigated. Another priority area is understanding the natural history of zoonotic pathogens in wildlife- particularly how perturbations to wildlife populations as a result of human exploitation, land-use and climate change could alter the dynamics of pathogen transmission between wildlife hosts, and facilitate cross-species transmission. Engaging extractive industries in partnering in and investing in both human and animal surveillance programs may provide an interesting option Development actions of highest priority are to 1) enhance capacity for cost-effective surveillance activities targeting high risk pathogens, in at-risk human and animal populations; 2) improve integration and sharing of data between state-run animal and human health agencies; 3) improve dynamic forecasting of disease outbreaks, so that surveillance activities are responsive to changing climatic,

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Committee on Science, Space, and Technology environmental and demographic conditions. This will require more accurate forecasting of the future state of ecological, epidemiological and economic systems within which diseases are emerging.

Within the framework of One Health, some progress has been made towards mobilizing the transdisciplinary research required to study climate change and health. However, more can and should be done. Of particular importance is support at state and federal levels to establish a One Health research center or institution, a physical manifestation that brings together health disciplines, along with other hard and soft sciences and directly applies scientific research and knowledge to the prevention of and response to disease epidemics. Such an institution would ensure research infrastructure is in place which encourages human and animal health professionals, biologists, climatologists and social scientists to work together and share ideas and solutions within the same research environment. Of equal importance is establishing funding mechanisms for One Health research to ensure availability of dedicated large-scale funding for projects that seek to apply a systems approach to studying the impact of climate change on disease transmission at local and regional scales. Such grants would provide the framework for the formation of successful interdisciplinary research groups, and the outputs needed to address this important issue.

Submitted by: Representative Troy Balderson (OH-12) 1) I would like each of you to weigh in on this if you could. One of my biggest concerns is the person-to-person transmission and community spread of these types of viruses. As we see COVID-19 cases arise in different parts of the country, what can be done at the community level to prevent a city or county-wide outbreak from occurring, beyond the standard personal hygiene recommendations from the CDC?

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2) I am sure you are all aware of some of the abnormalities that surround the virus' original outbreak in China, for example the possible downplaying of the numbers by the Chinese government. How would you say the lack of transparency about COVID-19 by China and others like Iran has impacted the CDC's and the WHO's abilities to respond to the heath emergency at hand? Response: We know that when a new disease emerges in a human population one of the most difficult and important roles of human physicians and pathologists is to recognize the clinical signs and symptoms in their patients as being different from a known disease presentation. Through careful and purposeful tracking of clinical signs and symptoms health care professionals around the world utilize case definitions to track disease across individuals and populations. Many respiratory diseases, like influenza and COVID-19, have similar signs and symptoms in individual patients which complicates identification of a new disease, or different presentation in an individual patient of a known disease. The World Health Organization has established International Health Regulations where-by countries that sign on agree to report epidemiological data based on known case definitions for particular diseases of concern, along with syndromic data that may include broader clinical signs and symptoms of disease, as well as events of unknown sources or causes that may constitute public health emergencies of international concern. Given such complexity of what diseases are known and that they often have different presentations in individuals, across geographies, and across health care systems it is very challenging for even the most advanced health systems in the world to reach 100% accuracy in disease status and reporting. As a recognized global leader, the United States plays a critical role in encouraging sharing of accurate and timely information on health and disease. Creating, implementing and facilitating working examples to disseminate scientific knowledge, developing platforms for sharing factual and timely information, and critically evaluating information sourced across a broad and complex information networks are some important areas for focus and leadership on a global stage.

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Responses by Dr. John Brownstein Submitted by Representative Ami Bera (CA-07) Dr. Brownstein, a successful public health response to an outbreak of a new infection requires high-quality data to detect the pathogen, characterize the risk, project the outbraak trajectory, and stop it. Leveraging existing technology and supporting research and development of new technology can help the United States better address today's pandemic and future outbreaks. On the Technology 1) How can advanced analytics be used to characterize the risk of an outbreak and project the likely trajectory of an outbreak? What kind of data would be needed to use these analytics in order to effectively support response activities by public health agencies? 2) How can we best combine new technologies with tried-and-true disease surveillance techniques to detect and predict the spread of infectious diseases? New technologies should be used in partnership with traditional disease surveillance. The use of AI and crowdsourcing in this field provides ample opportunities for early detection of disease or quickly identifying changes in patterns. The benefit to new technology is it's speed. Traditional disease surveillance is very thorough, but because of this, it can be quite time consuming and burdensome if there are not enough resources to support traditional efforts. By using new technologies as a first line of action, you are able to be alerted sooner, and therefore you can be pointed to where traditional surveillance activities could be most beneficial. Early detection on our tools can help concentrate activities or give pinpoints to where we can start. Innovative disease surveillance tools are seen as complementary to traditional surveillance by highlighting sensitivity over specificity whereas traditional surveillance is more specific. Additionally,

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new technologies can shed light on areas with decreased access to care, highlighting the potential for hot-spots during outbreaks. On the Implementation 3) What kind of legal barriers prohibit technology companies' use of regularly collected data to help public health agencies track transmission data from individuals with confirmed or suspected COVID-19? One of the largest legal barriers we face is related to privacy and confidentiality concerns. Personal health information is protected in this country, so it can be difficult to get details at the individual level that are vital for understanding transmission dynamics. For example, aggregated forms of demographic information (e.g., age, sex, race, location) is shared, however, we are often unable to make a connection between them. Individually each factor is important, but knowing their interconnectedness would make our work even more comprehensive. 4) How should the U.S. government navigate those legal barriers in partnership with the information technology industry, while ensuring the patient's data is properly safeguarded?? 5) How can the Federal government support technology and innovation in the public health surveillance sector? The Federal government can support technology and innovation in public health surveillance through sustained investment of personnel, funding and acceptance of new technology. We need this support even when there are no major outbreaks so that we are able to prepare for when we face a new emerging infectious disease threat. There is a need for a new discipline in disease forecasting akin to weather. The US government could invest in an agency that would provide foundational support for this effort.

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Submitted by Representative Bill Foster (IL-11) 1) Is there a correlation between the death rate of COVID-19 and the percentage of the population with existing pulmonary and respiratory issues? I say this because I assume that the percentage of the population in China with these issues is far less than the United States, and the death rate in the U.S. would potentially be far-greater based on this premise. People with pre-existing pulmonary and respiratory diseases have an increased risk of severe infection if they contract COVID-19. However, there are many other aspects that contribute to death rates, including (but not limited to) other health conditions, access to care and age that also would contribute to death rate. So we can't make any assumptions based on pre-existing lung disease alone. One of the most important factors in disease modeling is determining how contagious a disease is. Put simply, the RO [R-nought} is the average number of people in a population that a single infected person wi/1 spread the disease to over the course of their infection. However this is a complex and constantly changing factor. 2) Can you tell us the current RO for COVID-19 and how that number may change as time passes? As of April, the most commonly accepted estimate for the ROof COVID-19 is between 2-3. This number may change over time as we grow in our understanding of transmission dynamics, factors that may increase or decrease susceptibility and immunity. 3) Can you elaborate on certain factors that increase the effectiveness and usefulness of infectious disease modeling as well as areas that present challenges for modeling efforts?

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Infectious disease modeling can only be as good as the data that is available. Having quality data, both in completeness and detail, increases the effectiveness and usefulness of infectious disease modeling. However, the biggest burden for innovative infectious disease modelling is access to reliable data, whether it be granular line-list data or information on population mobility. More sustained funding of these efforts would allow for collaborations between data sources, modellers, and health officials to be established and maintained. Additionally, health officials and policymakers need to know about the available tools ahead of crises. Sustained funding would allow for greater collaboration and marketing of these tools. 4) In your opinion, what are the key research and development gaps for infectious disease modeling, and how can those gaps be addressed? The biggest gap is the ability to maintain these efforts outside of outbreaks, in a sustainable way. So much of this work is done via grant or philanthropic funding, which has been generally only released during crises. Sustainable funding would make these initiatives time to grow and prepare stakeholders, via training and preparedness.

Submitted by: Representative Jerry McNerney (CA-09) 1) What are some of the challenges of expanding AI in this area? Official buy-in and acceptance of this kind of data is critical to be able to expand AI in infectious disease research. Also AI depends on the quality of the data. As they say garbage in is garbage out, we need improved surveillance information (high quality clinical data streams) to improve our forecasting efforts. 2) You've noticed the success that the Flu Near You crowdsourcing tool, which you helped create, has had as a symptom surveillance

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Committee on Science, Space, and Technology tool. Can you speak more to how systems like these may serve to fill in gaps of information and provide early signals of disease impacts at a community level

Crowdsourcing tools, like Flu Near You (FNY), connect with users more frequently than someone may visit a physician. With regular checkins, you are able to detect even the slightest changes in reporting patterns because people might not always seek care depending on factors like healthcare access and illness severity. With this kind of tool, we are able to capture mild illnesses that do not require hospitalizations, which fills in gaps of understanding. For example, a person may report having a cough on the first day of experiencing a cough, but might not seek professional care for over a week if the cough persists. When reported into a crowdsourced tool like FNY, we capture that data sooner, which allows for an earlier signal that disease may be occurring.

Submitted by Representative Haley Stevens (MI-11) Dr. Brownstein, much of your research focuses on using innovative disease surveillance platforms for detecting and monitoring outbreaks and mitigating their spread. HealthMap, a project you and your colleagues at Boston Children's Hospital founded in 2006, is a freely available, automated website that delivers data and alerts on emerging infectious diseases. HealthMap is used by major public health organizations including WHO, CDC, and HHS, to facilitate real-time surveillance. 1) Can you elaborate on both HealthMap and other cutting-edge tools can help us understand and manage diseases? HealthMap allows us to understand and manage diseases by capturing disease signals that might not be as quickly recognized as more traditional methods. It taps into data that is not traditionally used for surveillance (for example, news and social media) to enhance our understanding of diseases. With AI tools we are able to monitor every disease that poses a potential threat in a unified way. The speed of the tools can help direct official containment efforts to the areas most critically in need.

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2) How could innovative tools increase collaboration between decision-makers during an outbreak? Aggregating data from a variety of different sources can provide visibility into information otherwise siloed within different organizations or working groups. Innovative tools can increase collaboration between decision-makers during an outbreak by providing data as supplementary sentinel signals to traditional surveillance efforts. These tools provide a data landscape of the current situation, which would promote preemptive collaboration ahead of an outbreak, which would build buy-in across groups. 3) How can the Federal government support research and development into new modeling technologies? The Federal government can reiterate the need for sustained, rather than acute, support for these endeavors. The more funding spent on these tools will mean less funding that would be needed acutely during a public health response and public health officials will be better prepared. Additionally, funding spent on detecting the next outbreak and keeping outbreaks controlled will mean less impact on the global and domestic economy.

Submitted by Representative Troy Balderson (OH-12) 1) I would like each of you to weigh in on this if you could. One of my biggest concerns is the person-to-person transmission and community spread of these types of viruses. As we see COVID-19 cases arise in different parts of the country, what can be done at the community level to prevent a city or county-wide outbreak from occurring, beyond the standard personal hygiene recommendations from the CDC?

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There are 3 critical pieces at the city and county level that can help minimize the spread of infectious diseases: 1) Have ample testing abilities: You need to be able to test people for COVID-19 in order to estimate and understand the spread within a community. Without sufficient testing, there is a higher probability that a virus will circulate. This means having enough tests to be able to test seemingly healthy individuals in addition to those presenting with symptoms. 2) Contact tracing: Wihen you have a confirmed case of a disease like COVID-19, it is critical to identify all of the potential people that the single case may have infected. Contact tracing allows all potential new cases to be aware of their exposure as well as giving them the opportunity to self-isolate and be tested to help reduce spread in a community. 3) Enforcing social distancing procedures: Social distancing procedures are very important when dealing with preventing community-level transmission. Reduced contact with other people is the key to minimizing risk within a community. At the city or county level, this could include, reduced business hours, reduced occupancy and stay-at-home orders. 2) I am sure you are all aware of some of the abnormalities that surround the virus' original outbreak in China, for example the possible downplaying of the numbers by the Chinese government How would you say that the lack of transparency about COVID-19 by China and others like Iran has impacted the CDC's and WHO's abilities to respond to the health emergency at hand. Especially when dealing with a novel outbreak, such as COVID-19, where we have not seen human infection before, having as much information as quickly as possible is vital to accurately prepare and understand risk. In order to predict transmission rates, we need to understand (1) how many people have been infected, (2) how cases are connected and (3) how long after potential exposure did they become ilL

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Without this understanding, we cannot accurately determine global risk and prepare for widespread outbreaks.

Responses by Dr. Peter Hotez

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Responses by Dr. Tara Kirk Sell

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APPENDIX II: ADDITIONAL MATERIAL FOR THE RECORD

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Articles Submitted by Representative Ed Perlmutter

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THE CORONAVIRUS CRISIS: FAKE FACTS ARE FLYING ABOUT CORONAVIRUS. NOW THERE'S A PLAN TO DEBUNK THEM February 21, 202011:33 AM ET Heard on All Things Considered

Figure 8. Malaka Gharib.

Figure 9.

The World Health Organization is sharing social media posts to debunk widely circulated rumors about coronavirus cures. Facebook/Screengrab by NPR

Updated on March 9th at Noon EST The coronavirus outbreak has sparked what the World Health Organization is calling an "infodemic" — an overwhelming amount of

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information on social media and websites. Some of it's accurate. And some is downright untrue. The false statements range from a conspiracy theory that the virus is a man-made bioweapon to the claim that more than 100,000 have died from the disease (as of this week, there are more than 3800 reported fatalities world wide). WHO is fighting back. In early January, a few weeks after China reported the first cases, the U.N. agency launched a pilot program to make sure the facts about the newly identified virus are communicated to the public. The project is called EPI-WIN — short for WHO Information Network for Epidemics. "We need a vaccine against misinformation," said Dr. Mike Ryan, head of WHO's health emergencies program, at a WHO briefing on the virus in February. Article continues after sponsor message

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Subscribe to Goats and Soda's newsletter for a weekly update on the outbreak. While this is not the first health crisis that has been characterized by online misinformation — it happened with Ebola, for example — researchers are especially concerned because this outbreak is centered in China. The world's most populous country has the largest market of Internet users globally: 21% of the world's 3.8 billion Internet users are in China.

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And fake news can spread quickly online. A 2018 study from Massachusetts Institute of Technology found that "false news spreads more rapidly on the social network Twitter than real news does." The reason, say the researchers, may be that the untrue statements inspire strong feelings such as fear, disgust and surprise. This dynamic could cause fake coronavirus cures and treatments to fan out widely on social media — and as a result, worsen the impact of the outbreak, says Bhaskar Chakravorti, dean of global business at the Fletcher School at Tufts University. Over the past decade, he has been tracking the effect of digital technology on issues such as global health and economic development. The rumors offer remedies that have no basis in science. One untrue statement suggests that rubbing sesame oil on the skin will block the coronavirus. If segments of the public turn to false treatments rather than follow the advice of trusted sources for avoiding illness (like frequent hand-washing with soap and water), it could cause "the disease to travel further and faster than it ordinarily would have," says Chakravorti. There could be a political agenda behind the fake coronavirus news as well. Countries that are antagonistic toward China could try to hijack the conversation in hopes of creating chaos and eroding trust in the authorities, says Dr. Margaret Bourdeaux, research director for Harvard Belfer Center's Security and Global Health Project. "Disinformation that specifically targets your health system or your leaders who are trying to manage an emergency is a way of destroying, undermining, disrupting your health system," she says. In the instance of vaccines, Russian bots have been identified as fueling skepticism about the effectiveness of vaccination for childhood diseases in the U.S. The World Health Organization's EPI-WIN team believes that the countermeasure for misinformation and disinformation is simply to tell the truth.

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Figure 10. Life Kit. Fake News: How to Spot Misinformation

It works rapidly to debunk unjustified medical claims on social media. In a series of bright blue graphics posted on Instagram, EPI-WIN states categorically that neither sesame oil nor breathing in the smoke of fire or fireworks will kill the new coronavirus. Part of this truth-telling strategy involves enlisting large-scale employers. The approach, says Melinda Frost, an officer on the EPI-WIN team, is based on the idea that employers are the most trusted institution in society, a finding reflected in a 2020 study on global trust from the public relations firm Edelman: "People tend to trust their employers more than they trust several other sources of information." Over the past several weeks, Frost and her team have been organizing rounds of conference calls with representatives from Fortune 500 companies and other multinational corporations in sectors such as health, travel and tourism, food and agriculture, and business.

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The company representatives share questions that their employees might have about the coronavirus outbreak — for example, is it safe to go to conferences? The EPI-WIN team gathers the frequently asked questions, has their experts answer them within a few days, and then sends the responses back to the companies to distribute in internal newsletters and other communication. Because the information is coming from their employer, says Frost, the hope is that people will be more likely to believe what they hear and pass the information on to their family and community. Bourdeaux at Harvard calls this approach a "smart move." It borrows from "advertising techniques from the 1950s," she adds. "They're establishing the narrative before anybody else can. They are going on offense, saying, 'Here are the facts.' " WHO is also collaborating with tech giants like Google, Twitter, Facebook, Pinterest and TikTok to limit the spread of harmful rumors. It's pursuing a similar tactic with Chinese digital companies such as Baidu, Tencent and Weibo. "We are asking them to filter out false information and promote accurate information from credible sources like WHO, CDC [the U.S. Centers for Disease Control and Prevention] and others. And we thank them for their efforts so far," said Dr. Tedros Adhanom Ghebreyesus, director-general of WHO, in a briefing earlier this month. Twitter, for example, now actively bumps up credible sources such as WHO and the CDC in search results for the term "coronavirus." "We're also taking proactive action on any coordinated attempts to undermine the public conversation on this critical issue," wrote a Twitter spokesperson in a statement to NPR. Facebook (which is one of NPR's financial sponsors) is implementing similar strategies. "When people search for information related to the virus on Facebook, we will surface an educational pop-up with credible information in multiple languages and countries," wrote a Facebook spokesperson in a statement to NPR.

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"We've connected people to regional health ministries in several countries, for example: The Center for Health Protection in Hong Kong, Taiwan Center for Disease Control in Taiwan, the Republic of the Philippines Department of Health in the Philippines, the Ministry of Health in Italy." Facebook has taken the extra step of deploying fact-checkers to remove content with false claims or conspiracy theories about the outbreak. Kang-Xing Jin, head of health at Facebook, wrote in a statement about one such rumor that it has eliminated from its platform: that drinking bleach cures coronavirus. Chakravorti applauds WHO's coordination with the digital companies — but says he's particularly impressed with Facebook's efforts. "This is a radical departure from Facebook's past record, including its controversial insistence on permitting false political ads," he wrote in an op-ed in Bloomberg News. Still, there is no silver bullet to fighting health misinformation. It has become "very, very difficult to fight effectively," says Chakravorti of Tufts University. A post making a false claim about coronavirus can just "jump platforms," he says. "So you might have Facebook taking down a post, but then the post finds its way on Twitter, then it jumps from Twitter to YouTube." In addition to efforts by WHO and other organizations, individuals are doing their part. On Wednesday, The Lancet published a statement from 27 public health scientists addressing rumors that the coronavirus had been engineered in a Wuhan lab: "We stand together to strongly condemn conspiracy theories suggesting that COVID-19 does not have a natural origin .... Conspiracy theories do nothing but create fear, rumors and prejudice that jeopardize our global collaboration in the fight against this virus."

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Dr. Deliang Tang, a molecular epidemiologist at Columbia University's Mailman School of Public Health, says his friends from medical school and his research colleagues in China find it difficult to trust Chinese health authorities, especially after police reprimanded the eight Chinese doctors who warned others about a pneumonia-like disease in December. As a result, Tang's network in China has been looking to him and others in the scientific community to share information. Since the outbreak began, Tang says he has been answering "30 to 50 questions a night." Many want to fact-check rumors or learn about clinical trials for a potential cure. "My real work starts at 7 p.m.," he says — morning in China.

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Figure 11. Global Health. Coronavirus World Map: Tracking the Spread of the Outbreak

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Figure 12. Global Health. Ironic Twist: In Spring, Trump Halted Research Key To COVID-19 Drug He's Now Taken.

Figure 13. Global Health. New U.N. Tracker Looks At How Countries' COVID-19 Responses Are Helping Women.

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Figure 14. Global Health. Coronavirus FAQ: Folks At Trump-Biden Debate Were Scanned For Fever. Is That Helpful?

Figure 15. All Things Considered. Kids And Superspreaders Are Driving COVID-19 Cases In India, Huge Study Finds.

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Figure 16. Opinion. A Nigerian Finds Hard Truths — And Hope — In Netflix Series On Nigeria. Popular on NPR.org.

Figure 17. Politics. 4 Takeaways from the Mike Pence-Kamala Harris Vice Presidential Debate.

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Figure 18. Politics. Duckworth: Block Supreme Court Pick Who Thinks 'My Daughters Shouldn't Even Exist'.

Figure 19. Politics. Trump Halts Coronavirus Relief Talks Until After The Election.

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Figure 20. Politics. Government Scientist Adds To Whistle-Blower Complaint And Quits NIH.

Figure 21. Politics. Tracker: Key Trump Contacts Who Have Tested Positive For The Coronavirus.

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Figure 22. Law. White Gun-Wielding St. Louis Couple Reportedly Indicted By Grand Jury. NPR Editors' Picks.

Figure 23. Music. 'SNL' Nixes Morgan Wallen Appearance After Singer Violates COVID-19 Safety Protocols.

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Figure 24. National. Philadelphia Sues For The Right To Control Guns.

Figure 25. World. 'To Protect Myself And My Family': Saudi Critics Abroad Fear Long Reach Of The Crown.

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Figure 26. Music News. A Voice Of Iran, Master Singer Mohammad Reza Shajarian, Has Died.

Figure 27. Book Reviews. Phil Klay's New 'Missionaries' Is An Ambitious Novel Of Ideas.

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Figure 28. Games & Humor. Rubik's Cube Inventor Writes A New Book: It's Full Of Twists And Turns.

THE WASHINGTON POST: CORONAVIRUS RUMORS AND CHAOS IN ALABAMA POINT TO BIG PROBLEMS AS U.S. SEEKS TO CONTAIN VIRUS Todd Frankel 3/3/2020 Coronavirus rumors and chaos in Alabama point to big problems as U.S. seeks to contain virus ANNISTON, Ala. — Not long before local leaders decided, in the words of one of them, that federal health officials “didn’t know what they were doing" with their plan to quarantine novel coronavirus patients in town, a doctor here set out in a biohazard suit to stage a one-man protest along the highway with a sign. “The virus has arrived. Are you ready?” it asked. As Democrats slam Trump over coronavirus, Pence and Azar go on the defense

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The town didn’t think it was. Residents already were unnerved by strange stories posted on Facebook and shared via text messages about helicopters secretly flying in sick patients, that the virus was grown in a Chinese lab, that someone — either the media or the government — was lying to them about what was really going on. The quarantine plan hastily hatched by the federal Department of Health and Human Services was soon scrapped by President Trump, who faced intense pushback from Alabama’s congressional delegation, led by Republican Rep. Mike D. Rogers. Americans evacuated after falling ill aboard the Diamond Princess cruise ship in Japan would not be coming to Anniston, a town of 22,000 people in north-central Alabama, after all. They would remain in the same Texas and California sites where they were taken after leaving the cruise ship. What happened here over the past week illustrates how poor planning by federal health officials and a rumor mill fueled by social media, polarized politics and a lack of clear communication can undermine public confidence in the response to the novel coronavirus, which causes the disease named covid-19. The rapidly spreading virus has rattled economies worldwide in recent weeks and caused the deaths of more than 2,900 people, mostly in China. The panic and problems that burned through Anniston also provided a preview of what could unfold in other communities, as the spread of the virus is considered by health experts to be inevitable. “Their little plan sketched out in D.C. was not thought out,” said Michael Barton, director of the emergency management agency in Calhoun County, where Anniston is located. As local officials learned more, Barton added, “We knew then —” “We were in trouble,” said Tim Hodges, chairman of the county commission. In Anniston, local leaders were stunned to discover serious problems with the federal government’s plan for dealing with patients infected with the virus — starting with how the patients would get to Alabama, according to interviews with county and city officials, along with business leaders who dealt with the federal response.

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Figure 29. A dormitory, front center, in Anniston, Ala., where the federal government intended to house coronavirus patients. (Elijah Nouvelage for The Washington Post).

“I was shocked,” Anniston Mayor Jack Draper said. “I was shocked by the lack of planning. I was shocked by the manner in which it was presented to us.” Two HHS officials — Darcie Johnston, director of intergovernmental affairs, and Kevin Yeskey, principal deputy assistant secretary for preparedness and response — said in a Feb. 23 meeting with local officials that the patients would be flown from California to the Fort McClellan Army Airfield in Anniston, according to multiple local officials. The airfield was closed when the Army base was shuttered in 1999. Local officials said they told the HHS officials during the meeting the runway was in bad shape. “The more we talked,” Hodges said, “the more holes we found.” The HHS plan also called for housing coronavirus patients at the Center for Domestic Preparedness, a FEMA facility on the old Army base and one of several redevelopment projects at the sprawling outpost. The center has several brick dormitory buildings — behind tall black fencing — where federal officials planned for the patients to live. Federal

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officials even picked out the building they wanted to use for the first arrivals: Dorm No. 28, local officials said. A team of federal health workers would care for the patients and U.S. marshals would keep them from leaving the quarantine, local officials said they were told. The dorms normally house emergency responders from around the country. But the center doesn’t have any special capabilities for handling infectious diseases, local officials said. The center is used for training. It has isolation hospital rooms — located in a former Army hospital building — but they are mostly just props, with fake equipment and light switches that exist only as paint on walls. Meanwhile, federal officials never contacted the town’s hospital, Regional Medical Center, about handling covid-19 patients, said Louis Bass, the hospital’s chief executive.

Figure 30. The Center for Domestic Preparedness is near a dormitory where the federal government intended to house quarantined passengers from the Diamond Princess cruise ship. (Elijah Nouvelage for The Washington Post).

Yet HHS officials said in a statement released to the public Feb. 22 that patients who become seriously ill would be sent to “pre-identified hospitals for medical care.” “We were surprised,” Bass said.

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The hospital does have eight negative-pressure isolation rooms, but patients with serious complications would need to be sent to a larger institution, such as Emory University Hospital in Atlanta, 90 miles away, Bass said.

Figure 31. Tim Hodges is chairman of the Calhoun County Commission. (Elijah Nouvelage for The Washington Post).

Emory University Hospital did not respond to a question about whether it was told about the HHS plan. A federal contract for a local ambulance service was secured at the last moment, after HHS had already issued a statement about its plan for Anniston. Details on how to handle other tasks — including patients’ laundry and food — seemed unfinished. The preparations for bringing patients to Anniston were handled partly by Caliburn International, a government contractor that previously provided emergency medical services to federal agencies, according to interviews and documents reviewed by The Washington Post. Former Trump chief of staff John F. Kelly joined the firm based in Reston, Va., as a board member last year. Caliburn is the parent company of Comprehensive Health Services, which has come under scrutiny for its operation of medical services at a detention site for migrant children.

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A Caliburn spokeswoman referred questions about the Anniston operations to the Centers for Disease Control and Prevention, which did not immediately respond to a request for comment. HHS, through its Office of the Assistant Secretary for Preparedness and Response, responded to The Post’s questions about its Anniston operations with a statement noting the office’s staff members “have a longstanding relationship” with the disaster preparedness center and were familiar with its capabilities. The statement also said the federal agency “was considering the facility as a contingency location” and decided during discussions with local officials that “the site would not actually be needed.” It was Trump who finally canceled the planned quarantine in Anniston on Feb. 23, according to tweets from Rogers and Sen. Richard C. Shelby (R-Ala.) that referred to their conversations with the president. The news arrived as people attended an emergency meeting of the Calhoun County Commission. Cheers broke out. “I guess in our culture today a tweet is considered official,” Barton said. Anniston has plenty of experience dealing with unwelcome threats — and learning to live with them. It was for years home to the nation’s chemical weapons stockpile, including sarin and mustard gas. Later, it was the location of a chemical weapons incinerator, where those munitions were carefully destroyed. The town also deals with the toxic legacy of a former Monsanto plant that for decades polluted the soil and water with PCBs, which were banned in the 1970s amid health concerns. The pollution resulted in a $700 million settlement for 20,000 residents in 2003. But the novel coronavirus posed a different kind of challenge. Fear that the HHS plan was flawed gave new energy to already circulating rumors and wild theories about the virus. Residents didn’t know whom to believe. Trump had said without evidence that CNN and MSNBC were exaggerating the threat. Rush Limbaugh was on the radio saying it was no worse than the regular flu. Facebook posts claimed the outbreak had been foreshadowed by a 1981

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Dean Koontz book. And the idea the virus could have been created in a Chinese biochemical lab was floated widely, including by Sen. Tom Cotton (R-Ark.). The whirlwind caught the attention of Michael Kline, a urologist in Anniston. “I don’t think anyone knows what’s going on,” he said. So on the weekend of Feb. 22-23, Kline dressed up in a blue biohazard suit with his “the virus has arrived” sign. He stood along the highway and waved to passing vehicles. He wanted to drum up opposition to allowing infected patients in Anniston. But even after the plan was abandoned, Kline said he still wasn’t certain patients weren’t being housed at the old Army base.

Figure 32. © Todd Frankel/The Washington Post Urologist Michael Kline protests a plan to quarantine patients infected with covid-19 at a facility in Anniston.

Rumors of black helicopters ferrying infected patients to the training center at night were rampant. The local Home Depot sold out of painting and sanding face masks. Hodges, the commissioner, said he heard often from worried residents. But helicopters were common in the area because

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of a nearby Army depot and National Guard training center. Only now they were nefarious. Other people talked about mysterious vans driving along county roads. Hodges and Draper held emergency news conferences and meetings to try to lessen the panic. But those meetings also allowed for additional rumors to flourish during public comment periods. A commission meeting included one resident tying the coronavirus to a 1992 United Nations document about climate change. “That’s how long this has been going on,” he said. “The public is going crazy,” said Bobby Foster, a business owner who spoke at the meeting and asked the commissioners to try harder to distribute accurate information. Glen Ray, president of the local NAACP, talked about the virus at a Sunday service at Rising Star United Methodist Church on Feb. 23 to try to calm people’s worries. But he was also dismayed that one of the county commissioners wore a red “Make America Great Again” hat to an emergency meeting about the virus. “It’s not about Donald Trump,” Ray said later. “A virus is not going to just jump on a Democrat. So at times like this, we need to be coming together. No time for politics.” Anniston’s flirtation with the dreaded virus did have one positive effect, officials said. It made them realize they need to prepare — that the virus could come without warning and they shouldn’t rely on outsiders alone for expertise. Barton, the emergency management director, helped create a county infectious disease task force. It has already had its first meeting. The focus is not solely on the coronavirus. It will handle the flu and whatever other viruses pop up in the future. The public’s interest in the virus hasn’t faded, either. Barton gave a talk Thursday to a lunchtime meeting of a civic organization, the Exchange Club. It had been planned months ago but he decided to talk about the aborted plan to bring infected patients to town. People peppered Barton with questions about why federal health officials had ever considered the disaster training facility and how much

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emergency food they should keep at home. They wanted to know how to avoid getting sick. Barton suggested hand-washing and keeping a safe distance from sick people. As he talked, a lady reached into her purse, squeezed some alcohol sanitizer on her hands and passed the bottle around the table. Emma Brown and Beth Reinhard contributed.

Editors' Pick|40,146 views|Mar 1, 2020,11:22pm EST

FEARS OF THE COVID-19 CORONAVIRUS PROVIDE MORE OPPORTUNITY FOR MISINFORMATION ABOUT ‘MIRACLE CURES’

Figure 33. Nina Shapiro. Contributor. Healthcare. Dispelling health myths, fads, exaggerations and misconceptions.

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Figure 34. Herbal remedies. Getty.

Unless you live in a cave or have literally had your head in the sand these past few weeks, you are well aware that fears about the novel coronavirus, or COVID-19, are rampant. While the earliest cases of the illness originated in Wuhan, China, it has since spread to six continents, fifty countries, and is now a bi-coastal disease in the United States. The first U.S. death due to COVID-19 infection occurred in the state of Washington, followed two days later by a second death in the same state. Within days of it being seen primarily in west coast states, cases of the illness arose in Rhode Island and New York. While most physicians and health organizations, including the Centers for Disease Control (CDC), have been advising to be cautious but not to panic, many find that panic is the only option, as it seems every time one refreshes a browser, reports of new states, new countries, and new deaths pop up. Both brick and mortar as well as online stores have run out of water, masks (despite the fact that the CDC does not recommend use of masks for protection, unless you’re a healthcare worker caring for an infected individual), and apparently ramen noodles. Perhaps we’re all headed back to the college dorms to perfect our ramen noodle cooking skills? Much worse than panicking over COVID-19 is taking the alternative medicine/herbal remedy route. The National Institutes of Health (NIH) has a subsection entitled the National Center for Complementary and

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Integrative Health. Even this subsection states (in bold): There is no scientific evidence that any of these alternative remedies can prevent or cure the illness caused by this virus. It follows with: “In fact, some of them may not be safe to consume.” The latter statement refers in particular to herbs found in traditional Chinese medicine (TCM). A toxicology study published in 2015 found some of these herbs to contain pharmaceutical agents including warfarin (a blood thinner commonly known as coumadin), dexamethasone (steroids), and paracetamol (pain killers). In addition, heavy metals such as arsenic, lead, and cadmium were found. Some remedies even contained DNA of a snow leopard. Cute.

Figure 35. Snow Leopard. Getty.

False claims abound regarding prevention and treatment of COVID-19 infections, and seem to spread nearly as fast as the disease itself. Just a few that have been circulating include:

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Dexamethasone: Here Are The Side Effects Of The Covid-19 Coronavirus Treatment That Trump Received Trump’s Doctor Announces The President Has Antibodies Against The Covid-19 Coronavirus, But Trump Probably Didn’t Make Them Humana: Moving Doctors From Fee-For-Service Cut Medicare Costs 19%

“Immune boosters will ward off COVID-19 infections” “High dose Vitamin C will prevent it” PROMOTED AWS Infrastructure Solutions BRANDVOICE | Paid Program Modernizing Your Enterprise With AWS Outposts ServiceNow BRANDVOICE | Paid Program 3 Ways To Deliver Effortless Customer Experiences Salesforce BRANDVOICE | Paid Program End The Dreaded The 4-Hour Service Window “Oregano Oil Proves Effective Against Coronavirus” “Diet modification, including avoiding spicy foods, cold drinks, milkshakes, or ice cream will prevent the infection.” “Drinking hydrogen peroxide will kill the virus” A big fat “no” to all of the above, especially the bit about the ice cream. Sadly, even some physicians are sharing inaccurate recommendations regarding COVID-19. One pediatrician begins by providing sound information and recommendations on her site regarding incidence of the illness, methods of prevention, and signs to look for that should trigger concern. She then goes on to recommend elderberry, vitamin C, frankincense, and bone broth. Yes, there are disclosures that there is no hard data on any of this, but these remedies, especially when there are none to date that are available, will draw in any captive audience. While most of this stuff is harmless, it’s a waste of money and it will not treat or prevent any viral infection, let alone one caused by COVID-19.

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Figure 36.

Frankincense, an aromatic resin used in incense and perfumes. (Photo by FlowerPhotos/Universal ... [+] Universal Images Group via Getty Images Wellness influencers are recommending high-dose vitamin infusions, including vitamins A, C, and D, to prevent and treat coronavirus. There is no evidence that these can help. In fact, using extremely high doses of vitamins can lead to kidney and liver problems. Using too much vitamin A during pregnancy can lead to fetal abnormalities. Besides it being wise to avoid claims for herbal remedies, steer clear of any scams that appear on social media, an email, a website link offering a “miracle cure,” claiming testimonials about conspiracy theories, offering “secret vaccines not released by the government,” or asking for money for fake fundraising efforts. And remember, “all natural” has nothing to do with being safe or effective. It’s a marketing term, and a poor one at that. The CDC is continually updating its site regarding information about COVID-19. This will provide you the most accurate information, including information regarding travel advisories, risk assessment, prevention strategies, updates on testing, and treatments. Frankincense somehow didn’t make it to their site.

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I wrote a book dispelling health myths called HYPE: A Doctor's Guide to Medical Myths, Exaggerated Claims and Bad Advice-- How to Tell What's Real and What's Not, to stop.

ARGONNE NATIONAL LABORATORY Press Release | Argonne National Laboratory New coronavirus protein reveals drug target Northwestern University March 2, 2020 A potential drug target has been identified in a newly mapped protein of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID19). A potential drug target has been identified in a newly mapped protein of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID19). The structure was solved by a team including the University of Chicago (U of C), the U.S. Department of Energy’s (DOE) Argonne National Laboratory, Northwestern University Feinberg School of Medicine and the University of California, Riverside School of Medicine (UCR). The scientists said their findings suggest drugs that had previously been in development to treat the earlier SARS outbreak could now be developed as effective drugs against COVID-19. The protein Nsp15 from Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is 89% identical to the protein from the earlier outbreak of SARS-CoV. Studies published in 2010 on SARS-CoV revealed that inhibition of Nsp15 can slow viral replication. This suggests drugs designed to target Nsp15 could be developed as effective drugs against COVID-19. “The newly mapped protein, called Nsp15, is conserved among coronaviruses and is essential in their lifecycle and virulence. Initially, Nsp15 was thought to directly participate in viral replication, but more recently, it was proposed to help the virus replicate possibly by interfering with the host’s immune response.” — Andrzej Joachimiak

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Figure 37. Structure of the Nsp 15 hexamer. (Image by Northwestern University.)

The structure of Nsp15 will be released to the scientific community on March 4 on the RSCB Protein Data Bank. This new structure was solved by the group of Andrzej Joachimiak, Distinguished Fellow of Argonne, the University of Chicago professor and director of the Structural Biology Center at Argonne’s Advanced Photon Source (APS), a DOE Office of Science user facility in conjunction with the Center for Structural Genomics of Infectious Diseases. Dr. Joachimiak is a co-director of the center. Karla Satchell, principal investigator for the Center for Structural Genomics of Infectious Diseases and professor of microbiologyimmunology at Northwestern, leads this international team of scientists investigating the structure of the SARS CoV-2 virus to understand how to stop it from replicating. The initial genome analysis and design of constructs for protein synthesis were performed by the bioinformatic group of Adam Godzik, a professor of biomedical sciences in the UCR. “The newly mapped protein, called Nsp15, is conserved among coronaviruses and is essential in their lifecycle and virulence. Initially, Nsp15 was thought to directly participate in viral replication, but more recently, it was proposed to help the virus replicate possibly by interfering with the host’s immune response,” said Joachimiak.

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Mapping a 3D protein structure of the virus, also called solving the structure, allows scientists to figure out how to interfere in the pathogen’s replication in human cells. Satchell said, “The Nsp15 protein has been investigated in SARS as a novel target for new drug development, but that never went very far because the SARS epidemic went away, and all new drug development ended. Some inhibitors were identified but never developed into drugs. The inhibitors that were developed for SARS now could be tested against this protein.” Rapid upsurge and proliferation of SARS-CoV-2 raised questions about how this virus could became so much more transmissible as compared to the SARS and MERS coronaviruses. The scientists are mapping the proteins to address this issue. “While the SARS-CoV-2 is very similar to the SARS virus that caused epidemics in 2003, new structures shed light on the small, but potentially important differences between the two viruses that contribute to the different patterns in the spread and severity of the diseases they cause,” Godzik said. Northwestern is the lead site for the international center that comprises eight institutions, including U of C and UCR. The center has committed resources across all eight sites since the news of the new coronavirus was made public in January. The structure of Nsp15 is the first structure solved by the center. The Center for Structural Genomics of Infectious Diseases is funded as a contract from the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health (NIH), in part to serve as a response site for structure biology in the event of an unexpected infectious disease outbreak. SARS-CoV-2 is responsible for the current outbreak of COVID-19. Over the past two months, the pathogen infected more than 80,000 people and caused at least 2,700 deaths. Although currently mainly concentrated in China, the virus is spreading worldwide and has been found in 46 countries (www.trackcorona.live). Millions of people are being quarantined, and the epidemic has impacted the world economy. There is

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no existing drug for this disease, but various treatment options, for example utilizing medicines effective in other viral ailments, are being attempted. Satchell, Joachimiak and Godzik — along with the entire center team — will map the structure of some of the 28 proteins in the virus in order to see where drugs can throw a chemical monkey wrench into its machinery. The proteins are folded globular structures with precisely defined function and their “active sites” can be targeted with chemical compounds. The first step is to clone and express the genes of the virus proteins and grow them as protein crystals in miniature ice cube-like trays. The consortium includes nine labs across eight institutions that will participate in this effort. Viewing these proteins down to the arrangement of their atoms requires an intense X-ray beam. Thus, once the crystals are grown, the center scientists image them using the APS’ extremely bright light source in a process called X-ray crystallography. Data for structure determination were collected at Structural Biology Beamlines funded by DOE Office of Biological and Environmental Research. Satchell and her team are well prepared for the challenges that come with developing drugs to fight the virus. The Center for Structural Genomics of Infectious Diseases, established in 2007, has mapped more than a thousand parts of lethal bacteria and viruses in three dimensions, exposing an intimate chemical portrait of diseases. This view offers scientists a window into the bacteria or virus’ vulnerabilities enables them to create drugs to disable it or vaccines to prevent it. This study has been funded by contract HHSN272201700060C from the National Institute of Allergy and Infectious Diseases of NIH. Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of

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Science. The U.S. Department of Energy’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit https://energy.gov/science. Tags: Protein Science, Structural Biology, X-Ray Science and Technology, Diseases, Coronavirus, COVID-19

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In: Understanding the Spread … Editor: Andrew J. Hinerman

ISBN: 978-1-53618-892-9 © 2021 Nova Science Publishers, Inc.

Chapter 2

INFECTIOUS DISEASE MODELING: OPPORTUNITIES TO IMPROVE COORDINATION AND ENSURE REPRODUCIBILITY United States Government Accountability Office

ABBREVIATIONS ASPR Response CDC DRC Ebola EOC FDA 

Office of the Assistant Secretary for Preparedness and Centers for Disease Control and Prevention Democratic Republic of the Congo Ebola virus disease Emergency Operations Center Food and Drug Administration

This is an edited, reformatted and augmented version of United States Government Accountability Office; Report to Congressional Requesters; Accessible Version, Publication No. GAO-20-372, dated May 2020.

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HHS NIH NSTC PHEMCE Enterprise Zika

Department of Health and Human Services National Institutes of Health National Science and Technology Council Public Health Emergency Medical Countermeasures Zika virus disease

WHY GAO DID THIS STUDY Outbreaks of infectious diseases—such as Ebola, Zika, and pandemic influenza—have raised concerns from Congress about how federal agencies use modeling to, among other things, predict disease distribution and potential impacts. In general, a model is a representation of reality expressed through mathematical or logical relationships. Models of infectious diseases can help decision makers set policies for disease control and may help to allocate resources. GAO was asked to review federal modeling for selected infectious diseases. This chapter examines (1) the extent to which HHS used models to inform policy, planning, and resource allocation for public health decisions; (2) the extent to which HHS coordinated modeling efforts; (3) steps HHS generally takes to assess model development and performance; and (4) the extent to which HHS has addressed challenges related to modeling. GAO reviewed documents and interviewed HHS officials, state officials, and subject matter experts. GAO identified practices commonly used to assess infectious disease model performance and reviewed 10 selected modeling efforts to see if they followed these practices.

WHAT GAO RECOMMENDS GAO recommends that HHS (1) develop a way to routinely monitor, evaluate, and report on modeling coordination efforts across multiple

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agencies and (2) direct CDC to establish guidelines to ensure full reproducibility of its models. HHS agreed with GAO’s recommendations.

WHAT GAO FOUND Within the Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention (CDC) and the Office of the Assistant Secretary for Preparedness and Response (ASPR) used models to inform decision-making during and after outbreaks of Ebola, Zika, and pandemic influenza. These agencies’ modeling efforts informed public health planning, outbreak response, and, to a limited extent, resource allocation. Four CDC centers perform modeling. HHS agencies reported using multiple mechanisms to coordinate modeling efforts across agencies, but they do not routinely monitor, evaluate, or report on the extent and success of coordination. Consequently, they risk missing opportunities to identify and address modeling challenges—such as communicating learly, and obtaining adequate data and resources—before and during an outbreak. As a result, agencies may be limiting their ability to identify improvements in those and other areas. Further, there is potential for overlap and duplication of cross-agency modeling efforts, which could lead to inefficiencies. CDC and ASPR generally developed and assessed their models in accordance with four steps GAO identified as commonly-recognized modeling practices: (1) communication between modeler and decision maker, (2) model description, (3) verification, and (4) validation. However, for four of the 10 models reviewed, CDC did not provide all details needed to reproduce model results, a key step that lets other scientists confirm those results. GAO found that CDC’s guidelines and policy do not address reproducibility of models or their code. This is inconsistent with HHS guidelines and may jeopardize the reliability of CDC’s research. This chapter also identifies several modeling-related challenges, along with steps agencies have taken to address them.

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United States Government Accountability Office May 13, 2020 The Honorable Frank Pallone Jr. Chairman The Honorable Greg Walden Republican Leader Committee on Energy and Commerce House of Representatives The Honorable Diana DeGette Chairwoman Subcommittee on Oversight and Investigations Committee on Energy and Commerce House of Representatives

Today’s globalized economy and transportation systems allow infectious diseases to spread more rapidly than ever. Notable outbreaks include novel coronavirus beginning in 2019, Zika virus disease (Zika) in 2015, Ebola virus disease (Ebola) in 2014, and H1N1 pandemic influenza in 2009. Disease outbreaks can cause catastrophic harm to the United States, disrupt economic and social systems, and kill, sicken, and traumatize people on a massive scale. For example, approximately 1 billion people worldwide get sick annually from zoonotic pathogens— pathogens that can spread from animals to humans—of which, approximately 15 million people die. Such outbreaks are on the rise. The latest example is the novel coronavirus disease which had, as of May 6, 2020, caused approximately 250,000 deaths worldwide and sickened approximately 3,600,000 people.1 In the United States, the virus had caused 1

These numbers represent case counts as provided to the World Health Organization on May 6, 2020. According to the World Health Organization, they are likely an underestimation of the true number of cases because of differences in testing strategies, definitions, reporting practices, and other factors between countries, territories, and areas. According to the Centers for Disease Control and Prevention (CDC), the exact number of novel coronavirus disease illnesses, hospitalizations, and deaths is unknown for a variety of reasons: it can

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approximately 63,000 deaths, and sickened approximately 1,200,000 people. The situation has heightened U.S. attention to potential future infectious disease threats and raised questions about the nation’s preparedness and response capabilities. It has also raised concerns among some members of Congress about how federal agencies predict the spread of emerging infectious diseases, in particular through the use of modeling. A model is a simplified representation of reality expressed through mathematical or logical relationships. Modeling is widely used in fields as diverse as engineering, finance, meteorology, and wildlife management. In public health, infectious disease modeling can help decision makers by predicting the social and economic effects of an intervention and informing spending for preparedness and response, among other things. It can answer public health questions that other methods cannot, whether for practical, ethical, or financial reasons.2 However, because models simplify reality, they may give misleading answers if the underlying data or assumptions are flawed or not fully understood by decision makers. Further, some real-world systems can be difficult to model because of their inherent complexity, scale, or randomness. For these and other reasons, researchers must carefully design, interpret, and communicate the results of models that may be used to support public health decisions. Understanding where and when infectious disease outbreaks may occur can provide information—in near real time—to decision makers who help set disease control policies and allocate resources. You asked us to examine how federal agencies have used models to inform decision-making in recent infectious disease outbreaks, and the limitations and challenges in developing and using models. This chapter examines (1) the extent to which the Department of Health and Human

2

cause mild illness, symptoms might not appear immediately, there are delays in reporting and testing, not everyone who is infected gets tested or seeks medical care, and there may be differences in how states and territories confirm numbers in their jurisdictions. The novel coronavirus pandemic began in the United States near the end of our review period; as such, we do not discuss it in this chapter. Modeling can help to address ethical concerns. For example, modeling can help to design a vaccine trial that can help minimize the number of cases of disease while also fulfilling ethical requirements. Additionally, it can help with financial issues, such as reducing the costs of conducting a large-scale vaccine trial.

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Services (HHS) has developed or used models to inform public health planning, policy, and resource allocation for Ebola, Zika, and pandemic influenza; (2) the extent to which HHS coordinated its modeling efforts for selected infectious diseases; and (3) steps HHS took to develop and assess the performance of its models for the selected infectious diseases and steps it applied to a selection of infectious disease models. It also (4) describes the extent to which HHS has addressed challenges related to modeling for selected infectious diseases. In our review, we focused on HHS because of its leadership in scientific and technical issues related to infectious disease modeling, role in infectious disease outbreak preparedness and response activities, and use of infectious disease modeling for policy and regulatory activities. Within HHS, we identified four agencies—the Centers for Disease Control and Prevention (CDC), Office of the Assistant Secretary for Preparedness and Response (ASPR),3 National Institutes of Health (NIH), and Food and Drug Administration (FDA)—that may develop or use infectious disease models. It is important that these agencies coordinate with one another and with other relevant external entities to avoid the overlap and duplication of modeling efforts across agencies and to share new ideas and advances in modeling that might lead to new insights. We focused on three infectious diseases in our review: Ebola, Zika, and pandemic influenza. We selected these diseases based on their inclusion on the National Institute of Allergy and Infectious Diseases’ Emerging Infectious Diseases/Pathogens list and consulted with agency officials and five infectious disease modeling experts for input on the selection of diseases in our review.4 We selected the experts based on our background research and input from agency

3

Subject to the authority of the Secretary, the Assistant Secretary for Preparedness and Response has duties related to public health emergency preparedness and response, biodefense, medical countermeasures, and other relevant topics. 42 U.S.C. § 300hh–10. 4 The National Institute of Allergy and Infectious Diseases is one of NIH’s 27 Institutes and Centers that conducts and supports basic and applied research to better understand, treat, and ultimately prevent infectious, immunologic, and allergic diseases. The National Institute of Allergy and Infectious Diseases’ pathogen priority list is periodically reviewed and is subject to revision in conjunction with federal partners, including the U.S. Department of Homeland Security, which determines threat assessments, and CDC, which is responsible for responding to emerging pathogen threats in the United States.

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officials (additional details on expert selection methodology can be found in appendix I). To examine the extent to which HHS has conducted modeling to inform public health planning, policy, and resource allocations for selected infectious diseases: 

 

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We interviewed agency personnel, including agency officials and staff who develop and use models, referred to here as “modelers,” and reviewed agency documents and reports to determine how or why the agencies develop or fund models; determine the types of models used and the questions they are addressing; or obtain a general description and specific examples of how these agencies use models to inform planning, policy, and resource allocation.5 We interviewed NIH officials about funding for research related to modeling for the selected diseases. We interviewed officials from five state health departments— selected based on a review of a CDC draft report on model usage, on the level of influenza activity that states experienced, and geographic variation by U.S. region—about their experiences using CDC-developed modeling tools for influenza response. For context on and examples of the types of modeling that CDC and ASPR conducted, we reviewed documents CDC and ASPR officials provided to us or cited in our interviews. (For a bibliography of models reviewed, see appendix II.) We did not include FDA and NIH in this portion of the review, because FDA has a limited role in modeling, and NIH funds, rather than conducts, modeling.6

To examine the extent to which HHS agencies coordinated their modeling efforts for the selected infectious diseases: 5

We asked agency officials questions about how models inform budget decisions. In response to these questions, agency officials said that models are not used to inform budgets, but that they may inform resource allocation decisions, which may or may not inform budgets. 6 FDA had limited modeling activity related specifically to the blood donor bank for Zika virus, while NIH funds modeling outside the agency.

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We interviewed agency officials and reviewed documents related to coordination and collaboration, including memoranda of understanding between agencies, to identify the nature and extent of coordination and collaboration across HHS agencies that conduct or fund modeling. We compared these actions to six of the eight leading collaboration practices we identified in our prior work based on their relevance to the coordination efforts we reviewed (see appendix I).7 In this chapter, and in our past work, we define coordination broadly as any joint activity that is intended to produce more public value than could be produced when organizations act alone.

To examine steps HHS took to develop and assess the performance of models for selected diseases and the steps it applied to a selection of infectious disease models: 

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7

We identified steps that infectious disease modelers generally consider when developing and assessing the performance of models from a synthesis of information gathered from interviews with agency officials, interviews with additional relevant experts, and reviews of documents. From these sources, we also gathered information on how these assessments may impact the use of models for public health decision- making. We reviewed information regarding steps taken to develop and assess the performance of models, for a non-probability sample of models in published papers or memos, including seven models prepared by CDC (two each for Ebola and Zika, and three for pandemic influenza); and three prepared by ASPR (one for each disease).8 We compared the steps taken in the development and

We excluded two leading practices from our review related to reinforcing agency accountability and reinforcing individual accountability for collaborative efforts. GAO, Results-Oriented Government: Practices That Can Help Enhance and Sustain Collaboration among Federal Agencies, GAO-06-15 (Washington, D.C.: Oct. 21, 2005). 8 For Ebola and Zika, we focused on review of selected papers or memos produced since 2014 in order to capture the period following the 2014-2016 Ebola and 2015-2016 Zika outbreaks.

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assessment of the performance of these models to the commonlyconsidered steps we identified as described above and followed up with agencies to confirm our determinations and gather information on why some steps were not taken. To describe the extent to which HHS has addressed challenges related to modeling for selected infectious diseases, we took the following steps: 

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We interviewed selected experts regarding modeling-related challenges. We also interviewed agency officials, including modelers, and selected experts, regarding challenges and limitations related to modeling; steps they’ve taken to address the challenges; and whether these challenges can be addressed or are ongoing. We reviewed documents and reports from agencies and other sources such as the National Science and Technology Council report, “Towards Epidemic Prediction: Federal Efforts and Opportunities in Outbreak Modeling” to identify challenges related to modeling and steps taken or recommended, if any, to alleviate these challenges.9

We conducted this performance audit from May 2018 to May 2020 in accordance with generally accepted government auditing standards. Those standards require that we plan and perform the audit to obtain sufficient, appropriate evidence to provide a reasonable basis for our findings and conclusions based on our audit objectives. We believe that the evidence obtained provides a reasonable basis for our findings and conclusions based on our audit objectives.

9

For pandemic influenza, we focused on papers and memos produced since 2009, when the H1N1 pandemic occurred in the United States. Pandemic Prediction and Forecasting Science and Technology Working Group of the National Science and Technology Council, Towards Epidemic Prediction: Federal Efforts and Opportunities in Outbreak Modeling (Washington, D.C.: December 2016).

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BACKGROUND Public Health Agency Roles in Infectious Disease Outbreaks and Response In the United States, HHS is the lead federal agency responsible for public health. Its responsibilities include preparing for, mitigating, responding to, and recovering from public health emergencies.10 Within HHS, ASPR and CDC prepare for and respond to infectious disease outbreaks. ASPR leads and coordinates national preparedness and response to outbreaks in the United States. It also coordinates and supports advanced research and development, manufacturing, and procurement and deployment of medical countermeasures, such as vaccines, drugs, therapies, and diagnostic tools that can be used in the event of a potential public health emergency to protect the public from harm. CDC monitors and responds to outbreaks by, among other things, studying the link between infection and health; monitoring and reporting cases of infection; and providing guidance to the public, travelers, and health care providers. During public health emergencies, CDC may operate an Emergency Operations Center (EOC) for monitoring and coordinating its response to emergencies—including infectious disease outbreaks of Ebola, Zika, and pandemic influenza—in the United States and abroad. 10

The U.S. Department of Agriculture has a lead role for incident management during an animal disease incident affecting domestic livestock or poultry. It conducts both infectious disease and economic modeling. Other agencies are also involved in planning or responding to infectious disease threats. For example, the Department of Transportation has a responsibility to guide preparedness for the U.S. aviation system to respond to such diseases. See GAO, Air Travel and Communicable Diseases: Comprehensive Federal Plan Needed for U.S. Aviation System’s Preparedness, GAO-16-127 (Washington, D.C.: Dec. 16, 2015). The Department of Homeland Security is responsible for domestic incident management and serves as the lead for interagency coordination and planning for emergency response. It assists in providing information to emergency management officials and provides enforcement of international quarantines through the Department of Homeland Security/U.S. Coast Guard, U.S. Customs and Border Protection, and U.S. Immigration and Customs Enforcement. See GAO, Defense Civil Support: DOD, HHS, and DHS Should Use Existing Coordination Mechanisms to Improve Their Pandemic Preparedness, GAO-17150 (Washington, D.C.: Feb. 10, 2017).

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The EOC staff helps with directing specific incident operations; acquiring, coordinating, and delivering resources to incident sites; and sharing incident information with the public. Other agencies perform additional work related to infectious diseases. For example, FDA monitors and protects the blood supply, and NIH makes grant awards that support research related to diseases and modeling.11 ASPR, CDC, and FDA have different approaches to modeling.12 In the cases of Zika, Ebola, and pandemic influenza, CDC and ASPR are two key agencies that conduct federal infectious disease modeling efforts. As of February 2020, ASPR had a centralized modeling unit staffed by about nine people, who are a mix of federal and contract employees, according to ASPR officials. At CDC, however, modeling is decentralized and integrated into the individual centers that make up the agency.13 Some staff work full time on modeling, while others spend part of their time on other tasks. In addition, some of CDC’s modeling efforts are conducted externally. According to CDC, approximately 70 staff members participated in modeling studies, as of October 2018.14 Of those staff, CDC’s Health Economics and Modeling Unit employed about 10 modelers who have worked on Ebola and other diseases. For Zika, CDC officials responding to Zika said most modeling work was done by one modeler in CDC’s Division of Vector-Borne Diseases, a part of the National Center

11

FDA establishes standards and is responsible for identifying and responding to potential threats to blood safety or supply. FDA promulgates and enforces standards for blood collection and the manufacturing of blood products. Of our selected diseases, FDA officials said the agency has only modeled for Zika. NIH funds and carries out basic and applied research to better understand diseases and related technology. 12 According to NIH officials, NIH supports extramural modeling through its partners, universities, and other organizations. One institute within NIH, the Fogarty International Center, models internally on a case-by-case basis, and modelers generate the questions and topics themselves. NIH officials explained that their modeling is for basic research and should not be used for making policy or other decisions. 13 “Centers” refers collectively to CDC’s centers, institute, and offices. 14 Modeling is conducted within four CDC Centers and is organized differently in each Center. According to CDC, the number of modelers should be regarded as an estimate, because many of the people who perform CDC modeling activities self-identify as epidemiologists, statisticians, or economists. Further, some may model part-time in order to address questions in their areas of expertise.

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for Emerging and Zoonotic Infectious Diseases.15 CDC influenza officials said influenza modeling is conducted by six or seven members of CDC’s Influenza Division.16 Agency infectious disease modeling activities are not limited to Ebola, Zika, or pandemic influenza. Agency efforts to protect the nation from disasters and emergencies can be organized into two elements: preparedness and response. Infectious disease modeling is one tool used to inform a wide range of decisions related to outbreak preparedness and in response to an outbreak. In the context of infectious disease outbreaks, ASPR and CDC perform work on preparedness and response. For example, ASPR leads the Public Health Emergency Medical Countermeasures Enterprise (PHEMCE), an interagency group that helps develop medical countermeasures—FDAregulated products including drugs, or devices that may be used in the event of a potential public health emergency to protect the public from harm.17 CDC may activate its EOC to assist with the response during an outbreak.18 For example, during the 2014-2016 West Africa Ebola outbreak, CDC activated its EOC in July 2014 to help coordinate activities. CDC personnel were deployed to West Africa to assist with response efforts, including surveillance, data management, and laboratory testing.

15

CDC officials responding to Zika said this Zika modeler was supported by one to three shortterm interns or graduate fellows. During the response, according to these officials, additional modelers from around CDC were brought in to help model. CDC is made up of centers, institutes, and offices, which we collectively refer to as centers. Such centers can be made up of units known as divisions, and divisions may contain branches. 16 While this audit focuses on pandemic influenza, CDC modelers and officials said that they use seasonal influenza modeling to prepare for a pandemic and that seasonal models can be used in a pandemic. Therefore, we are discussing seasonal modeling efforts in our review of pandemic influenza modeling. 17 The PHEMCE was established by HHS in 2006. It is responsible for providing recommendations to the Secretary of HHS on medical countermeasure priorities, and development and procurement activities. HHS, Office of Public Health Emergency Preparedness; Statement of Organization, Functions, and Delegations of Authority, 71 Fed. Reg. 38403 (July 6, 2006). 18 An EOC is a physical location where responders, including federal and state/territorial responders, and nongovernmental responders, can meet to coordinate information and resources to support incident management during a response.

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Infectious Disease Outbreaks

Source: CDC and WHO. | GAO-20-372. aAccording to WHO, a Public Health Emergency of International Concern is an event which constitutes a public health risk to other countries through the international spread of disease and potentially requires a coordinated international response. bThis patient later died. Two health care workers who cared for the patient tested positive for Ebola, both recovered. c Seven other people were cared for in the U.S. after being exposed to the virus and becoming ill in West Africa. Six recovered, one died. dThere are 66 cases and 49 deaths. Ebola was first identified in the DRC in 1976. eCDC estimates this outbreak resulted in over 28,600 total cases of Ebola and 11,325 deaths. This was the largest Ebola outbreak recorded. fThere are eight suspected cases, including four deaths. gThere are 54 total cases and 33 deaths. hSince June 12, 2019, no additional cases have been reported in Uganda. iThis is the DRC’s largest Ebola outbreak, and the second largest Ebola outbreak recorded. Figure 1. Timeline of Ebola outbreaks since 2014.

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Since the 1980’s, emerging infectious diseases have resulted in more recurrent disease outbreaks, causing an increasing number of human infections. Emerging infectious diseases have at least one of the following characteristics: they are newly recognized, have emerged in new areas, are newly affecting many more individuals, or have developed new attributes. Some of these diseases—including Ebola and Zika—are zoonotic pathogens, meaning they spread from animals to humans.19 Zoonotic pathogens can be carried from an animal to a human by another animal, such as a mosquito, chicken, or bat, which is known as a vector. Such pathogens sicken approximately 1 billion people annually.

Ebola According to the World Health Organization, Ebola causes an acute, serious illness, which is often fatal if untreated. Ebola is introduced into human populations through close contact with the blood and other bodily fluids of infected animals. Humans spread Ebola through direct contact with the bodily fluids of infected individuals or objects contaminated with these fluids. Ebola symptoms include fever, muscle pain, vomiting, diarrhea, impaired kidney and liver functioning, and, in some cases, internal and external bleeding. There have been five Ebola outbreaks since 2014, including the 2014-2016 West Africa outbreak which caused more than 28,600 cases and 11,325 deaths.20 Since 2018, there has been an

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Strains of zoonotic influenza that have caused human infections or that have the potential to cause such infections are described as zoonotic influenza viruses of public health concern. They are considered to be “of concern” because if the virus gains the ability to spread efficiently among humans, it could cause a global pandemic. The primary sources for influenza A viruses are birds and swine, but they can infect other animal species, including cats, dogs, and ferrets. Influenza B viruses only circulate widely among humans. The 20142016 Ebola, 2015-2016 Zika, and 2009 H1N1 pandemic influenza outbreaks are examples of emerging infectious disease outbreaks. For the purposes of this chapter, we will refer to these outbreaks as infectious disease outbreaks, and these diseases as infectious diseases. During the 2014-2016 Ebola epidemic, 11 people were treated for the virus in the United States. The majority of the cases were people who were exposed to the virus and became ill while in West Africa. During this outbreak, CDC helped to coordinate technical assistance and disease control activities with partners and deployed personnel to help with response efforts and provided support with logistics, staffing, and communication, among other things.

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ongoing outbreak in the Democratic Republic of the Congo. Figure 1 provides a timeline of Ebola outbreaks since 2014.

Zika Zika is a virus that is primarily transmitted through mosquito bites. It can cause symptoms such as fever, rash, conjunctivitis (red eyes), and joint and muscle pain. It can also be transmitted from mother to child during pregnancy, or around the time of birth, or from person to person through sexual contact or blood transfusion.21 Many infected people do not have symptoms or will only experience mild symptoms. The Zika outbreak that began in 2015 affected individuals infected with the virus in ways that had not been seen with previous outbreaks of the disease. Specifically, during the 2015-2016 outbreak, Zika infection in pregnant women was linked to microcephaly and other severe brain defects, according to CDC.22 CDC officials said this was the first time in more than 50 years that an infectious pathogen has been identified as the cause of birth defects. Zika was also linked to other problems, such as miscarriage, stillbirth, and Guillain-Barré syndrome, an uncommon disorder affecting the nervous system.23 In the Western Hemisphere, the first cases of locally- transmitted Zika were confirmed in Brazil in May 2015. In December 2015, locally-transmitted Zika was reported in Puerto Rico. On January 22, 2016, CDC activated its Emergency Operations Center to respond to outbreaks of Zika occurring in

21

GAO, Emerging Infectious Diseases: Actions Needed to Address the Challenges of Responding to Zika Virus Disease Outbreaks, GAO-17-445 (Washington, D.C.: May 23, 2017). 22 Microcephaly is a rare nervous system disorder that causes a baby’s head to be smaller than expected and not fully developed, which can lead to impaired thought processes, delayed motor function, and other adverse outcomes. A study published in December 2017 found that 19 children ages 19 to 24 months born with microcephaly and with laboratory evidence of Zika infection experienced problems, including an inability to sit independently, difficulties with sleeping and feeding, seizures, and hearing and vision problems. Ashley Satterfield-Nash et al., “Health and Development at Age 19-24 Months of 19 Children Who Were Born with Microcephaly and Laboratory Evidence of Congenital Zika Virus Infection During the 2015 Zika Virus Outbreak—Brazil, 2017,” Morbidity and Mortality Weekly Report, vol. 66, no. 49 (Atlanta, Ga.: Centers for Disease Control and Prevention, Dec. 15, 2017): 1347-1351. 23 Guillain-Barré syndrome is a rare disorder in which the body’s immune system attacks the nervous system outside the brain and spinal cord, causing muscle weakness and, in some cases paralysis, although most people recover.

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the Americas and to increased reports of birth defects and Guillain-Barré syndrome in areas affected by Zika. Within the continental United States, the first locally-transmitted cases were confirmed in Florida in June 2016. The World Health Organization declared Zika a Public Health Emergency of International Concern from February to November 2016.24

Pandemic Influenza In the spring of 2009, a novel influenza virus emerged, known as influenza A (H1N1)pdm09.25 According to CDC, it was detected first in the United States and quickly spread across the world, causing a pandemic or global outbreak of a new influenza A virus. This new virus contained a combination of influenza genes not previously identified in animals or people. The virus was very different from other H1N1 viruses circulating at the time, so seasonal influenza vaccines offered little cross-protection against infection with the new H1N1 virus, according to CDC. A vaccine against the new virus was produced, but it was not available in large quantities until late November—after the peak of illnesses during the second wave in the United States.26 CDC activated its EOC on April 22, 2009, to manage the H1N1 response. From April 12, 2009, to April 10, 2010, CDC estimated there were about 60.8 million cases, 274,304 hospitalizations, and 12,469 deaths in the United States due to the new H1N1 virus. According to CDC, few young people had any existing 24

According to the World Health Organization, a Public Health Emergency of International Concern is an event which constitutes a public health risk to other countries through the international spread of disease and potentially requires a coordinated international response. 25 Periodically, new influenza A viruses emerge from animals such as birds and pigs that have sustained transmission and spread efficiently among humans. This situation is called an influenza pandemic. Pandemic influenza is different from seasonal influenza. Pandemic influenza may cause moderate to high rates of medical visits, complications, hospitalization, and death. It may cause major impacts on the general public including travel restrictions and school or business closings and has the potential for having a severe impact on domestic and world economies. In the 2009 pandemic, this virus was designated as influenza A (H1N1) pdm09 virus. 26 We previously reported on key issues raised by the federal government’s response to the 2009 H1N1 influenza pandemic. See GAO, Influenza Pandemic: Lessons from the H1N1 Pandemic Should Be Incorporated into Future Planning, GAO-11-632 (Washington, D.C.: June 27, 2011).

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immunity—as detected by antibody response—to the virus, but nearly onethird of people over 60 years old had antibodies against it, likely from exposure to an older H1N1 virus. Multiple strains of influenza can infect humans, including strains that originate in animals. According to CDC, human infections with an Asian lineage avian influenza A (H7N9) virus were first reported in China in March 2013. During an epidemic that lasted from October 1, 2016, through September 30, 2017, the World Health Organization reported 766 human infections with H7N9 virus, making it the largest H7N9 epidemic. From 2013 to December 7, 2017, there were 1,565 humans infected with Asian lineage H7N9 reported by the World Health Organization. According to CDC, while the risk posed by H7N9 virus to the public’s health was low, the agency was concerned about its pandemic potential.

Infectious Disease Models Agencies use infectious disease models to answer a variety of public health questions, including those related to outbreak preparedness and response.27 A model is a physical, mathematical, or logical representation of a system, phenomenon, or process that allows a researcher to investigate that system, phenomenon, or process in a controlled way. For example, the classic Susceptible-Infected-Recovered or “SIR” model divides a population into three categories: 1) susceptible to the disease, S; 2) infected and infectious, I; and 3) recovered or removed from the infected or susceptible population, R. This model uses equations to determine how many people move between these three categories. The equations contain parameters—numerical descriptors of the disease based, for example, on experiment, expert opinion, or statistics of an ongoing or past outbreak. The equations allow the researcher to estimate how many people are or 27

For the purposes of this chapter, the terms “models” and “modeling” refer specifically to models focused on infectious disease, rather than other types of models or modeling of human health issues. The types of modeling discussed herein may be helpful for emerging infectious diseases such as the novel coronavirus disease; however, this disease is not included in the scope of our review because of the timing of the novel coronavirus event.

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could be affected by the disease. For example, for past Ebola outbreaks, models estimated that after 40 days, about 44 percent of the population in close contact with infected individuals was susceptible to infection, 31 percent was infected, and 22 percent was recovered.28 Based on these parameters, equations for transfer between categories, and underlying demographics of the community, an epidemiologist could use the model to estimate how many people within a given town could be susceptible, infected, or removed from the categories of susceptible or infected (due to death or recovery and immunity).29 Based on model estimates and if a vaccine was available, CDC officials said the decision maker could plan for a specific number of vaccine kits and additional medical staff and supplies to treat infected patients. Models can also help agency officials anticipate future outbreaks, forecast the spread or severity of a disease, and predict the effects and costs of different intervention options. After an outbreak, models can help sort out what happened, what drove the outbreak, and how it compared to past outbreaks. Other tools are available to accomplish some of these tasks, but models are particularly useful when existing data are not sufficient to answer a given question, or when agencies need to integrate data from disparate sources. Infectious disease models can be put into two broad categories: 

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Statistical models. This type of model identifies relationships or patterns that can be used to describe what is occurring or predicts what may occur in the future based on what has occurred in the past. Statistical models tend to use a large amount of data, such as past observed events, to forecast future events, such as disease occurrence, but do not require a fundamental understanding of

Astacio, Jaime, D. Briere, M. Guillen, J. Martinez, F. Rodriguez, N. Valenzuela-Campos, Mathematical Models to Study the Outbreaks of Ebola, Biometrics Unit Technical Reports; Number BU-1365-M (1996). To accurately estimate the numbers of people in any of the Susceptible-Infected- Recovered categories of the model, the latest demographic information, such as population size and density, would be needed.

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biological processes or human behavior.30 They can predict outcomes when causes are not known or understood and when scientific understanding of a disease is limited. They tend to use large amounts of data on past events to forecast future events. Statistical models do not provide full explanations about an infectious disease but may be used when epidemiologists have all or most of the data needed to test a hypothesis. Several benefits can be derived from statistical modeling, including the ability to control for multiple factors that might impact the outcome reviewed, and the ability to isolate the potential effect of infectious disease factors on a particular outcome. Mechanistic models. Mechanistic models rely heavily on scientific evidence and theory related to infectious diseases, and the understanding of disease dynamics or human behavior from prior knowledge—such as biological processes or interactions between people—to represent known processes. They use basic infectious disease science to inform public health guidance and provide insights into outbreak emergence, spread, and control. For example, population- based models can simulate the course of an epidemic by dividing the population into different categories, such as susceptible, infected, and recovered. Mechanistic models can project the likely course of disease transmission, calculate and predict the effect of proposed interventions, and take into account variable conditions, such as human behavior.31

Statistical models can be further broken down by individual techniques, such as regression, which utilizes relationships between predictor variables and response variables, or Bayesian techniques, which use new data to continually update probabilities regarding prior assumptions. 31 Mechanistic models can be further broken down by individual techniques, such as SusceptibleInfected-Recovered models, which move populations through the stages of a disease epidemic from susceptible to infected to recovered, or agent-based models, which incorporate data on an individual’s behaviors into simulations that assess the effectiveness of public health actions, programs, or policies.

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Title National Biodefense Strategy

Year issued 2018

2017-2018 Public Health Emergency Medical Countermeasures Enterprise Strategy and Implementation Plan

2017

HHS Pandemic Influenza Plan

2005 with 2017 update

HHS Ebola Response Improvement Plan National Strategy for Pandemic Influenza Implementation Plan

2016 2006

Description of modeling Maintains the goal of incorporating forecasting and modeling into intelligence products and processes, as appropriate, and improving the ability to model and forecast the likelihood and impact of bioincidents. It calls for using modeling capabilities to maintain situational awareness and support decision making during a response effort. Discusses using models for purposes such as setting requirements for medical countermeasures and highlights modeling’s use in the Zika response. As a part of this plan, ASPR established an Innovation Modeling Hub designed to provide analytic decision support and access to real-time modeling capabilities to senior decision makers. States that forecasting, modeling, and planning tools facilitate dynamic estimates of pandemic influenza spread, burden, and impact. Key actions, as described in the 2017 plan, are using innovative data sources and models to better forecast disease emergence and patterns, and developing and using modeling tools in emergencies to inform policy, clinical guidance, and response strategies involving medical countermeasures. Discusses using modeling to inform the design of clinical vaccine trials. Expands infectious disease modeling capabilities and works to ensure that mechanisms are in place to share model results with state and local authorities, and the private sector.

Legend: ASPR: Office of the Assistant Secretary for Preparedness and Response HHS: Department of Health and Human Services Source: GAO analysis of White House and HHS documents. | GAO-20-372.

Both statistical and mechanistic models can range from simpler to more complex. A simpler model may, for example, have fewer parameters (inputs) or equations than a more complex model. According to CDC

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modelers and an expert, a simpler model may be run with a variety of software, ranging from spreadsheet software to more sophisticated software, whereas more complex models are usually run using sophisticated statistical or mathematical programming languages. As a model becomes more complex, it can become harder to describe, recreate, and understand its internal functioning. Modeling is identified as a beneficial tool in various national plans for disease response and biodefense. These plans do not define the extent to which modeling should occur or how models should be developed for policy, resource allocation, or planning purposes. See Table 1 for examples of relevant national plans.

HHS HAS USED INFECTIOUS DISEASE MODELS TO HELP INFORM POLICY AND PLANNING Use of Models to Inform Planning and Policy Decisions CDC and ASPR use models primarily to answer questions from decision makers. CDC and ASPR officials told us, and documents show, that modeling is one source of information that may inform such decisions, along with sources such as expert opinion, surveillance, other prior work on the disease, and an official’s own knowledge. CDC modelers and officials said there is no “rule” as to when to use models, and in some situations, it may not be considered useful. For example, CDC did not use modeling when issuing a travel notice for an Ebola outbreak in specific provinces in the Democratic Republic of the Congo, officials said. Instead, CDC based the travel order on an analysis that considered disease incidence and prevalence, public health infrastructure, and the availability of therapeutics, among other things. Similarly, CDC officials responding to Ebola said modeling may be undesirable when it would take too long to engage the necessary external

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subject matter experts or when modeling would detract from responding to a disease.32 CDC and ASPR modelers use models for a variety of purposes. CDC officials said modeling is done differently for each disease, and the amount and type of modeling varies across CDC centers, in part because some centers have less capacity to conduct modeling than others. According to a CDC internal report, the most frequent uses of infectious disease modeling at CDC are:  

 

guiding preparedness and response efforts; conducting economic analyses to evaluate the benefits of public health actions, thereby reducing illness and deaths from infectious diseases; understanding pathogen biology, disease transmission, and estimating disease burden; and assessing the effect of interventions and prevention strategies.33

ASPR modelers and officials said models have provided information about topics such as:    

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resources, including protective equipment, needed to help respond to an Ebola outbreak; the number of therapeutics and vaccine doses needed to respond to Ebola, both in Africa and domestically; expected U.S. demand for Zika diagnostics; and the number of vaccine doses needed to mitigate the spread of pandemic influenza.

During an infectious disease outbreak, modeling results may need to be provided to decision makers in time frames potentially as short as one day. 33 Results are based on CDC interviews with 38 CDC infectious disease modelers and 15 division directors from multiple divisions. Infectious disease modelers were interviewed in groups, as were division directors from each center, along with a representative of the Center’s division. According to these interviews, other uses of modeling include guiding the development of public health policies and guidelines.

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ASPR modelers and officials said modelers tend to serve in a broad role that can include modeling, data analysis, or other tasks. For example, officials said a modeler could provide a team with day-to-day analytic support and not necessarily spend time developing models or use them. Additionally, ASPR maintains a Visualization Hub that can be used for outbreak planning and response, including outbreaks of pandemic influenza and other emerging infectious diseases (see Figure 2).

Sources:  Anice Hoachlander, Courtesy Mills + Schnoering Architects, LLS (left

photo), Department of Health and Human Services (right photo); ASPR documentation and interview with ASPR modelers and officials (text) | GAO-20-372. Figure 2. Office of the Assistant Secretary for Preparedness and Response’s (ASPR) Visualization Hub.

CDC and ASPR modelers and officials said they generally initiate modeling in response to questions from decision makers. The modelers then work closely with epidemiologists and other subject matter experts to answer the questions. Modeling, according to CDC officials, may be used by individuals or groups within centers, such as division directors, branches, or teams to influence decisions.34 Who answers a particular question depends, according to ASPR modelers and officials, on the decision maker. Sometimes questions asked will not be within their

34

CDC is made up of centers, offices, and an institute, which we collectively refer to as centers. Such centers can be made up of units known as divisions, and divisions may contain branches.

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mission—modelers may suggest such questions be sent to a more relevant agency or part of HHS. CDC and ASPR have modeled to answer a variety of public health questions relevant to Ebola, Zika, and pandemic influenza, and, at times, the results helped inform policy and planning decisions. Modelers and officials provided the following examples: 

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Planning: ASPR modelers and officials said the bulk of the agency’s modeling is related to the planning, development, and deployment of medical countermeasures. For example, these modelers and officials said many clinical trials for vaccines and therapeutics were planned during the 2014-2016 Ebola outbreak response. As a part of these planning activities, ASPR modelers said modelers developed forecasts of future trajectories of disease incidence under a variety of conditions. These forecasts indicated a significant likelihood the disease incidence in Sierra Leone could decrease to a level that would significantly reduce the success of the trials, according to modelers. Additionally, at the beginning of the 2014-2016 Ebola outbreak response, CDC modelers received modeling questions related to the resources needed to effectively limit the spread of the disease, according to CDC documentation. CDC used models to predict the number of Ebola cases that could be expected over time with and without disease interventions such as Ebola treatment units, community care centers, and safe burials. On the basis of this information and other factors, including a United Nations document on Ebola needs, CDC leadership and other U.S. government officials recommended a rapid increase in Ebola response aid, according to CDC documentation. According to CDC documentation, later analyses demonstrated that this increase helped to greatly reduce the actual number of cases, compared to the likely number if prompt action had not been taken.35 Additionally, in response to the H7N9 influenza outbreak

Meltzer, Martin I, S. Santibanez, L. S. Fischer, T. L. Merlin, B. B. Adhikari, C. Y. Atkins, C. Campbell, I. C. Fung, M. Gambhir, T. Gift, B. Greening, W. Gu, E. U. Jacobson, E. B. Kahn, C. Carias, L. Nerlander, G. Rainisch, M. Shankar, K. Wong, M. L. Washington,,

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in 2017, ASPR modeled to determine when doses of influenza vaccine should be delivered and how many doses should be administered in order to mitigate a domestic outbreak. This model found that having a vaccine stockpile could be helpful in preventing disease and that a slow effort to administer an H7N9 vaccine could reduce the vaccine’s usefulness. Policy: During the Zika outbreak, CDC modelers and officials said they modeled to determine the potential effectiveness of using pesticides to remove insects from aircraft, trains, or ships. According to modelers and agency officials, the issue arose as concern about Zika virus grew, including from other countries and U.S. agencies, like the Department of Transportation and Department of Defense.36 The model indicated that humans are more likely than insects to transport Zika on airplanes, and officials therefore concluded that the use of pesticides on airplanes would not be an effective intervention. According to CDC modelers and officials, this modeling resulted in an additional sentence being added to World Health Organization policy, which stated that pesticide use was not expected to be effective.37 The extent of modeling conducted for Ebola, Zika, and pandemic influenza varied according to the question being asked, along with other factors as follows:38

Modeling in Real Time During the Ebola Response, Morbidity and Mortality Weekly Report, vol. 65, no. 3 Supplement (Atlanta, Ga.: Centers for Disease Control and Prevention, July 8, 2016). 36 Within the United States, the Department of Transportation has a responsibility to guide preparedness for the U.S. aviation system to respond to communicable diseases. GAO-16127. 37 In the World Health Organization’s report on aircraft disinsection for controlling the international spread of vector borne diseases, disinsection was described as considered to be of low effectiveness for preventing the importation of pathogens as the risk of pathogen importation by mosquitos when compared to the risk posed by infected travelers is low. World Health Organization, Report of the WHO Ad-hoc Advisory Group on aircraft disinsection for controlling the international spread of vector-borne diseases, (Geneva, Switzerland: World Health Organization, 2016), 6. 38 Experts and CDC officials said modelers should have the flexibility to select models based on the question that needs to be answered. Neither the HHS agencies we interviewed nor a variety of national strategies and plans we examined have guidance or policies on how agencies should conduct modeling. While this audit focuses on pandemic influenza, CDC

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Type of question: CDC and ASPR have used models to answer such questions as who should be prioritized for vaccination or treatment, how transmissible a disease is, and how effective certain interventions are likely to be, according to modelers and agency officials. For example, ASPR modelers and officials said they modeled to help estimate the resources needed to respond to an Ebola outbreak; the number of therapeutics and vaccine doses needed to respond to Ebola, both in Africa and the U.S; and the expected U.S. demand for Zika diagnostics. One ASPR official said that, during the 2009 pandemic influenza outbreak, modeling questions were used to provide decision makers with information on what might happen in a given situation. For example, models were used to provide information related to decisions on early vaccine distribution and how this intervention could affect the potential mortality rate. Time to model: How soon decision makers needed information also influenced the extent to which CDC and ASPR modeled. For example, if decision makers needed an answer in a week, modelers would inform the decision makers about how much of the answer they could provide within that time frame, ASPR modelers said. Similarly, CDC modelers and officials said that, in one instance, modelers had only 12 hours to provide decision makers with information. Even estimating the time needed to develop and conduct modeling could represent an additional challenge, according to CDC modelers responding to Zika. According to a CDC article on modeling to inform responses to novel influenza viruses, the amount of time required to develop and execute a model can vary from less than a week to more than a month.39 Agency officials concurred with these time frames.

modelers and officials said that they use seasonal influenza modeling to prepare for a pandemic and that seasonal models can be used in a pandemic. Therefore, we are including seasonal models in our review of pandemic influenza modeling. M. Gambhir, C. Bozio, J. J. O’Hagan, A. Uzicanin, L. E. Johnson, M. Biggerstaff and D. L. Swerdlow, “Infectious Disease Modeling Methods as Tools for Informing Response to

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Personnel and data availability: The availability of qualified personnel was also a factor that affected how much modeling agencies conducted for the selected diseases. For example, CDC modelers and officials said the agency’s Division of Vector-Borne Diseases has focused its resources in other areas, such as building the capacity of states to address vector-borne diseases, and therefore had not invested in individuals with the right skill sets to conduct modeling for the Zika outbreak response.40 As a result, the division had to call on the three or four CDC modelers from outside of the division who were available to assist with the Zika outbreak response, which limited the amount of modeling that could be performed. Data challenges can also limit the types of modeling conducted. For example, when modeling for Zika, ASPR modelers said they used available information, but data quality and availability limited their ability to model. More data typically become available as an outbreak progresses, but models may be most helpful at the beginning of an outbreak when critical decisions need to be made (see Figure 3).

CDC and ASPR do not keep a list of all modeling conducted, and we therefore cannot quantify the extent of their efforts in terms of a number of models. ASPR modelers and officials said modeling is typically one small aspect of the way the agency carries out its mission. One ASPR official said models are never the sole source of information for decision-making.

40

Novel Influenza Viruses of Unknown Pandemic Potential,” Clinical Infectious Diseases, vol. 60, Supplement 1 (2015). CDC’s Division of Vector-Borne Diseases has a mission of reducing illness and death from vector-borne diseases such as Zika. Its goals are to (1) identify and detect disease- causing vector-borne pathogens; (2) understand when, where, how often, and how people are exposed to them; (3) prevent exposure and mitigate the consequences of infection; and (4) implement disease diagnostic, surveillance, control, and prevention programs.

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Source: GAO analysis of documents. | GAO-20-372. Figure 3. Timeline of Data Availability for Models Compared to Usefulness of Modeling During an Outbreak.

According to NIH officials, NIH does not conduct or fund internal modeling for decision-making purposes. NIH’s Fogarty International Center has conducted self-initiated, internal modeling to answer questions generated from research, and from ideas from Center-held workshops. Two NIH institutes—the National Institute of General Medical Sciences and the National Institute of Allergy and Infectious Diseases—along with NIH’s Fogarty International Center have awarded grants for external modeling research for our selected diseases. However, NIH officials said these efforts were intended to advance science, not for policy or outbreak response.41

Use of Models to Inform Resource Allocation Decisions CDC and ASPR modelers and officials said they considered modeling results to a limited extent when making decisions about resource 41

NIH is made up of 27 institutes and centers. Of these, 24 institutes and centers award more than 80 percent of the NIH budget each year to support investigators at more than 2,500 universities, medical schools, and other research organizations around the world. NIH institute or center directors decide which grants to fund, considering staff input, results of scientific peer review of grant applications, public health need, scientific opportunity, and the need to balance their scientific portfolios.

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allocation.42 While modeling can help determine the amount of particular resources needed during an infectious disease outbreak, CDC modelers and officials said it is not central to their resource allocation planning. For example, CDC modelers and officials noted that while a model could inform a decision maker about how many diagnostic testing supplies would be needed based on the range of predicted cases, this would be one input among many into the decision. Decision makers would also consider whether there are other diagnostic test supplies for similar diseases that could be used, the extent of laboratory testing capacity, or the longevity of those supplies. Models can be used to help plan for the cost of interventions by determining the numbers or types of interventions that can be used during a response to an infectious disease outbreak, according to CDC modelers and officials. It can also help decision makers recognize gaps in their ability to implement resource allocation decisions, according to CDC officials. For example, CDC leadership described how modeling input requirements spurred analysis of the factors limiting hospitals’ use of ventilators during a pandemic influenza outbreak.43 This work, according to CDC officials, helped determine the number of ventilators that should be included in the national stockpile. While modeling results are important to consider during a public health event, ASPR officials and modelers said it is also important to consider concrete financial estimates based on prior experience and whether recommended medical interventions or countermeasures are available or effective. For example, ASPR modelers and officials have occasionally been asked to analyze costs for medical countermeasures, but modelers and officials said that few medical countermeasures typically meet the 42

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In this review, we asked agency officials about how models inform budget decisions. In response to our questions, modelers and officials said that models are not used to inform budgets but discussed how models informed resource allocation decisions, which may or may not inform budgets. In the context of infectious disease outbreaks, resource allocation refers to planning or using resources—such as drugs or trained personnel—when a disease occurs simultaneously in different but interconnected regions. According to the World Health Organization, analysis is a detailed examination of anything complex in order to understand its nature or to determine essential features. A model is a physical, mathematical, or logical representation of a system, phenomenon, or process that allows a researcher to investigate that system, phenomenon, or process in a controlled way.

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requirements of decision makers, and existing medical countermeasures are typically unavailable for use in a response. ASPR modelers and officials noted that the usefulness of modeling to the decision maker in these instances is limited. In the event that they were asked to model for such questions, ASPR modelers and officials said time would also be a limiting factor in their analysis. CDC has also developed models to inform decision-making at the state level, specifically to assist state and local public health agencies in developing outbreak response plans. A professional organization of epidemiologists we contacted expressed some concerns with limitations of CDC models, specifically noting that state and local officials viewed CDC models as lacking the level of refinement needed for their state- and locallevel planning needs. To follow up, we interviewed officials from a nongeneralizable selection of five states based on their reported use of CDC models, the level of selected disease activity in the state, and geographic variation. Two of the five state health departments we contacted reported using one of CDC’s models for Ebola, Zika, or pandemic influenza. These two states confirmed that the usefulness of the CDC FluSurge pandemic influenza model was limited by unrealistic assumptions or a lack of predictive capability, but added that the models were useful to them when considering how to allocate resources or otherwise prepare for a severe pandemic.44 Officials from one state health department told us they had similar concerns with the CDC Ebola model regarding an unrealistic overestimate of the potential cases, but added that it was useful for informing staff allocation planning as part of their overall response. Officials from another state health department told us they used CDC’s Zika modeling results that indicated how many emergency room visits they could expect and what symptoms it would take to confirm a Zika infection. At the time, state officials said, commercial testing for Zika was not available, so this modeling was very helpful to health officials looking to recommend who hospitals should test based on the presence of Zika 44

According to CDC, FluSurge is a spreadsheet-based model which provides hospital administrators and public health officials with estimates of the surge in demand for hospitalbased services during an influenza pandemic.

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symptoms. State health department officials added that many other factors are considered when deciding on resource allocation, such as local leadership and willingness to embrace the public health response.

AGENCIES COORDINATE INFECTIOUS DISEASE MODELING EFFORTS BUT DO NOT FULLY MONITOR, EVALUATE, AND REPORT ON COORDINATION The four HHS agencies that work on infectious disease modeling reported using multiple mechanisms to coordinate their efforts.45 However, they do not routinely monitor these efforts, evaluate their effectiveness, or report on them to identify areas for improvement.

HHS Agencies Coordinate Infectious Disease Modeling Efforts in Multiple Ways The four HHS agencies that work on infectious disease modeling— ASPR, CDC, FDA, and NIH—reported using multiple mechanisms to varying extents to coordinate such efforts. For example: 

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Emergency Operations Center (EOC). During the response to an outbreak, CDC activates its EOC—a temporary, formal organizational structure for coordinating expertise within CDC and among agencies. The four HHS agencies—ASPR, CDC, FDA, and NIH—used EOCs to coordinate modeling efforts during responses to Ebola, Zika, and pandemic influenza outbreaks.46 For example,

In this chapter, we define coordination broadly as any joint activity that is intended to produce more public value than could be produced when organizations act alone. 46 According to ASPR officials, ASPR uses the HHS Secretary’s operations center, which coordinates with CDC’s EOC. Although included in the EOC, NIH officials stated that NIH’s primary focus was on funding research rather than the use of models during outbreaks. ASPR officials said they have not tested and deployed NIH-sponsored models

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United States Government Accountability Office during the 2015-2016 Zika outbreak, CDC’s EOC served as the command center for monitoring and coordinating the response by bringing together CDC scientists with expertise in areas such as arboviruses (the category that includes Zika), reproductive health, birth defects, and developmental disabilities.47 CDC modelers and officials told us that they had weekly strategy meetings and briefings with response leadership within the EOC where they discussed which modeling questions to prioritize.48 In general, CDC modelers in the EOC were expected to coordinate with modelers from other agencies within and outside of HHS—such as ASPR, FDA, NIH, and the Department of Homeland Security—to produce timely estimates of cases, hospitalizations, and deaths. These estimates can inform response leadership and enable them to assess the speed and impact of the geographic spread of the pandemic. Modelers in the EOC also provide support to decision makers as they examine the potential effects of various response options. These options include when and how to deploy Strategic National Stockpile assets, such as influenza antiviral drugs and mechanical ventilators.49 We found the use of EOCs to be

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directly for use during responses, but that their models are informed by the structural and parametric assumptions used by many academic modelers, including those funded by NIH. Arboviral disease is a general term used to describe infections caused by a group of viruses spread to people by the bite of infected arthropods (insects) such as mosquitoes and ticks. Infections usually occur during warm weather months, when mosquitoes and ticks are active. Examples include California encephalitis, Chikungunya, dengue, Eastern equine encephalitis, Powassan, St. Louis encephalitis, West Nile, Yellow Fever, and Zika. Within CDC’s EOC, the Incident Manager provides leadership and direction for the response and is responsible for ensuring response activities are coordinated across the agency and with external entities. Other leadership positions, such as the Deputy Incident Manager, Chief Science Officer, and Chief of Staff, provide assistance to the Incident Manager. The Strategic National Stockpile is the nation’s largest supply of potentially life-saving pharmaceuticals and medical supplies for use in a public health emergency severe enough to cause local supplies to run out. In October 2018, oversight of the Strategic National Stockpile was transferred from CDC to ASPR. Following this transfer, CDC modelers have continued to support decision-making regarding the acquisition of Strategic National Stockpile assets as well as their release during a response, according to CDC officials. For example, CDC officials said modelers and subject matter experts are working with ASPR modelers to determine stockpiling needs for the recently-approved Ebola vaccine.

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consistent with leading collaboration practices we have previously identified, such as defining and articulating a common outcome.50 Public Health Emergency Medical Countermeasures Enterprise (PHEMCE). The four HHS agencies also participated in PHEMCE, a federal interagency body formed by HHS in 2006 that coordinates the development, acquisition, stockpiling, and recommendations for use of medical products that are needed to effectively respond to a variety of high-consequence public health emergencies.51 PHEMCE is led by ASPR and also includes partners at the Departments of Defense, Veterans Affairs, Homeland Security, and Agriculture. PHEMCE’s 2017- 2018 strategy and implementation plan, its most recent, identified Ebola, pandemic influenza, and emerging infectious diseases more broadly as high-priority threats. PHEMCE leadership could ask modelers to address questions related to these infectious diseases, according to ASPR modelers and officials. According to ASPR officials, such questions tend to support larger response-related efforts, and modeling results are often incorporated into final reports and products. According to ASPR officials, as of February 2020, the PHEMCE structure has been updated and it is unclear how modeling fits into the new structure. We found that coordination through PHEMCE is consistent with leading collaboration practices such as establishing mutually reinforcing or joint strategies. Working groups. Modelers with the four HHS agencies have participated in working groups related to infectious disease modeling (see Table 2). The use of working groups and similar bodies is consistent with leading collaboration practices that we have previously reported as useful for enhancing and sustaining interagency collaboration, such as identifying and addressing

GAO-06-15. HHS, Office of Public Health Emergency Preparedness; Statement of Organization, Functions, and Delegations of Authority, 71 Fed. Reg. 38403 (July 6, 2006).

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needs by leveraging resources.52 For example, CDC and ASPR modelers participated in the National Science and Technology Council’s Pandemic Prediction Forecasting Science and Technology Working Group, which facilitates coordination among numerous federal agencies. In 2016, this group produced a report that identified challenges in outbreak prediction and modeling for federal agencies and offered recommendations for federal actions to advance the development and effective application of outbreak prediction capabilities.53 Joint model development. ASPR and CDC modelers jointly developed some modeling products during outbreak responses. For example, during the 2014-2016 Ebola response, ASPR and CDC developed a model to estimate future numbers of Ebola patients needing treatment at any one time in the United States. According to a publication describing the model, policymakers have used it to evaluate responses to the risk for arrival of Ebola-infected travelers, and it can be used in future infectious disease outbreaks of international origin to plan for persons requiring treatment within the United States.54 Building these positive working relationships can help bridge organizational cultures by building trust and fostering communication, which facilitates collaboration and is vital in responding to emergencies. For example, in our 2011 report, we found that, through interagency planning efforts, federal officials built relationships that helped facilitate the federal response to the H1N1 influenza pandemic.55 Similarly, HHS officials said that federal coordination during the H1N1 pandemic was much easier because of these formal networks and informal

GAO-06-15. National Science and Technology Council, Towards Epidemic Prediction. From 2015 until July 2018, the National Security Council maintained a permanent Pandemic Preparedness and Response Directorate. This directorate was to oversee ongoing work to prepare for future infectious disease threats, and to coordinate a response when such threats arise. 54 Rainisch G. et al. “Estimating Ebola Treatment Needs, United States”. Emerging Infectious Diseases (2015). 55 GAO-11-632. 53

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relationships built during pandemic planning activities and exercises. Table 2. Selected examples of working groups for infectious disease modeling Working group Pandemic Prediction Forecasting Science and Technology Working Group

Description This interagency working group, directed by the National Science and Technology Council, is responsible for analyzing the state of infectious disease modeling and prediction, and facilitating coordination among numerous federal agencies. According to CDC modelers and officials, as of October 2018, the charter for this group is no longer active, and it meets on a voluntary, ad hoc basis. Centers for Disease Control According to CDC officials, this group connects modelers by and Prevention’s (CDC) holding seminars, managing an email list, and arranging for infectious disease modeling members to peer review one another’s models. This group had over community of practice 160 participants from various centers across CDC, as of June 2019. Modeling coordination During the 2014-2016 Ebola and 2015-2016 Zika outbreaks, the groups Department of Health and Human Services’ (HHS) Office of the Assistant Secretary for Preparedness and Response (ASPR) established temporary modeling coordination groups that brought together government agencies and academics to share early modeling results and discuss pressing questions that could be answered through modeling, according to ASPR modelers and officials. A wide range of entities participated in these groups, including the four HHS agencies, other federal agencies such as the Departments of Defense and Homeland Security, universities, and foreign entities, such as the World Health Organization and the United Kingdom. According to ASPR modelers and officials, there are no plans to convene modeling coordination groups unless there is an ongoing infectious disease outbreak. Source: GAO analysis of ASPR and CDC information. | GAO-20-372.

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Memoranda of understanding. The four HHS agencies have entered into various agreements through memoranda of understanding in order to define their relationships for coordinating infectious disease modeling (see Table 3). Generally these memoranda were between individual agencies rather than department-wide. We found that the use of memoranda of understanding was consistent with leading collaboration practices,

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United States Government Accountability Office such as agreeing on roles and responsibilities. Our prior work found that agencies that articulate their agreements in formal documents can strengthen their commitment to working collaboratively.56 Similarly, CDC modelers and officials said that written agreements can reduce the possibility of misunderstandings or disagreements and help ensure that participants have a mutual understanding of collaboration goals. For example, in the absence of such written agreements, the potential for duplication is increased because agencies could be working on similar types of models without one another’s knowledge. Table 3. Selected examples of memoranda of understanding for coordinating on infectious disease modeling

Collaborating agencies The Office of the Assistant Secretary for Preparedness and Response (ASPR) and Centers for Disease Control and Prevention (CDC)

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Description From 2013 to 2018, CDC and ASPR had a memorandum of understanding to promote collaboration, provide expertise, and facilitate data and information exchange related to infectious disease modeling. This agreement expired in 2018. ASPR modelers and officials told us that, as of August 2019, it had not been updated, and there were no plans to do so. Despite this, according to CDC modelers and officials, the substance of the agreement is still being followed. CDC modelers and officials told us they continue to collaborate with ASPR modelers on the development of models that address questions of mutual interest. For example, for the ongoing Ebola response, CDC modelers and officials said they have kept ASPR informed on modeling efforts, and ASPR shares data on vaccine production that is included in one of the models. ASPR and FDA have a memorandum of understanding to promote collaboration and enhance knowledge and efficiency by providing for the sharing of information and expertise. This memorandum was in place from 2012 to 2017, and was then renewed in 2019. It remains valid unless modified by consent of both parties or terminated by either party immediately upon written notice in the event that a federal statute is enacted or a regulation is issued by a federal partner that materially affects the memorandum.

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Description According to FDA modelers and officials, the agreement facilitates collaboration related to FDA’s Medical Countermeasure Initiative and FDA’s role in supporting the HHS-led Public Health Emergency Medical Countermeasures Enterprise (PHEMCE).a FDA modelers and officials told us that the agreement supports the frequent, ongoing collaborations between FDA and ASPR, including collaboration related to preparedness for emerging infectious diseases. However, FDA modelers and officials said, while no specific steps have been taken with regards to collaborating on infectious disease modeling under the agreement, modeling assistance could be provided in the future, if needed. ASPR and the National From 2013-2018, ASPR had a memorandum of understanding with Institutes of Health’s NIH’s Models of Infectious Disease Agent Study program to (1) (NIH) Models of enable Models of Infectious Disease Agent Study program Infectious Disease Agent researchers to work with ASPR as part of public health preparedness Study programb and response activities, (2) share data and information, and (3) support model development and use in the HHS modeling hub. This agreement has expired. ASPR modelers and officials told us that, as of August 2019, it has not been updated, and there were no plans to do so. CDC and NIH’s Models of Since 2015, CDC has had a memorandum of understanding with Infectious Disease Agent NIH’s Models of Infectious Disease Agent Study program, to Study programb promote collaboration and facilitate the exchange of data, tools (models), methods, and information. It was set to expire in February 2020. ASPR and other federal From 2013 to 2018, ASPR had separate memoranda of agencies understanding with the Departments of Defense and Homeland Security to promote collaboration, provide expertise, and facilitate data and information exchange. The goals of the collaboration in both agreements were to explore ways to, among other things: share analytical approaches and efforts, such as modeling and simulation tools, in support of public health preparedness and response activities; provide personnel as needed to facilitate analytical efforts; and share data and information. These goals were similar to those laid out in the agreement between CDC and ASPR. These agreements expired in 2018. ASPR modelers and officials told us that, as of October 2019, they have not been updated, and there were no plans to do so. Source: GAO analysis of ASPR and CDC information. | GAO-20-372. a Launched in 2010, FDA’s Medical Countermeasures Initiative is intended to coordinate medical countermeasure development, preparedness and response within FDA. b NIH’s Models of Infectious Disease Agent Study program is a collaboration of research and informatics groups to develop computational models describing the interactions between infectious agents and their hosts, disease spread, prediction systems, and response strategies.

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Forecasting competitions. CDC and NIH have sponsored formal forecasting competitions to improve modeling for Ebola, Zika, and seasonal influenza.57 According to a report from the National Science and Technology Council, controlled, multi‐center modeling contests and projects generate valuable insights. For example, they often show that simpler models perform as well as more complex models and that ensemble models, which combine the results of multiple models to predict an outcome, perform better than an individual model.58 Such competitions are consistent with a leading collaboration practice we previously reported: identifying and addressing needs by leveraging resources.59 In this case, such leveraging allowed CDC and NIH to obtain additional benefits and insights on models that may not otherwise be available. These modeling competitions can therefore help the HHS agencies better prepare for future outbreaks through coordination with participants. The following are examples of forecasting competitions sponsored by CDC or NIH: Ebola competition. NIH’s Fogarty International Center held an Ebola forecasting competition from August to December 2015, related to the 2014-2016 West African Ebola outbreak, to compare the accuracy of predictions from different Ebola models, among other things. According to NIH modelers and officials, lessons learned from the challenge were that (1) with regard to short-term

CDC modelers told us that models are developed to model for influenza in general, not specifically for seasonal or pandemic influenza. These officials said they model specifically for pandemic influenza when they identify a novel influenza strain. There are different ways a model can be modified for pandemic influenza to represent the nuances of how the virus is shared and the disease’s dynamics. When new models are developed for pandemic influenza, the basics of the disease’s transmission and growth are the same as those used for seasonal influenza. However, for pandemic influenza, they are more extreme. 58 National Science and Technology Council, Towards Epidemic Prediction. Simpler models are ones that use few parameters (inputs) or equations and interactions between those parameters or equations, while complex models are ones that use many more parameters or equations and have more interactions between them. Ensemble modeling is a process where multiple models that show different possible outcomes are combined to predict an outcome. The objective of ensemble modeling is to improve the accuracy of the model through averaging the result of multiple models. In weather forecasting, ensemble models are used by forecasters to measure the likelihood of a forecast. 59 GAO-06-15.

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incidence predictions, ensemble estimates were more consistently accurate than predictions by any individual participating model; (2) as expected, more accurate and granular epidemiological data improved forecasting accuracy; (3) the availability of contextual information, including patient-level data and situational reports, is important for accurate predictions; (4) the accuracy of forecasting was not positively associated with more complex models; and (5) coordination of modeling teams and comparison of different models is important to ensure robustness of predictions. According to NIH officials, based on these lessons and in response to the most recent Ebola outbreak, NIH has established a coordination group to share information about modeling and data sharing for this particular outbreak and a formal model comparison is underway under World Health Organization leadership. Aedes (Zika) competition. In 2019, CDC hosted a forecasting competition related to using models to predict the presence of Aedes mosquitoes, which is a vector for the Zika virus. Evaluating these models can, according to CDC, help clarify model accuracy and utility, the seasonal and geographical dynamics of these mosquitoes, and key directions for future research. According to CDC documentation, these advances can contribute to improved preparedness for arboviral invasion in the United States and in other regions where Aedes suitability may be limited and changing.60 CDC plans to evaluate forecasts for this competition in early 2020, as soon as final surveillance data for 2019 are available. FluSight (seasonal influenza) competition. CDC holds an annual seasonal influenza forecasting competition—known as FluSight— to facilitate efforts to engage external researchers to improve the science and usability of seasonal influenza forecasts. The results of

Arboviral disease is a general term used to describe infections caused by a group of viruses spread to people by the bite of infected arthropods (insects) such as mosquitoes and ticks. Infections usually occur during warm weather months, when mosquitoes and ticks are active. Examples include California encephalitis, Chikungunya, dengue, Eastern equine encephalitis, Powassan, St. Louis encephalitis, West Nile, Yellow Fever, and Zika.

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the competition are evaluated by the CDC Influenza Division, which works with state and local partners to determine whether the results are useful to them and if there are other metrics, milestones, or targets that would be more helpful in making public health decisions. According to CDC officials in February 2020, the results from the FluSight competition are not directly incorporated into pandemic influenza forecasting because the most accurate seasonal influenza forecasts would not necessarily be the most accurate pandemic influenza forecasts. According to these officials, the overall lessons learned from the FluSight competition relate to how to quantify, visualize, and communicate model results and model accuracy, as well as the value of forecast ensembles to summarize multiple models. CDC officials said these lessons are incorporated into pandemic influenza forecasting plans. Coordination with academic and other modelers. CDC coordinated infectious disease modeling efforts with academic and other modelers through various means, including the following: Intergovernmental Personnel Act agreements. CDC has used agreements under the Intergovernmental Personnel Act of 1970 to collaborate with external experts on modeling efforts.61 For example CDC’s Division of Vector-Borne Diseases had an agreement from 2014 to 2017 to assign a CDC official to the Harvard T.H. Chan School of Public Health. The agreement was to help CDC integrate with a larger modeling community and provide the Harvard School of Public Health with expertise in arboviral diseases and applied public health. Vector-Borne Disease Centers of Excellence. CDC has funded the Vector-Borne Disease Centers of Excellence, which are engaged in modeling-specific projects. In 2017, CDC established five

The Intergovernmental Personnel Act of 1970, Pub. L. No. 91-648, 84 Stat. 1909 (Jan. 5, 1971), as amended, codified at 5 U.S.C. §§ 3371-3375. The Intergovernmental Personnel Act’s Mobility Program provides for the temporary assignment of personnel between the federal government and state and local governments, colleges and universities, Indian tribal governments, federally-funded research and development centers, and other eligible organizations.

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universities as regional centers of excellence to help prevent and rapidly respond to emerging vector-borne diseases across the United States. According to CDC, the goals of the centers are to build effective collaboration between academic communities and public health organizations at federal, state, and local levels for surveillance, prevention, and response, among other things. Support for other governmental entities. CDC has coordinated with other entities—such as state and local officials—to provide modeling tools, estimates of case counts, or effects of interventions during the Ebola, Zika, and pandemic influenza outbreaks. For example, CDC developed pandemic influenza models for state and local health departments to use in influenza pandemic planning activities. The tools are available on the CDC pandemic influenza website and from ASPR’s emergency preparedness information portal. As previously discussed, officials from two of the states we spoke with said they generally were unaware of the availability of the models. According to CDC modelers and officials, these models were developed in the mid-2000s for pandemic influenza planning and remain useful but had not been a priority to update because they have not received a request to do so. Informal collaboration. CDC has engaged in a range of informal collaborations related to infectious disease modeling. According to CDC modelers and officials, modelers often develop relationships through conferences or other contacts. For example, CDC modelers and officials said they informally collaborated on Ebola modeling needs with academic institutions, as well as modelers and analysts in the World Health Organization and other U.S. government agencies, such as the Federal Emergency Management Agency. For example, CDC modelers and officials told us that model estimates produced under collaboration with academics helped inform decisions about how many beds to be ordered and delivered on the ground in West Africa during the 2014-2016 Ebola Outbreak. Similar to the forecasting competitions described above, such informal coordination mechanisms are consistent with

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United States Government Accountability Office the best practice of identifying and addressing needs by leveraging resources, thus obtaining additional benefits that may not be available if they were working separately.62 For example, we have previously reported that informal collaboration mechanisms—such as building relationships between key personnel and soliciting input for research projects—can provide the opportunity to leverage expertise.63

HHS Agencies Do Not Fully Monitor, Evaluate, and Report on Coordination Efforts CDC and ASPR modelers and officials did not routinely monitor, evaluate, and report on coordination efforts for infectious disease modeling.64 While CDC did conduct after-action reviews for Ebola and Zika, which included a review of modeling efforts, such reviews are not routine outside of a response and do not examine modeling coordination between agencies.65 ASPR modelers and officials told us they saw no reason to monitor coordination efforts under the memorandum of understanding with CDC because such memoranda outline expectations rather than requirements. However, we have found that agencies that create a means to monitor, evaluate, and report the results of collaborative efforts can better identify areas for improvement.66 We have previously reported 62

GAO-06-15. GAO, Biodefense: Federal Efforts to Develop Biological threat Awareness, GAO-18-155 (Washington D.C.: Oct. 11, 2017). 64 As previously discussed, FDA maintains a limited role in modeling for the three diseases we focused on related solely to the blood donor bank for Zika, and NIH funds, rather than conducts, modeling. As such, we have chosen to focus on ASPR and CDC—the agencies which more routinely model for Ebola, Zika, and pandemic influenza—in this portion of our discussion. 65 CDC conducted an after-action review of the 2014-2016 Ebola outbreak to assess the effectiveness of the response. Specifically, the report found that response teams and task forces did not fully understand the role of modeling. The report also found that, when modelers were embedded in their own task force, they had better access to response knowledge and leadership, could more easily collaborate, and could establish their own priorities based on all response needs. 66 GAO-06-15. 63

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that progress reviews or after action reviews can be useful mechanisms for monitoring, evaluating, and reporting on collaborative efforts. For example, we previously reported that, to monitor, evaluate, and report on the status of achieving the Healthy People 2010 objectives, HHS held progress reviews in which the federal agencies with lead responsibilities for a focus area reported on the progress towards achieving the objectives. During these reviews, the participating agencies discussed the data trends, barriers to achieving the objectives, strategies undertaken to overcome barriers, and alternative approaches to attain further progress. By holding similar progress reviews in which CDC and ASPR evaluate and report on coordination efforts for infectious disease modeling, these agencies could be better positioned to identify and address challenges prior to infectious disease outbreaks occurring, which could lead to improved responses. Further, there is the potential for overlap and duplication of modeling efforts across agencies, which may not be identified if coordination efforts are not effectively being monitored, and which could lead to inefficiencies. The memorandum of understanding between CDC and ASPR had expired in 2018. Agency officials told us they had no plans to review or update the agreement. According to ASPR modelers and officials, the agreement has not been updated because it was not a priority and the substance of the expired agreement is being followed. However, without an active agreement in place that clearly defines the goals of the collaborative effort and the roles and responsibilities of participants, a lack of understanding and agreement becomes more likely, particularly as agencies’ priorities evolve over time. Our prior work on leading collaboration practices found that agencies that articulate their agreements in formal documents can strengthen their commitments to working collaboratively, and that such agreements are most effective when they are regularly reviewed and updated.67 Further, we found that the memorandum of understanding between ASPR and CDC was not fully implemented when it was active. For example, according to this agreement, CDC was to appoint a designee to

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participate in a steering committee related to modeling within HHS. However, ASPR modelers and officials told us that this steering committee was never formed because of changing leadership and priorities. They told us that HHS does not have any intention to form such a steering committee in the future. However, our past work shows creating a steering committee or other similar coordination mechanism could help facilitate monitoring of coordination efforts.68 We similarly found that other memoranda of understanding related to infectious disease modeling were not fully implemented. For example, although ASPR had a 2013-2018 memorandum of understanding with NIH’s Models of Infectious Disease Agency Study program, ASPR modelers and officials said they rarely use models funded by NIH, including those funded through the program.69 In particular, ASPR modelers and officials recalled only using one such model in recent years. That model, known as “FluTE,” is an influenza model that was used as part of a larger study on vaccine availability. However, ASPR modelers faced challenges in using this model. Specifically, these ASPR modelers and officials said the FluTE model initially was not compatible with ASPR’s computer system, so software engineers had to modify the source code to resolve the compatibility issue. The model did not have documentation describing its parameters, according to ASPR modelers and officials, so they had to read through the model’s source code to understand them. Similarly, regarding a separate agreement between ASPR and FDA, FDA modelers and officials said that, while there is ongoing information sharing, no specific steps have been taken with regard to collaborating on infectious disease modeling under the agreement. However, these modelers and agency officials said that modeling assistance could be provided in the future, if needed.

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GAO, Marine Debris: Interagency Committee Members Are Taking Action, but Additional Steps Could Enhance the Federal Response, GAO-19-653 (Washington, D.C.: Sept. 25, 2019). NIH’s Models of Infectious Disease Agency Study Program is a collaborative network of scientists who develop and use models to improve the understanding of infectious disease dynamics.

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CDC AND ASPR GENERALLY FOLLOWED IDENTIFIED PRACTICES FOR INFECTIOUS DISEASE MODELING, BUT CDC HAS NOT FULLY ENSURED MODEL REPRODUCIBILITY We identified four elements of practices for developing and assessing models: (1) communication between decision maker and modeler, (2) description of the model, (3) verification, and (4) validation. We determined that CDC and ASPR generally followed these GAO-identified practices for 10 models we reviewed.70 However, for four of the 10 models, CDC modelers did not provide all of the details needed in the verification steps to reproduce their model results, which is inconsistent with HHS guidelines on transparency and reproducibility.71

CDC and ASPR Generally Followed Identified Modeling Practices but Did Not Always Fully Assess Model Performance According to our interviews with agency modelers and experts, along with our review of selected literature, there are no documented standards that prescribe the steps agencies must or should follow when developing and assessing models. However, based on our interviews and review, we identified four broad elements of the modeling process that modelers generally consider. They are: 1) communication between modelers and officials to refine questions to be addressed by the model, such as geographic spread of the disease and total cases of the disease; 70

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GAO shared with agency modelers and outside experts the practices we identified for model development and assessment and confirmed with them that these practices were generally used and appropriate for our analysis. U.S. Department of Health & Human Services, Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated to the Public, (Washington, D.C.: October 2002) (HHS Guidelines) Pt. I, D.2.c.2.

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United States Government Accountability Office 2) description of the model, including detailed descriptions of assumptions and data sources used; 3) verification; and 4) validation.72 Figure 4 outlines the model development and assessment process.

1. Validation of the model should not rely on the same data used to develop the model. Source: GAO analysis of peer-reviewed literature and expert interviews. | GAO-20372. Figure 4. Outline of Process to Develop Models and Assess Their Performance.

Based on our assessment of 10 selected models, we found that CDC and ASPR generally took steps that corresponded to our four elements, and agency modelers generally agreed with our assessment of each model. See Table 4 for more information on the elements.73 See appendix III for a list of models we reviewed and a complete list of the steps we identified that make up each element.

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Verification establishes that the model is running as intended by its developers. Validation involves running the model and determining if the results are consistent with data external to the model itself. We reviewed seven infectious disease models from CDC and three from ASPR. Of these models, three were for Ebola (two CDC, one ASPR), three for Zika (two CDC, one ASPR), and four for pandemic influenza (three CDC, one ASPR).

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Table 4. Elements of Model Development Element Communication between decision maker and modelera Description of the model

Description and selected steps Establishes clear understanding of model questions, limitations, uncertainty, etc. This element can include a discussion between a decision maker and a modeler. If done properly, it confirms the model is designed to answer the decision maker’s questions. This step includes a clear description of model assumptions, limitations, inputs, outputs, general type of model (stochastic, deterministic, etc.). It also includes a statement of the model equations, algorithms, and software.b Verification This element establishes that the model is running as intended by its developers. It consists of an independent expert reviewing model programming, checking code accuracy, confirming model code is publicly shared, and testing model assumptions and handling of input data. Validation This element establishes that the model is providing results consistent with data external to the model itself. It consists of comparing model output with real-world data (historical or current), data from comparable models, and analysis of the sensitivity of model outputs to changes in various model inputs (sensitivity analysis). Source: GAO Analysis of documents from the Centers for Disease Control and Prevention, Office of the Assistant Secretary for Preparedness and Response, and other sources | GAO-20-372. a For ease of reading and comprehension, Table 4 combines the elements of “Clarifying Objectives” and “Communicating Results” in Appendix 3 into the overarching “Communication between decision maker and modeler” element listed above. b The National Institute of Standards and Technology defines an algorithm as a computable set of steps to achieve a desired result.

Communication between Modeler and Decision Maker In all 10 agency models we reviewed, we found that agencies took all the steps we identified for communication between decision maker and modeler. In some cases, these steps were formalized, while in others they were informal. For example, CDC modelers responding to Ebola ensured communication with decision makers by following a memo template they developed, which has a section requiring modelers to communicate key aspects of their model. These modelers noted, however, that they would not follow all the steps in their memo template for models developed during an outbreak because of time constraints. CDC modelers responding to pandemic influenza noted they do not have formal best practices for communication about key model aspects to decision makers, and a CDC modeler responding to Zika highlighted the role of CDC’s Emergency

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Operations Center (EOC) in communication between decision makers and modelers, which is activated only during a response. ASPR modelers noted that—as a best practice—they hold a discussion for all new models, in which decision makers describe what they are looking for and modelers describe what they can provide.

Description of the Model In nine of the 10 models we reviewed, modelers took all steps we identified for describing their model type, inputs, outputs, assumptions, and limitations. In one case, ASPR’s “flumodels” package, the agency did not carry out the step of describing the model’s limitations. ASPR modelers told us they did not do so because they expected the model’s intended users—primarily federal public health modeling experts—would understand the limitations of their model, an assumption we find reasonable. Verification In six of 10 models reviewed, we found agency modelers followed most of the steps we identified for model verification. However, in four of the seven CDC models reviewed, CDC did not publish the model’s code, a part of model reproducibility and a model verification step.74 We examine CDC’s policy and efforts on reproducibility in more detail below. Validation For four of the 10 models we reviewed, agencies performed few validation steps. In all three CDC pandemic influenza models we reviewed, and the ASPR Zika model, sensitivity analysis was the only validation step performed.75 CDC influenza modelers said they did not perform other validation steps because of a lack of comparable external models or applicable data which could be used for other types of model validation. 74 75

All three of the ASPR models we reviewed included published model code. We examined three Ebola models, three Zika models, and four pandemic influenza models for our review. Sensitivity analysis is a model validation method that is used to determine how much the model projections change in response to changes in input data.

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For example, they said they could not validate their models using realworld data because they made projections for scenarios that did not come to pass (e.g., an unmitigated pandemic influenza outbreak). They said they have continued to look for comparable models that could be used to crossvalidate their model estimates. ASPR modelers responding to the Zika outbreak also did not have access to comparable external models or applicable data to confirm their model projections, but have since attempted to validate their model. For the other six models we reviewed, agencies carried out most but not all validation steps. For example, CDC modelers responding to Zika also said they did not perform crossvalidation (comparison of different model results to each other) for their Zika model because of a lack of comparable models. However, these ASPR and CDC Zika modelers said they have attempted to validate their model since its publication as new data emerges, and we found this occurred. CDC modelers and ASPR modelers responding to Zika followed identified practices and validated their model projections for the Zika outbreak, although their efforts yielded mixed results for model performance. CDC modelers responding to Zika attempted to estimate whether there was an enhanced risk of microcephaly in infants born to expectant mothers infected with Zika.76 Using data available during the initial stage of the outbreak, they calculated the enhanced risk to be between 0.88 and 13.2 percent if the mother was infected in the first trimester. In two subsequent studies using later data on the actual incidence of microcephaly as a result of the outbreak, other researchers found the enhanced risk was within the bounds of CDC modelers’ earlier projections: a 10 percent enhanced risk in one study and an 8.3 percent enhanced risk in the other. In the second case, ASPR modelers attempted to estimate potential new cases of Guillain-Barré syndrome, a rare disorder in which the body’s immune system attacks part of its own nervous system, in places burdened by Zika infection. Their initial projections were that there 76

Microcephaly is a birth defect where a baby’s head is smaller than expected when compared to babies of the same sex and age. Babies with microcephaly often have smaller brains that might not have developed properly.

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would be between 191 and 305 new cases in Puerto Rico, a three- to fivefold increase above the number normally expected. ASPR modelers attempted to verify these results themselves and found that the incidence did increase, but only two-fold, to 123 new cases. Assessing Model Validity Assessing model validity means determining whether a model is sufficiently accurate for its purpose. Several methods are available, including the following:  

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Modelers can compare the results of the model against real-world data the model was designed to predict. If there are no such data, another method is to determine how much the model projections change in response to changes in input data. This is known as model sensitivity analysis. Modelers can also withhold a part of the available data in building the model and then confirm the model can reproduce the withheld data. A method that does not rely directly on real-world data is to run the model along with a separate, independent model using the same input data, and comparing the outputs. Model validity can also be assessed through independent performance evaluations. For example, agencies sometimes host modeling competitions, in which independent modelers compare the predictive performance of multiple models under controlled conditions using standardized data. The National Institutes of Health hosted an Ebola forecasting competition in 2015, and the Centers for Disease Control and Prevention (CDC) launched its FluSight competition in 2013.

During an infectious disease outbreak, multiple validation approaches may be necessary, since there may be insufficient data or few comparable models. For these reasons, experts said that model validation should be frequent and is never complete. Source: GAO analysis of published academic literature, US Department of Energy documents, CDC documents and data. | GAO-20-372.

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Agency Modelers Follow a Variety of Approaches to Modeling The Challenge of Modeling During an Outbreak Early in the 2014-2016 Ebola outbreak, Centers for Disease Control and Prevention (CDC) officials faced the challenge of answering questions with limited data and time. In order to estimate the potential number of future cases and to aid in planning for additional disease-control efforts, CDC developed EbolaResponse, an Excel spreadsheet-based model that could forecast how interventions would impact the outbreak. Using EbolaResponse, CDC predicted in early September 2014 that 1.4 million cases of Ebola could occur in Liberia and Sierra Leone by January 2015, if the world health community did not increase interventions. These estimates included a correction factor intended to account for the underreporting of cases and that, according to officials, was to represent model uncertainty. Partly because of these estimates of rapidly increasing cases, CDC and others increased intervention by sending more treatment units, personnel, and medical supplies in late 2014. EbolaResponse was created to model the effects of intervention, and it later turned out to be unreliable for the 4-month forecast that CDC used to support its request for increased intervention. Independent analysis found that the model could forecast cases up to a month ahead well but could not provide any measure of uncertainty. Furthermore, the model was unable to make accurate forecasts much beyond 3 months, a limitation that was common among the models used during the outbreak. CDC later reported that roughly 8,500 cases, or 34 percent of the corrected EbolaResponse prediction of 25,000 cases, occurred in Liberia by the end of January 2015. Source: GAO analysis of CDC documents and data. | GAO-20-372.

We also found that CDC and ASPR modeling approaches varied somewhat, while generally remaining within the bounds of our identified practices. For example, all the agency modeling groups reviewed their model assumptions, but they also varied in whether this review was formal or informal and internal or external. CDC modelers responding to Ebola use a formal internal peer review process during non-outbreak periods, as

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well as a detailed checklist to ensure communication with decision makers, full consideration of model inputs and outputs, quantification of model uncertainty, and validation of the model. By contrast, CDC modelers responding to Zika told us they do not have a formal system for evaluating their models, and instead rely on their own review of model assumptions. ASPR and CDC pandemic influenza modelers told us their modeling approach also relied on peer review, but the review was done by external experts; informally for ASPR and formally for CDC pandemic influenza modelers. There are several reasons agency modeling approaches can vary. According to agency modelers, agency modeling practices can be influenced by the availability of time, data, and comparable models. For example, CDC pandemic influenza modelers and officials said they follow a shortened process when facing time constraints by documenting model development in a journal publication after the model has already been put to use. Similarly, CDC modelers responding to Ebola noted that, during a response, a lack of time may mean models are not reviewed through CDC’s formal clearance process; instead, a more informal review of model results may occur. CDC and ASPR modelers also described variation in the complexity of the models they use. They said they sometimes use both simple and complex models for the same disease and during the same outbreak. CDC modelers and officials responding to Ebola said that they preferred models run in spreadsheet programs for their transparency and communicability, whereas CDC influenza modelers mostly use dedicated statistical software programs to run models and spreadsheets for communicating with state and local health departments. ASPR modelers develop more complex prediction models so that they can be reused to answer more than one question, as opposed to models run in spreadsheet programs that are designed to answer one question. Experts and agency modelers generally agreed that infectious disease models should not be more complex than is necessary to answer the questions they were developed to address. A simpler model may be run on a variety of software programs, ranging from spreadsheet programs to

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specialized programming languages that can do statistical analysis. One downside of models run in spreadsheet programs, according to CDC influenza modelers, is that it is harder to conduct quality control measures. Two experts we spoke to, along with CDC Zika modelers, also expressed concerns with reliability and reproducibility of models run in spreadsheet programs.77

CDC Has Not Fully Implemented a Policy to Ensure Model Reproducibility Since 2002, HHS agencies responsible for disseminating influential scientific, financial, or statistical information have been required to ensure methods used to develop this information are “reproducible.”78 A 2019 report from the National Academies of Sciences, Engineering, and Medicine noted that the scientific enterprise depends on the ability of the scientific community to scrutinize scientific claims and to gain confidence over time in results and inferences that have stood up to repeated testing. As part of this process of scrutiny, a study’s data and code should be made available so that the study is reproducible by others.79 The National 77

The American Statistical Association notes that, while spreadsheet tools such as Microsoft Excel are useful for a variety of purposes, they do not consider it an ideal environment for programming or reproducible analysis and they do not recommend using it for the primary analysis of data. 78 Section 515 of the Consolidated Appropriations Act, 2001, Pub. L. No. 106-554, App. C, 114 Stat. 2763A-125, 153-54 (Dec. 21. 2000), directed the Office of Management and Budget to issue government-wide guidelines “that provide policy and procedural guidance to Federal agencies for ensuring and maximizing the quality, objectivity, utility, and integrity of information (including statistical information) disseminated by Federal agencies.” The Office of Management and Budget issued these guidelines in 2002. Office of Management and Budget, Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated by Federal Agencies; Republication, 67 Fed. Reg. 8452 (Feb. 22, 2002). In accordance with the Office of Management and Budget’s guidelines, HHS issued guidelines in 2002, (HHS Guidelines). HHS is also subject to a number of statutory requirements that protect the sensitive information it gathers and maintains including, but not limited to, the Health Insurance Portability and Accountability Act of 1996, Privacy Act of 1974, and Federal Information Security Management Act of 2014. In addition, the HHS guidelines state that agency requirements for reproducibility and transparency in their scientific publications “does not override other compelling interests such as privacy, trade secrets, intellectual property, and other confidentiality protections” required by law. (HHS Guidelines, Pt. I, D.2.c.2.). 79 National Academies of Sciences, Engineering, and Medicine, Reproducibility and Replicability in Science (2019) (Washington, D.C.: National Academies Press, 2019).

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Academies report defines reproducibility as obtaining consistent computational results using the same input data, computational steps, methods, code, and conditions of analysis. Reproducibility is specifically addressed earlier in this section in our discussion of model verification, a step that requires making code available for independent review. HHS requires its component agencies to either follow HHS department guidelines on reproducibility or to ensure their own guidelines include a high degree of transparency about the data and methods used to generate scientific information.80 HHS guidelines require that, in a scientific context, agencies identify the supporting data and models for their published scientific information and provide sufficient transparency about data and methods that an independent reanalysis could be undertaken by a qualified member of the public. When asked whether CDC has specific policies related to reproducibility that would have applied to provision of model code in their published scientific research, CDC referred to its guidelines developed in response to the 2002 HHS Guidelines.81 However, CDC guidelines do not contain any reference to reproducibility, models, or provision of model code. CDC guidelines for review of scientific information provided to the public focus on completeness, accuracy and timeliness, data management and analysis, clarity and accuracy of presentation, and validity of interpretation of findings. CDC’s policy on public health research and non-research data management and access does not make any reference to reproducibility or model code. This lack of reference to reproducibility in CDC’s guidelines and policies is not in accordance with HHS guidelines. Our review found four instances in which CDC modelers did not provide model code when they published their models. CDC modelers said in some instances, issues with publication formats made the code difficult to share, they did not have time to produce a user-friendly version of the code, or they would share the code upon request.

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HHS Guidelines, Pt. I, C, Pt. I, D.2.c.2. HHS Guidelines, Pt. I, D.2.j. Section D “Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry, Subsection V, Part B.

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By contrast, ASPR modelers provided code for every model within our review when they published their models. While neither agency cited a specific HHS policy that required them to share model code, ASPR modelers noted that their internal peer review process typically includes sharing model source code with other modelers within PHEMCE. In our review of HHS guidelines and agency-specific guidance for these HHS guidelines, we found that, of three published agency guidance, two require reproducibility, or transparency for the methods used in the reports they issue to the public. Of these agencies, CDC was the only one that did not explicitly require transparency or reproducibility. The National Academies report noted that researchers have to be able to understand others’ research in order to build on it.82 This chapter also notes that the ability of qualified third parties to reproduce a model using published code is important because it can reveal mistakes in model code, which can lead to serious errors in interpretation and reported results. If researchers do not share an important aspect of their study, such as their model code, it is difficult to confirm the results of their research and ultimately produce new knowledge. One agency official acknowledged the importance of releasing model code, noting that HHS could benefit by ensuring policies across the agency are consistent regarding reproducibility and transparency in modeling. By not specifically addressing reproducibility in their policy on dissemination of scientific information, CDC risks undermining the reliability of the scientific information they disseminate to the public.

MODELERS FACED SEVERAL CHALLENGES AND HAVE WORKED TO ADDRESS THEM Based on our review of documents and reports from agencies, as well as expert and agency interviews, we identified three categories of 82

National Academies of Sciences, Engineering, and Medicine, Reproducibility and Replicability in Science.

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challenges that CDC modelers and officials and ASPR modelers faced when modeling for Ebola, Zika, and pandemic influenza, along with steps they took to address the challenges. The categories are data, resources, and communicating results.83

Data Challenges According to a 2016 report from the National Science and Technology Council (NSTC), obtaining timely and accurate data and information has long been a major challenge to an effective response during an infectious disease outbreak.84 One expert described reliable data as a modeler’s most limited resource. Until data of sufficient quality and quantity are available and usable, the predictive value of models will be limited. Agency modelers and officials provided examples of data-related challenges, which we categorize as follows:85 

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Data Access. Public health data, according to one expert, often has access restrictions. For example, ASPR modelers said their ability to access data during the 2014-2016 Ebola outbreak was reduced by a need to enter into agreements with data-owning countries in order to obtain patient data. Modelers said there were agreements between CDC and data owners, but further agreements would have been required for ASPR to obtain data because the agreements did not authorize CDC to share data with its partners.86 In addition to

Within each category, officials said it may not be possible to address all challenges. While the challenges we describe herein are drawn from modeling for Ebola, Zika, and pandemic influenza, the categories of challenges we identified—data, resources, and communicating results—are likely applicable to infectious disease modeling in general. 84 National Science and Technology Council, Towards Epidemic Prediction. 85 These challenges tended to be specific to a disease or agency. 86 CDC explained that it often attempts to negotiate broader terms with respect to data sharing, but that in certain instances, the data-owning country will request that data sharing be limited to the CDC. In those instances, other agencies wanting access to data from that country would be free to pursue similar agreements. A World Health Organization official explained that, when a country provides data on outbreaks, a nuanced approach needs to be taken because countries expect their data to be responsibly managed. This official further

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the example above, the lack of data sharing agreements during the 2014-2016 Ebola outbreak response led to modeling projects being delayed, according to a CDC publication.87 ASPR modelers said their inability to obtain data without a data-sharing agreement made it challenging for them to developing a current, reliable estimate of Ebola incidence before modelers could start creating future estimates of disease incidence. They said that, as a result, they instead developed a statistical model, which provided less reliable estimates of future numbers of disease cases than they would have preferred. Modelers said they worked to address this challenge by obtaining data and indirect information through personal relationships with other modelers. In addition to the example provided above, CDC modelers and officials responding to Ebola described experiencing data access challenges. Data availability. Without sufficient data, models may be unable to identify an epidemic’s key drivers, which could result in misdirected intervention efforts.88 For example, ASPR modelers noted that during the 2015-2016 Zika outbreak response, there were substantial limits on available data, and data that were available could be unreliable and delayed. They said it was very difficult, and in many cases effectively impossible, to determine the accuracy of forecasting models for the evolving Zika outbreak. In addition, CDC officials and modelers responding to Ebola, Zika, and influenza described encountering limits on available data as an ongoing challenge. Steps that modelers said they have taken to address data availability challenges include designing models to use a minimum amount of data, building trust and communication with stakeholders who might be able to provide additional data,

explained that data cannot be provided to other countries without an arrangement with the home country. 87 M. I. Meltzer, et al., Modeling in Real Time during the Ebola Response. 88 We have previously reported on challenges related to data integration at the Department of Homeland Security’s National Biosurveillance Integration Center. These challenges are, in part, based on a lack of data. GAO, Biodefense: The Nation Faces Long-Standing Challenges Related to Defending Against Biological Threats, GAO-19-635T (Washington, D.C.: June 26, 2019).

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and updating data systems to provide all available information.89 According to CDC modelers, data availability will likely continue to pose a challenge to public health responses. Data collection. There is limited manpower during an infectious disease outbreak response, which can limit the health care system’s ability to collect data, according to CDC modelers and officials responding to Ebola and ASPR modelers.90 ASPR modelers said if a provider has to fill out a time-consuming form, then they will be delayed in treating the next patient. In order to address this challenge, CDC modelers and officials and ASPR modelers said data requesters should ask for the minimum amount of data needed. For example, CDC modelers and officials said they focus on understanding what data are essential, how they are collected, and the policy implications of reporting those data. A 2016 NSTC report recommended the federal government address this challenge by identifying questions likely to arise during an outbreak response, in order to help define and prioritize data collection and modeling goals.91 Data quality. Experts said creating models with low-quality data can result in inaccurate models that may not provide clear answers to decision maker questions. For example, CDC modelers and officials responding to the 2015-2016 Zika outbreak said the data quality varied, based on many factors such as surveillance systems that were doing different things and defining reporting Zika cases

Influenza officials explained that data may not be available at the start of a disease outbreak and different entities may be reluctant to share data. Further, information may be backfilled into the system as it becomes available, officials said. Data systems are updated weekly, according to officials, and when officials find out about a missing data source. At this time, data may be revised, but officials said, the unrevised data is also preserved. 90 CDC officials said, following a response, CDC maintains and stores data and other information including after-action reports in response-specific files—data reported through data collection systems would continue to be stored according to the data system’s considerations. Officials noted they developed an open repository of data collected from external sources in relation to Zika. In order to address data challenges, ASPR officials told us they retain all data and source code from prior analyses, including those performed for responses, which can, if applicable, be used in new analyses. However, officials said they do not aim to be an official repository for such data. 91 National Science and Technology Council, Towards Epidemic Prediction.

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differently, and the availability of diagnostic testing.92 Because of data quality concerns, there were questions about whether modeling could be conducted, but through discussions modelers and agency officials said they were able to address challenges. To address such challenges, CDC modelers and officials responding to Zika said they worked to improve public data sharing, sent an official to the Pan-American Health Organization to help interpret data and understand the outbreak from an international perspective, and used modeling methods appropriate for data with high levels of uncertainty. In addition to the example provided above, CDC modelers and officials responding to Ebola, ASPR modelers, and experts described experiencing data quality challenges. Data integration. CDC modelers and officials responding to Ebola and Zika also faced the challenge of integrating multiple data sets, which may not be standardized or in a readily usable form. For example, CDC modelers and officials responding to Zika found it challenging to integrate data as the definition of the disease was refined over time. As the definition got more specific and monitoring systems became available, it was hard to establish data trends, these officials said. Further, there were variations in who would be tested, with all people who exhibited symptoms being tested in some areas, and only pregnant women in others, and also when data would be placed into a combined form and reported to state, national, or international officials, according to these officials. This integration issue may have complicated efforts to conduct modeling such as determining the risk of microcephaly in infants over time. In order to address this challenge, Zika modelers said they set up an online data repository to, among other things, standardize shared data.

We have previously reported on the challenges faced by manufacturers of Zika diagnostic tests, including those related to research and development, testing, and regulatory approval. GAO-17-445.

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Resource-Related Challenges CDC modelers and officials responding to Ebola and Zika, along with experts, said finding staff with sufficient training to support modeling during an infectious disease outbreak represented an ongoing challenge.93 For example, CDC modelers responding to Zika said it can be difficult to find modelers with both an epidemiological background and skills in coding and mathematics.94 Modelers and agency officials said those who had the correct skills were in high demand, and it was difficult to fully engage them in the Zika outbreak response. They said they could have conducted more modeling or completed modeling efforts more rapidly if they had had access to more modelers with the right skills. To address this challenge, modelers participate in trainings on how to communicate what models can and cannot do, participate in working groups that support modeling efforts, employ the Intergovernmental Personnel Mobility Act Program, maintain collaborations with external partners, and host students and researchers.95 ASPR modelers said they faced personnel challenges in their modeling efforts but that they were wide-ranging and not specific to Ebola, Zika, or pandemic influenza.96 According to a 2016 NSTC report, time constraints make it challenging for researchers to keep up with scientific literature during an

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CDC Influenza modelers said they had previously experienced this challenge but had since built up their modeling capacity and maintained relationships with modeling groups, so it was not hard to identify additional modelers. According to CDC, many of the people who model at CDC consider themselves epidemiologists, economists or statisticians, rather than modelers. Some studied modeling as a part of their graduate work, while others acquired modeling skills from short courses offered by universities and professional organizations, or from participating in modeling projects. Intergovernmental Personnel Act agreements authorize the temporary assignment of certain employees between the federal government and state, local and Indian tribal governments, institutions of higher education and other eligible organizations, for up to 2 years, a period that may be extended. We have previously reported on strengthening the science and technology workforce as a consideration for maintaining U.S. competitiveness through transformational technological advances. GAO, Science and Technology: Considerations for Maintaining U.S. Competitiveness in Quantum Computing, Synthetic Biology, and Other Potentially Transformational Research Areas, GAO-18-656 (Washington, D.C.: Sept. 26, 2018).

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outbreak.97 CDC influenza modelers said they faced this challenge and that they conduct weekly searches for new influenza publications, which normally identify about 150 publications each week. To address this challenge, modelers said they conduct literature searches, share the responsibility of reviewing publications and informing others of their content, talk to experts, and attend conferences. Modelers said this challenge was more easily addressed than others.

Communication Challenges Communicating model results can be difficult and, as modelers and agency officials pointed out, decision makers will not give credence to results from a model they do not understand. Model results, according to CDC influenza modelers, are often nuanced and complicated, and officials have to think about what pieces of information are the most important to convey to a decision maker, the public, or health officials. Furthermore, as one expert noted, the complexities of modeling can get lost in translation, especially with the media, which may focus on only a worst-case scenario. When modeling for infectious diseases, appropriately communicating complex information has been described as a constant challenge, and CDC influenza modelers described it as their biggest challenge. CDC influenza modelers particularly noted the challenge of communicating uncertainty.98 CDC influenza and ASPR modelers said if decision makers did not understand the models, they could misunderstand the results, which, according to ASPR modelers, could lead to errors in decision making. CDC modelers and officials responding to Ebola and Zika, CDC influenza modelers, ASPR modelers, and experts described experiencing challenges communicating model results to decision makers. 97 98

National Science and Technology Council, Towards Epidemic Prediction. Scientific information typically has some level of associated uncertainty. Uncertainty can arise from many sources such as data and limits to scientific understanding. Through quantifying uncertainty, scientists can compare their results, identify factors that contribute to uncertainty and other information that may affect the results, and assess the level of confidence in the results.

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Clear communication may help prevent misunderstandings. For example, one review article said officials may not understand what models can and cannot do before an epidemic, and modelers may not be fully aware of a decision maker’s needs.99 An expert said there is a need to constrain the use of models intended to inform decisions so that the model does not over- or under-influence a decision maker. And, according to ASPR modelers, decision makers sometimes want a model to make a decision for them, although models can only inform the decision making process. They said this is less of a problem during an outbreak response, when decision makers know they have to act based on incomplete information. Some steps officials described taking to address communication challenges were similar across CDC and ASPR officials. For example, CDC modelers and officials and ASPR modelers said they took steps to improve communication, such as working to develop relationships outside of an outbreak and to improve how data are visualized. For example, ASPR modelers and officials said they provided decision makers with a website that displays an interactive influenza model known as ShinyFlu. The website lets users adjust a model to see how its results could change based on its inputs used. However, modelers said this only works if the decision maker is willing to engage with data. Other steps to address communication challenges were not discussed by all modelers we spoke to. For example, ASPR modelers said that, when they use models with high uncertainty, they do additional research to assess and communicate how a model could be misrepresenting a realworld problem. Additionally, CDC modelers responding to Zika and CDC influenza modelers said they sometimes use the language of weather forecasting—which provides information on the risk of an event occurring over a specified period of time—to help communicate model outcomes.100

B.Y. Lee, L. A. Haidari, and M.S. Lee, “Modelling during an emergency: the 2009 H1N1 influenza pandemic,” Clinical Microbiology and Infection, Vol 19, 2013. 100 CDC modelers responding to Zika described potential challenges with using weather forecasting language for infectious disease modeling, noting that individuals respond differently to weather events. 99

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For all 10 of the models we reviewed, modelers communicated all the information they had agreed to provide to decision makers, including information about model uncertainty. Agency modelers and officials said they provided this information through discussions with decision makers and by showing decision makers the results of multiple modeling situations to convey uncertainty.

CONCLUSION Infectious disease modeling is one tool that can provide decision makers with valuable information to support outbreak preparedness and response. In particular, modeling can help answer questions that are difficult to address in other ways because of practical, ethical, or financial reasons. Federal agencies have recognized the importance of modeling. CDC and ASPR reported using it to inform policy and planning questions and, to a more limited extent, to inform planning and the use of resources. HHS agencies that work on infectious disease modeling—ASPR, CDC, FDA, and NIH—reported using multiple mechanisms to coordinate their modeling efforts, including working groups, memoranda of understanding, and coordination with academic and other external modelers. The use of these mechanisms was consistent with many leading collaboration practices, such as defining and articulating a common outcome and addressing needs by leveraging resources. However, HHS does not routinely monitor and evaluate its coordination efforts, as called for by another leading collaboration practice, which limits the department’s ability to identify areas for improvement. Further, there is the potential for overlap and duplication of modeling efforts across agencies, which may not be identified if coordination efforts are not effectively being monitored, and could lead to inefficiencies. By holding progress reviews in which CDC and ASPR evaluate and report on coordination efforts for infectious disease modeling, these agencies could be better positioned to identify and address challenges prior to infectious disease outbreaks, which could lead to improved response efforts.

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CDC and ASPR modelers generally followed GAO-identified modeling practices, with the notable exception of model verification. Specifically, CDC did not make model code available to others for four of the seven CDC models we reviewed. HHS does not have a policy that requires its agencies to share model code, but it does require its component agencies to either follow its guidelines or ensure that their own guidelines include a high degree of transparency to facilitate reproducibility by qualified third parties. Without sharing code and other important information, CDC cannot ensure that its models are reproducible, a key characteristic of reliable, high-quality scientific research.

RECOMMENDATIONS FOR EXECUTIVE ACTION In order to facilitate HHS infectious disease modeling efforts, we are making two recommendations. 

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The Secretary of Health and Human Services should develop a mechanism to routinely monitor, evaluate, and report on coordination efforts for infectious disease modeling across multiple agencies. (Recommendation 1) The Secretary of Health and Human Services should direct CDC to establish guidelines that ensure full reproducibility of CDC’s research by sharing with the public all permissible and appropriate information needed to reproduce research results, including, but not limited to, model code. (Recommendation 2)

AGENCY COMMENTS AND OUR EVALUATION We provided a draft of this chapter to the Department of Health and Human Services (HHS) for review and comment. In its comments, reproduced in appendix IV, HHS agreed with our recommendations and

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noted that it was developing a process to coordinate its infectious disease modeling efforts across its components. With regard to our second recommendation—that HHS should direct CDC to establish guidelines that ensure the full reproducibility of CDC’s research by sharing all permissible and appropriate information needed to reproduce research results, including, but not limited to, model code— HHS’s comments indicated that CDC believes it has already completed actions to implement this recommendation. 



For example, the HHS comments state that CDC has established policies such as “Public Access to CDC Funded Publications” and “Policy on Public Health Research and Nonresearch Data Management and Access” that ensure that results are made available to the public, as appropriate. However, as we state in our report, these policies do not contain any reference to reproducibility, models, or provision of model code and therefore do not fully address our recommendation. CDC also said in the HHS comments that its methods—including its practice of providing a copy of model code upon request—are in line with standard practice in the scientific community and peerreviewed journals. However, in the four instances we identified where CDC modelers did not share code, code being available upon request was only one of the reasons cited. Further, this practice is inconsistent with those of the other HHS agencies we reviewed, and may limit the ability of external researchers to confirm the results of CDC’s research and ultimately produce new knowledge.

As noted in our report, by not specifically addressing reproducibility in its policies on access to data and publications, CDC risks undermining the reliability of scientific information disseminated to the public. Therefore, we did not change our recommendation in response to HHS’s comments. We did, however, revise our report to include information on other HHS agency policies related to reproducibility.

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HHS also provided technical comments, which we incorporated as appropriate. As agreed with your offices, unless you publicly announce the contents of this chapter earlier, we plan no further distribution until 30 days from the report date. At that time, we will send copies of this chapter to the appropriate congressional committees, the Secretary of Health and Human Services, and to other interested parties. Carol A. Gotway Crawford, PhD Chief Statistician Timothy M. Persons, PhD Chief Scientist and Managing Director, Science, Technology Assessment, and Analytics

APPENDIX I: OBJECTIVES, SCOPE, AND METHODOLOGY In conducting our review of infectious disease modeling by the Department of Health and Human Services (HHS) agencies, our objectives were to (1) examine the extent to which HHS has used various types of models to inform policy, planning, and resource allocation for public health decisions for selected infectious diseases, (2) examine the extent to which HHS coordinated their modeling efforts for selected infectious diseases, (3) examine the steps HHS generally took to develop and assess the performance of its models for the selected diseases and steps it applied to a selection of infectious disease models, and (4) describe the extent to which HHS has addressed challenges related to modeling for selected infectious diseases. For purposes of this review, we focused on HHS because of its focus on scientific and technical issues related to disease modeling, role in infectious disease outbreak preparedness and response activities, and use of modeling for policy and regulatory issues related to disease. Within HHS, we identified four agencies—HHS’s Office of the Assistant Secretary for

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Preparedness and Response (ASPR), the Centers for Disease Control and Prevention (CDC), National Institutes of Health (NIH), and Food and Drug Administration (FDA)—which may develop or use infectious disease models. To inform all four objectives, we selected three naturally-occurring infectious diseases that have pandemic or epidemic potential—Ebola virus disease (Ebola), Zika virus disease (Zika), and pandemic influenza—to use as examples of broader infectious disease modeling efforts. We selected these diseases based on document review, their inclusion on NIH’s pathogen priority list, modeling being conducted by HHS agencies, and interviews with experts that we selected based on their experience with infectious disease.101 Based on these steps, the team selected diseases that fit into one of the three categories on NIH’s pathogen priority list: the disease (1) can be transmitted easily from person to person, resulted in a high mortality rate and had the potential for major public health impact, might cause social disruption, and may require special action for public health preparedness (Ebola), (2) was moderately easy to disseminate, and required specific enhancements for diagnostic capacity and enhanced disease surveillance (Zika), or (3) was an emerging pathogen that could be engineered for mass dissemination in the future because of availability, ease of production and dissemination, and have the potential for high morbidity and mortality rates and major health impacts (pandemic influenza).

HHS Use of Models to Inform Policy, Planning, and Resource Allocation Decisions To examine the types of models developed by HHS agencies to inform policy, planning, and resource allocation decisions, we reviewed 101

NIH’s pathogen priority list is comprised of over 60 pathogens across the three categories. We selected experts based on judgmental sampling, conversations with individuals who work in the infectious disease or modeling field, their expertise in the area of modeling related to infectious disease, a review of papers published by the experts, and a snowball sampling to include additional experts.

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documents from 2009—the year of the last pandemic influenza outbreak in the United States—to April 2019 to identify examples of models developed by the agencies for the three selected diseases. For context on and examples of the types of modeling that CDC and ASPR have conducted, we reviewed published articles that CDC and ASPR officials and experts provided to us or cited during the course of our review, such as articles identified during interviews which we later obtained. We also obtained selected internal memoranda, when available, that described models used in the Ebola virus outbreak. We did not include FDA and NIH in this review because FDA has a limited role in modeling, and NIH generally funds, rather than conducts, modeling.102 This review yielded articles and memoranda describing about 60 CDC and ASPR models. See appendix II for a bibliography of model publications reviewed. We then categorized the models using categories derived from a federal working group report to characterize the types of modeling conducted and the purpose of the modeling, when that purpose was identified. To analyze each study, one analyst initially coded each study, and each classification was then independently reviewed to verify that it had been correctly classified and to resolve any categorization discrepancies. We used these categories to describe types of modeling efforts undertaken by HHS agencies. Because we focused on studies published between 2009 and 2019, our findings are not generalizable to models that were developed outside of that time period. Additionally, because we relied on agency officials or reviews of relevant agency documents and publications to identify studies, we may not have captured all studies relevant to our scope. Further, because CDC and ASPR modelers and officials said that they do not publish every model they conduct, our review was not intended to develop an inventory of the modeling conducted during the time period. Therefore, we were unable to determine the extent to which the models we identified represented agency modeling efforts as a whole. To describe the extent of model use for public health decision making, we interviewed officials from HHS agencies identified as decision makers 102

FDA had limited modeling activity, specifically related to impacts to the pool of blood donors during the Zika outbreak response. NIH funds modeling outside the agency.

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for conducting the response to these selected diseases—CDC, ASPR, and FDA—and officials who conducted the modeling.103 We also interviewed two NIH institutes and one center about funding for research related to modeling for the selected diseases. Additionally, we conducted semistructured interviews of officials from five states concerning their use of models prepared by HHS agencies for decision making, among other topics. We selected these states based on a review of a CDC draft report on states’ use of CDC models, on the level of influenza activity experienced by states, and consideration of geographic variation by U.S. region. During our review, we sought to identify the common types of decisions that could be informed by models, as well as the considerations that could impact the extent to which a decision maker requests and uses models for specific types of decisions. Based on interviews with agency officials and our review of HHS models we identified examples of models that were used to make specific decisions during response and non- response times. Because we relied on officials to describe the extent to which models inform decision making, we may not have captured all relevant instances when models for the selected infectious diseases informed decision makers.

HHS Coordination of Modeling Efforts To examine coordination and collaboration across HHS agencies, we reviewed documents describing HHS agencies’ collaboration and coordination mechanisms such as Memoranda of Understanding, descriptions of Emergency Operations Center procedures, and after- action reports following infectious disease outbreaks. We also conducted interviews with and requested information from HHS officials, asking them to provide information on their efforts to coordinate their infectious disease modeling activities. In this chapter, and in our past work, we define coordination broadly as any joint activity that is intended to produce more public value than could be produced when organizations act alone. We 103

For purposes of this review, we attribute comments from HHS officials to modelers, officials, or modelers and officials.

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compared these actions to relevant selected collaboration leading practices:104      

define and articulate a common outcome; establish mutually reinforcing or joint strategies; identify and address needs by leveraging resources; agree on roles and responsibilities; establish compatible policies, procedures, and other means to operate across agency boundaries; and develop mechanisms to monitor, evaluate, and report on results.

Because we judgmentally selected a group of experts and diseases, the results of our review cannot be generalized to HHS coordination efforts for other infectious diseases. However, our assessment of collaboration and coordination activities did cover modeling efforts for the three selected diseases.

Developing Infectious Disease Models and Assessing Their Performance To identify steps that are generally considered when modelers develop infectious disease models and assess their performance, we conducted semi-structured interviews with relevant experts from academia and other organizations and CDC and ASPR officials, and reviewed literature identified by experts. We used a snowball sampling approach to identify relevant experts and groups. We initially identified five infectious disease modeling experts 104

GAO, Results-Oriented Government: Practices That Can Help Enhance and Sustain Collaboration among Federal Agencies, GAO-06-15 (Washington, D.C.: Oct. 21, 2005). We excluded the remaining leading practices—reinforce individual accountability for collaborative efforts through performance management system and reinforce agency accountability for collaborative efforts through agency plans and reports—because performance management and a review of agency strategic planning documents fell outside of the scope of our review.

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through informal conversation with individuals working in the field, infectious disease modeling experts known through GAO work, as well as a review of websites, publications, and grants funded by NIH. Using a snowball sampling approach, we reviewed key literature related to the steps generally taken to develop models and assess their performance, consulted with infectious disease modeling experts, and interviewed agency officials to identify relevant groups, as well as individual experts, who could convey to us the steps generally taken during infectious disease modeling. Through literature searches, the team identified literature from public health journals or other major sources. The team applied personal background and knowledge in public health, infectious disease modeling, and statistics to help identify key sources. For the selected literature, we reviewed references and used a snowball approach to identify further relevant studies. Finally, we reviewed CDC guidance on decision making for data access and long-term preservation as it related to documentation standards. Based on our review of identified literature, we developed a data collection instrument to assess the extent to which CDC and ASPR used the steps for infectious disease model development identified by experts and in the literature.105 Through this data collection instrument, we gathered information about the elements of developing and assessing model performance and the steps that could be taken within each element. In order to develop the data collection instrument, based on our review of literature, we mapped out steps to develop and assess model performance, and developed broad categories of assessment elements. Within each assessment element, we included steps modelers could take as a part of each assessment element. For example, the data collection instrument included items that recorded model verification steps that might have been taken by modeler(s) within the broader model verification element. The instrument was reviewed by internal stakeholders, who provided feedback on its content. Prior to sending the data collection instrument to the agency, 105

We did not include NIH and FDA in this review because NIH funds, rather than conducts modeling, and FDA has a limited role in modeling for our selected diseases.

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we filled in information on verification steps taken for each of the 10 selected models, based on provided model documentation to reflect steps we determined modelers took as a part of the model development and assessment process. In order to provide officials with this information, two analysts reviewed each model’s documentation, with one analyst providing an initial coding of the model and the other reviewing and verifying the first analyst’s findings. This method was first tested on one of the 10 selected models by two analysts independently coding information from the model’s documentation into the data collection instrument and then reviewing coding choices to reconcile any differences found. We then sent the instruments with filled-in information to CDC and ASPR modelers to receive their feedback concerning the steps taken to develop models and assess their performance, provide any missing information, and resolve any ambiguities. See Appendix III for a list of the 10 selected models reviewed and steps to develop and assess model performance included in the data collection instrument. The data collection instrument was intended to record whether a specific step had been taken, but did not assess the quality of the modeling steps. In order to determine steps CDC and ASPR took to develop and assess its models, we selected a non-generalizable sample of 10 models for review in our data collection instrument that demonstrated steps that HHS agencies took to develop models and assess their performance. The model selection process described above informed our selection of infectious disease models. To be selected for inclusion in our non- generalizable sample, the model had to be (1) developed by CDC, or ASPR officials or contractors; (2) developed to answer a question about Ebola, Zika, or pandemic influenza; and (3) used to inform public health decision makers during an outbreak or for preparedness activities. We selected 10 models that differed in form and answered different types of questions, which included studies prepared during both outbreak preparedness and response times, and covered topics such as the impact of vaccination programs on deaths and hospitalization. For Ebola and Zika, we focused on review of selected papers or memos produced since 2014 in order to capture the time period following the 2014-2016 Ebola and 2015-2016 Zika outbreaks. For

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pandemic influenza, we focused on papers and memos produced since 2009, when the H1N1 pandemic occurred in the United States. Because we selected from a group of models identified by HHS modelers and officials for Ebola, Zika, and pandemic influenza, the results of our review cannot be generalized to other diseases outside of the scope of this chapter. Furthermore, we requested models that informed public health decision making, and did not consider models that were not used for this purpose. Because we reviewed a non-generalizable sample of 10 models, the results of our review cannot be generalized to a larger population of models prepared by HHS agencies.

Challenges to Effective Modeling To identify challenges associated with modeling for the selected infectious diseases, we reviewed documents and reports to identify modeling challenges and steps to address those challenges, and interviewed agency officials and modelers, and experts identified through the previously-described snowball sampling methodology. We used semistructured interview protocols that included open-ended questions about challenges associated with infectious disease modeling and limitations associated with model development. Not all officials and experts we interviewed provided comments on every challenge or limitation. In addition, because we judgmentally selected a group of experts and diseases, the results of our review cannot be generalized to all infectious disease modeling efforts. We conducted this performance audit from May 2018 to May 2020, in accordance with generally accepted government auditing standards. These standards require that we plan and perform the audit to obtain sufficient, appropriate evidence to provide a reasonable basis for our findings and conclusions based on our audit objectives. We believe the evidence obtained provides a reasonable basis for our findings and conclusions based on our audit objectives.

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APPENDIX II: BIBLIOGRAPHY OF SELECTED MODEL PUBLICATIONS REVIEWED Ebola Models Carias, Cristina, et al. “Preventive Malaria Treatment for Contacts of Patients with Ebola Virus Disease in the Context of the West Africa 2014- 15 Ebola Virus Disease Response: An Economic Analysis.” The Lancet Infectious Diseases, vol. 16, no. 4 (April 2016): pp. 449-458. Christie, Athalia, et al. “Possible Sexual Transmission of Ebola Virus— Liberia, 2015.” Morbidity and Mortality Weekly Report, vol. 64, no. 17 (May 8, 2015): pp. 479-481. Martin I. Meltzer, et al. “Estimating the Future Number of Cases in the Ebola Epidemic - Liberia and Sierra Leone, 2014-2015.” Morbidity and Mortality Weekly Report, vol. 63, no. 3 suppl. (September 26, 2014): pp. 1-14. Meltzer, Martin I., et al. “Modeling in Real Time during the Ebola Response.” Morbidity and Mortality Weekly Report, vol. 65, no. 3 suppl. (July 8, 2016): pp. 85-89. Rainisch, Gabriel, et al. “Estimating Ebola Treatment Needs, United States.” Emerging Infectious Diseases, vol. 21, no. 7 (July 2015): pp. 1273-1275. Rainisch, Gabriel, et al. “Regional Spread of Ebola Virus, West Africa, 2014.” Emerging Infectious Diseases, vol. 21, no. 3 (March 2015): pp. 444-447. Undurraga, Eduardo A., Cristina Carias, Martin I. Meltzer, Emily B. Kahn. “Potential for Broad-Scale Transmission of Ebola Virus Disease during the West Africa Crisis: Lessons for the Global Health Security Agenda.” Infectious Diseases of Poverty, vol. 6, no. 159 (2017). Washington, Michael L., Martin I. Meltzer. “Effectiveness of Ebola Treatment Units and Community Care Centers Liberia, September 23October 31, 2014.” Morbidity and Mortality Weekly Report, vol. 64, no. 3 (January 30, 2015): pp. 67-69.

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Zika Models Adamski, Alys, et al. “Estimating the Numbers of Pregnant Women Infected with Zika Virus and Infants with Congenital Microcephaly in Colombia, 2015–2017.” Journal of Infection, vol. 76 (2018): pp. 529535. Dirlikov, Emilio, et al. “Guillain-Barré Syndrome and Healthcare Needs during Zika Virus Transmission, Puerto Rico, 2016.” Emerging Infectious Diseases, vol. 23, no. 1 (January 2017): pp.134-136. Ellington, Sascha R., et al. “Estimating the Number of Pregnant Women Infected With Zika Virus and Expected Infants With Microcephaly Following the Zika Virus Outbreak in Puerto Rico, 2016.” JAMA Pediatrics, vol. 170, no. 10 (2016): pp. 940-945. Grills, Ardath, et al. “Projected Zika Virus Importation and Subsequent Ongoing Transmission after Travel to the 2016 Olympic and Paralympic Games—Country-Specific Assessment, July 2016.” Morbidity and Mortality Weekly Report, vol. 65, no. 28 (July 22, 2016): pp.711-715. Johansson, Michael A., et al. “Zika and the Risk of Microcephaly.” The New England Journal of Medicine, vol. 375 (July 7, 2016): pp.1-4. Johnson, Tammi L., et al. “Modeling the Environmental Suitability for Aedes (Stegomyia) aegypti and Aedes (Stegomyia) albopictus (Diptera: Culicidae) in the Contiguous United States.” Journal of Medical Entomology, vol. 54, no. 6 (November 7, 2017): pp. 1605-1614. Mitchell, Patrick K. et al., “Reassessing Serosurvey-Based Estimates of the Symptomatic Proportion of Zika Virus Infections.” American Journal of Epidemiology, vol. 188, no. 1 (January 2019): pp. 206-213. Mier-y-Teran-Romero, Luis, Mark J. Delorey, James J. Sejvar, Michael A. Johansson. “Guillain-Barré Syndrome Risk Among Individuals Infected with Zika Virus: a Multi-Country Assessment.” BMC Medicine, vol. 16, no. 67 (2018). Mier-y-Teran-Romero, Luis, Andrew J. Tatem, Michael A. Johansson. “Mosquitoes on a Plane: Disinsection Will Not Stop the Spread of

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Vector- Borne Pathogens, a Simulation Study.” PLoS Neglected Tropical Diseases, vol. 11, no. 7 (July 3, 2017). Reefhuis, Jennita, et al. “Projecting Month of Birth for At-Risk Infants after Zika Virus Disease Outbreaks.” Emerging Infectious Diseases, vol. 22, no. 5 (May 2016): pp. 828-832. Russell, Steven, et al. “Detecting Local Zika Virus Transmission in the Continental United States: A Comparison of Surveillance Strategies.” PLoS Currents Outbreaks (November 22, 2017). Watts. Alexander G., et al. “Elevation as a Proxy for Mosquito-Borne Zika Virus Transmission in the Americas.” PLoS ONE, vol. 12, no. 5 (May 24, 2017).

Influenza Models106 Atkins, Charisma Y., et al. “Estimating Effect of Antiviral Drug Use during Pandemic (H1N1) 2009 Outbreak, United States.” Emerging Infectious Diseases, vol. 17. no. 9 (September 2011): pp. 1591-1598. Biggerstaff, Matthew, et al. “Estimates of the Number of Human Infections With Influenza A(H3N2) Variant Virus, United States, August 2011– April 2012.” Clinical Infectious Diseases, vol. 57, suppl. 1 (2013): pp. S12-S15. Biggerstaff, Matthew, et al. “Estimating the Potential Effects of a Vaccine Program Against an Emerging Influenza Pandemic—United States.” Clinical Infectious Diseases, vol. 60, suppl. 1 (2015): pp. S20-S29. Carias, Cristina, et al. “Potential Demand for Respirators and Surgical Masks during a Hypothetical Influenza Pandemic in the United States.” Clinical Infectious Diseases, vol. 60, suppl. 1 (2015): pp. S42S51.

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While only pandemic influenza is within the scope of this audit, CDC officials said they use seasonal influenza modeling to prepare for a pandemic and that seasonal models can be used in a pandemic. Therefore, we are including seasonal models in our review of pandemic influenza modeling.

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Cauchemez, Simon, et al. “Role of Social Networks in Shaping Disease Transmission during a Community Outbreak of 2009 H1N1 Pandemic Influenza.” Proceedings of the National Academy of Sciences of the United States, vol. 108, no. 7 (February 15, 2011): pp. 2825-2830. Dawood, Fatimah S., et al. “Estimated Global Mortality Associated with the First 12 Months of 2009 Pandemic Influenza A H1N1 Virus Circulation: a Modelling Study.” The Lancet Infectious Diseases, vol. 12 (September 2012): pp. 687-695. Fung, Isaac Chun-Hai, et al. “Modeling the Effect of School Closures in a Pandemic Scenario: Exploring Two Different Contact Matrices.” Clinical Infectious Diseases, vol. 60, suppl. 1 (2015): pp. S58-S63. Iuliano, A. Danielle, et al. “Estimates of Global Seasonal InfluenzaAssociated Respiratory Mortality: a Modelling Study.” The Lancet, vol. 391, no. 10127 (March 31, 2018): pp. 1285-1300. Jain, Seema, et al. “Hospitalized Patients with 2009 H1N1 Influenza in the United States, April–June 2009.” The New England Journal of Medicine, vol. 361, no. 20 (November 12, 2009): pp. 1935-1944. Kostova, Deliana, et al. “Influenza Illness and Hospitalizations Averted by Influenza Vaccination in the United States, 2005–2011.” PLoS ONE, vol. 8, no. 6 (June 19, 2013). Lafond, Kathryn E., et al. “Global Role and Burden of Influenza in Pediatric Respiratory Hospitalizations, 1982–2012: A Systematic Analysis.” PLoS Medicine, vol. 13, no. 3 (March 24, 2016). Meltzer, Martin I., Nancy J. Cox, Keiji Fukuda. “The Economic Impact of Pandemic Influenza in the United States: Priorities for Intervention.” Emerging Infectious Diseases, vol. 5, no. 5 (September-October 1999): pp. 659-671. Meltzer, Martin I., et al. “Estimates of the Demand for Mechanical Ventilation in the United States during an Influenza Pandemic.” Clinical Infectious Diseases, vol. 60, suppl. 1 (2015): pp. S52-S57.

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O’Hagan, Justin J., et al. “Estimating the United States Demand for Influenza Antivirals and the Effect on Severe Influenza Disease during a Potential Pandemic.” Clinical Infectious Diseases, vol. 60, suppl. 1 (2015): pp. S30-S41. Presanis, Anne M., et al. “The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis.” PLoS Medicine, vol. 6, no. 12 (December 8, 2009). Reed, Carrie, et al. “Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April-July 2009.” Emerging Infectious Diseases, vol. 15, no. 12 (December 2009): pp. 2004-2007. Reed, Carrie, Martin I. Meltzer, Lyn Finelli, Anthony Fiore. “Public Health Impact of Including Two Lineages of Influenza B in a Quadrivalent Seasonal Influenza Vaccine.” Vaccine, vol. 30 (2012): pp. 1993-1998. Reed, Carrie, et al. “Estimating Influenza Disease Burden from PopulationBased Surveillance Data in the United States.” PLoS ONE, vol. 10, no. 3 (March 4, 2015). Rolfes, Melissa A., et al. “Annual Estimates of the Burden of Seasonal Influenza in the United States: A Tool for Strengthening Influenza Surveillance and Preparedness.” Influenza and Other Respiratory Viruses, vol. 12 (2018): pp. 132-137. Russell, K., et al. “Utility of State-Level Influenza Disease Burden and Severity Estimates to Investigate an Apparent Increase in Reported Severe Cases of Influenza A(H1N1) pdm09 – Arizona, 2015–2016.” Epidemiology and Infection, vol. 146 (June 14, 2018): pp. 1359-1365. Shrestha, Sundar S., et al. “Estimating the Burden of 2009 Pandemic Influenza A (H1N1) in the United States (April 2009–April 2010).” Clinical Infectious Diseases, vol. 52, suppl. 1 (2011): pp. S75-S82. Tokars, Jerome I., Melissa A. Rolfes, Ivo M. Foppa, Carrie Reed. “An Evaluation and Update of Methods for Estimating the Number of Influenza Cases Averted by Vaccination in the United States.” Vaccine, vol. 36 (2018): pp. 7331-7337.

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APPENDIX III: TEN SELECTED INFECTIOUS DISEASE MODELS AND QUESTIONS FROM DATA COLLECTION INSTRUMENT Table 5. Documents describing models evaluated in our data collection instrument by agency and disease Agency and disease Centers for Disease Control and Prevention (CDC) Ebola Models

Office of the Assistant Secretary for Preparedness and Response (ASPR) Ebola Model CDC Zika Models

Document describing model Meltzer, Martin I., Charisma Y. Atkins, Scott Santibanez, Barbara Knust, Brett W. Petersen, Elizabeth D. Ervin, Stuart T. Nichol, Inger K. Damon, Michael L. Washington. Estimating the Future Number of Cases in the Ebola Epidemic–Liberia and Sierra Leone, 2014-2015, MMWR. Volume 63, Number 3, September 26, 2014. Rainisch, Gabriel, Manjunath Shankar, Michael Wellman, Toby Merlin, and Martin I. Meltzer. Regional Spread of Ebola Virus, West Africa, 2014. Emerging Infectious Diseases. Volume 21, Number 3, March 2015. Asher, Jason. Forecasting Ebola with a Regression Transmission Model. Epidemics. Volume 22, 2018.

Ellington, Sascha R., Owen Devine, Jeanne Bertolli, Alma Martinez Quiñones, Carrie K. Shapiro-Mendoza, Janice PerezPadilla, Brenda Rivera-Garcia, Regina M. Simeone, Denise J. Jamieson, Miguel Valencia-Prado, Suzanne M. Gilboa, Margaret A. Honein, Michael A. Johansson. Estimating the Number of Pregnant Women Infected With Zika Virus and Expected Infants With Microcephaly Following the Zika Virus Outbreak in Puerto Rico, 2016. JAMA Pediatrics. Volume 170, Number 10, October 2016. Johansson, Michael A., Luis Mier-y‐Teran-Romero, Jennita Reefhuis, Suzanne M. Gilboa, and Susan L. Hills. Zika and the Risk of Microcephaly. New England Journal of Medicine. Volume 375, Number 1, July 7, 2016.

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Agency and disease ASPR Zika Model

Document describing model Dirlikov, Emilio, Krista Kniss, Chelsea Major, Dana Thomas, Cesar A. Virgen, Marrielle Mayshack, Jason Asher, Luis Mier-yTeran-Romero, Jorge L. Salinas, Daniel M. Pastula, Tyler M. Sharp, James Sejvar, Michael A. Johansson, Brenda RiveraGarcia. Guillain-Barré Syndrome and Healthcare Needs during Zika Virus Transmission, Puerto Rico, 2016. Emerging Infectious Diseases. Volume 23, Number 1, January 2017. CDC Pandemic Influenza Biggerstaff, Matthew, Carrie Reed, David L. Swerdlow, Manoj Models Gambhir, Samuel Graitcer, Lyn Finelli, Rebekah H. Borse, Sonja A. Rasmussen, Martin I. Meltzer, Carolyn B. Bridges. Estimating the Potential Effects of a Vaccine Program against an Emerging Influenza Pandemic—United States, Clinical Infectious Diseases. Volume 60, Issue Supplement 1, 2015. Carias, Cristina, Gabriel Rainisch, Manjunath Shankar, Bishwa B. Adhikari, David L. Swerdlow, William A. Bower, Satish K. Pillai, Martin I. Meltzer, Lisa M. Koonin. Potential Demand for Respirators and Surgical Masks during a Hypothetical Influenza Pandemic in the United States. Clinical Infectious Disease. Volume 60, Issue Supplement 1, 2015. Reed, Carrie, Frederick J. Angulo, David L. Swerdlow, Marc Lipsitch, Martin I. Meltzer, Daniel Jernigan, and Lyn Finelli. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009, Emerging Infectious Diseases. Volume 15, Number 12, December 2009. ASPR Pandemic Influenza Asher, Jason, Matthew Clay. Deterministic compartmental Model models for influenza with mitigations. R: “flumodels” package. Version: 1.0.7, April 24, 2017. Source: GAO analysis of agency and other identified documents | GAO-20-372.

DATA COLLECTION INSTRUMENT GAO Review of Model Assessment Steps for Selected Agency Models Purpose The Government Accountability Office has been asked by the Congress to review the Department of Health and Human Services’ agency

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efforts to model infectious disease. As part of our methodology, we selected and reviewed published papers and internal memoranda from the sources provided to us. We reviewed these sources to describe the steps taken to describe, verify, validate, and communicate results of these modeling efforts. The purpose of this inquiry is to provide the authors of the selected papers the opportunity to confirm, clarify, or provide additional information in the table below.

Instructions In the table below, we have two sets of columns: one set indicating GAO’s assessment of whether the document contained information about a step being taken. The second set of columns is for the authors of the selected paper to fill out. If you agree with information in the GAO columns, please indicate your concurrence in the Reviewer Comments column. Otherwise, please provide information accordingly. If a step is marked “Step taken” please review the entries we have made in the GAO Reviewer Comments column for accuracy and completeness and indicate your concurrence in the Reviewer Comments column. Please also provide additional supporting documentation if available. For any steps that were taken, but where we indicated either “not taken” or “not enough information to determine” in our review, please provide a description of the actual steps and any documentation you may have. If a step was not taken, please provide an indication as to why that step was not taken and, if possible, please provide supporting documentation. For example, if limited data availability impacted the ability to conduct a model validation step(s), then please include this information in the appropriate table cells. In the table below, we reviewed the following: [first author, paper name, year] Please indicate the names of staff who completed this form:

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ASSESSMENT ELEMENT Clarify Objectives 1) Communication between decision maker or model requestor and model developer to establish clear understanding of model question(s), limitations, etc. 2) Question the model is designed to answer reflects the question posed

Model Description 3) Model assumptions are stated, for example:  Population characteristics  Transmission factors/equations  Statistical/distributional (frequentist/Bayesian)  Other 4) Model limitations are stated 5) Model inputs are defined (conceptual and operational definitions of variables) 6) Model outputs are described 7) Type of model used is stated (phenomenological, mechanistic, regression, simulation, deterministic SIR, etc.) 8) Underlying equation(s) or algorithms are included 9) Software/programming language used is stated

Model Verification (Internal Validation, Internal Consistency, Technical Validity) 10) Independent expert (internal or external) review of key programming choices and approaches

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11) Debugging tests and checks for coding accuracy 12) Model’s code or Excel spreadsheet is available 13) Test model assumptions (i.e., confirming model assumptions are reasonable and appropriate for question), for example:  Distributional assumptions about model residuals  Form of the model 14) Model handling of input data/parameters is verified as correct (i.e., as intended by developers) 15) Other

Model Validation 16) Sensitivity analysis (assessing impact of assumption/parameter uncertainty on output or model form) 17) Cross validation or between model comparisons: Compare results to other models that address the same problem 18) External validation: Compare model results to actual event data 19) Predictive validation: Compare model predictions for future events to actual outcomes. 20) Other

Communication107 21) Modelers supply customer with agreed upon information, which may vary depending on the model 22) Modeler provides customer with clear information on uncertainty in model results, such as inclusion of standard errors or confidence

107

Our discussion on developing and assessing model performance combines clarifying objectives and communicating results. We provide a further discussion of agency officials’ answers to questions about communication, as it relates to supplying customers with a model’s results in our discussion of communication challenges.

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United States Government Accountability Office intervals, or qualitative explanations of uncertainty in the model results

Assessment Steps Question Do you think that the assessment elements identified in the table above sufficiently reflect the steps that should generally be taken to develop and assess the performance of models? Would you remove any steps, add any steps, or make any other adjustments to these steps in order to consider them best practices in assessing performance of models, generally? Please explain.

APPENDIX IV: COMMENTS FROM THE DEPARTMENT OF HEALTH AND HUMAN SERVICES

Infectious Disease Modeling

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United States Government Accountability Office

APPENDIX V: ACCESSIBLE DATA Data Tables Accessible data for Figure 1: Timeline of Ebola outbreaks since 2014 2014-16 West African Outbreak   



 



March 23, 2014: WHO declares an Ebola outbreak in West Africa after 49 confirmed cases and 29 deaths. July 2014: the outbreak has spread to the capitals of Guinea, Liberia, and Sierra Leone. CDC activates its EOC to help coordinate activities with partners. August 2014: the outbreak becomes the largest since Ebola was first discovered. WHO declares the outbreak a Public Health Emergency of International Concern.108 September 2014: CDC and ASPR advise hospitals to prepare for potential of persons infected with Ebola traveling to U.S. and distribute preparedness checklist. CDC confirms first travel- associated case of Ebola in Dallas, Texas, and releases projection of 1.4 million cases in Liberia and Sierra Leone by January 2015, if Ebola spread continues at same rate.109 October 2014: a medical aid worker who volunteered in Guinea is hospitalized in New York City with Ebola, the individual recovered.110 July 1, 2015: CDC and ASPR launch National Ebola Training and Education Center, expanding on efforts to ensure facilities maintain readiness to care for U.S. patients. March 29, 2016: WHO lifts its Public Health Emergency of International Concern status, signaling an end to the West African Ebola outbreak.111

2014 DRC Outbreak 

108

August 24, 2014: DRC notifies WHO of an Ebola outbreak. This outbreak is unrelated to the West African outbreak.

According to WHO, a Public Health Emergency of International Concern is an event which constitutes a public health risk to other countries through the international spread of disease and potentially requires a coordinated international response. 109 This patient later died. Two health care workers who cared for the patient tested positive for Ebola, both recover. 110 Seven other people were cared for in the U. S. after being exposed to the virus and becoming ill in West Africa. Six recovered, one died. 111 CDC estimates this outbreak resulted in over 28,600 total cases of Ebola and 11,325 deaths.

Infectious Disease Modeling 

251

(H) November 21, 2014: WHO declares an end to the DRC outbreak.112

2017 DRC Outbreak  

(I) May 11, 2017: DRC notifies international public health agencies of a cluster of suspected cases of Ebola.113 (J) July 2, 2017: WHO declares an end to the DRC outbreak.

2018 DRC Outbreak  

(K) May 8, 2018: DRC declares an Ebola outbreak after two cases are confirmed by laboratory testing. (L) July 24, 2018: WHO declares an end to the DRC outbreak.114

2018- Present DRC Outbreak  



(M) August 1, 2018: the DRC reports an outbreak of Ebola. (N) June 2019: On June 11, the Ugandan Ministry of Health confirms their first imported case of Ebola from the DRC.115 On June 13, CDC activates its EOC to support the interagency outbreak response. (O) July 17, 2019: WHO declares the outbreak a Public Health Emergency of International Concern.116 The outbreak is ongoing in eastern DRC.

Abbreviations ASPR Office of the Assistant Secretary for Preparedness and Response CDC Centers for Disease Control and Prevention DRC Democratic Republic of the Congo EOC Emergency Operations Center WHO World Health Organization Source: CDC and WHO | GAO-20-372.

112

There are 66 cases and 49 deaths. Ebola was first identified in the DRC in 1976. There are eight suspected cases, including two deaths. On May 12, a third death is reported. 114 There are 54 total cases and 33 deaths. 115 Since June 12, 2019, no additional cases have been reported in Uganda. 116 This is the DRC’s largest Ebola outbreak, and the second largest Ebola outbreak recorded. 113

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United States Government Accountability Office

Accessible data for Figure 2: Office of the Assistant Secretary for Preparedness and Response’s (ASPR) visualization hub Office of the Assistant Secretary for Preparedness and Response (ASPR) Visualization Hub ASPR’s Visualization Hub can be used by the government in planning and response for outbreaks of pandemic influenza or other emerging infectious diseases, among other things. According to ASPR modelers and officials, the Hub is comprised of floor-to-ceiling television monitors arrayed in 4/5 of a circle in the room. According to modelers and officials, the main uses are for situational awareness, analytics, and “big data” insights, using scalable tools, dashboards, visualizations, and simulated training environments for analysts, decision makers, and project officers. The Visualization Hub can be used to conduct virtual exercises and training simulations, as well as exploring model sensitivities

Accessible data for Figure 3: Timeline of data availability for models compared to usefulness of modeling during an outbreak Timeline  Detection  Patient Zero  Early Response  When information from models could be most helpful  Early response actions could include:  Mobilization of personnel and resources  Drafting/signing agreements for data sharing  Intervention  Intervention actions could include:  Quarantine of infected persons  Application of vaccines to non-infected persons  Intervention – Post-intervention  Last recorded case  When models based on the outbreak are most accurate  Post-Intervention  End of outbreak declared  Post-intervention actions could include:  Assessment of response in formal reports  Validation of infectious disease models Source: GAO analysis of documents. | GAO-20-372.

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Accessible data for Figure 4: Outline of process to develop models and assess their performance Process Chart 1. Communication between decision maker and modeler a) Reality: Health care decision, intervention and disease biology b) Conceptualizing the problem c) Model Factors: Is decision/problem quantifiable? What are? i. Assumptions ii. Limitations iii. Data availability d) Conceptualizing the model 2. Description of your model a) Model choice b) Model input 3. Verification a) Your model 4. Validation a) Model output b) Data sources i. Other models ii. Real-world data iii. Withheld data Source: GAO analysis of peer-reviewed literature and expert interviews. | GAO-20-372.

INDEX A access, 8, 19, 22, 23, 36, 53, 60, 62, 111, 112, 113, 114, 184, 206, 213, 218, 220, 224, 229, 235 agencies, vii, 24, 27, 31, 36, 43, 52, 65, 70, 79, 99, 105, 107, 110, 111, 143, 155, 166, 167, 169, 170, 171, 172, 173, 174, 175, 182, 189, 191, 194, 195, 197, 199, 200, 201, 202, 205, 206, 207, 209, 211, 212, 214, 217, 218, 219, 220, 227, 228, 229, 230, 231, 232,233, 236, 251 artificial intelligence, 9, 17, 19, 21, 30, 69, 88 automotive sector, 6 avian, 18, 19, 20, 21, 31, 181 avian influenza, 31, 181

B behaviors, 13, 17, 24, 27, 183 belief systems, 97 biochemistry, 54 biodiversity, 106

biological processes, 183 biotechnology, 60 blood, 149, 171, 175, 178, 179, 206, 232 blood safety, 175 blood supply, 175 blood transfusion, 179

C chain of command, 100 challenges, vii, viii, 39, 52, 74, 76, 84, 85, 89, 104, 112, 113, 155, 156, 166, 167, 169, 173, 191, 198, 207, 208, 220, 221, 222, 223, 224, 225, 226, 227, 230, 237, 247 children, 23, 52, 53, 85, 143, 179 China, 4, 5, 6, 8, 14, 15, 18, 20, 37, 38, 42, 49, 51, 78, 84, 86, 90, 105, 109, 112, 116, 125, 126, 130, 140, 148, 154, 181 Chinese government, 6, 8, 109, 116 Chinese medicine, 149 climate change, 54, 105, 106, 107, 108, 146 clinical trials, 45, 47, 50, 70, 72, 98, 99, 130, 188

256

Index

collaboration, 12, 13, 29, 46, 49, 62, 104, 106, 113, 115, 129, 172, 197, 198, 199, 200, 201, 202, 205, 207, 227, 233, 234 communication, 57, 62, 64, 67, 73, 84, 140, 167, 209, 211, 216, 221, 226, 247 complexity, 43, 71, 109, 169, 216 complications, 92, 143, 180 Congress, iv, vii, 1, 7, 14, 15, 19, 21, 43, 44, 54, 70, 73, 81, 97, 99, 100, 166, 169, 244 conspiracy, 12, 56, 62, 125, 129, 151 constituents, 18, 20, 73, 74, 76, 94 coordination, 17, 105, 129, 166, 167, 172, 195, 197, 198, 199, 202, 203, 205, 206, 208, 227, 228, 233, 234 coronavirus, vii, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 16, 18, 19, 20, 21, 22, 24, 26, 27, 36, 37, 41, 42, 45, 46, 49, 50, 53, 68, 70, 73, 76, 83, 84, 92, 93, 95, 96, 98, 99, 100, 101, 102, 124, 125, 126, 127, 128, 129, 130, 132, 134, 135, 139, 140, 141, 144, 146, 147, 148, 150, 151, 152, 154, 156, 168, 181 cures, 11, 51, 56, 62, 63, 72, 124, 126, 129

D data analysis, 187 data availability, 191, 221, 245, 252 data collection, 65, 222, 235, 236, 243 data mining, 43 data processing, 33 death rate, 7, 8, 18, 20, 100, 105, 106, 112 deaths, 4, 5, 7, 18, 20, 37, 45, 59, 73, 76, 140, 148, 154, 168, 177, 178, 180, 186, 196, 236, 250, 251 decision makers, vii, 39, 166, 169, 184, 185, 186, 187, 190, 193, 194, 196, 211, 216, 225, 226, 227, 232, 236, 252 Democratic Republic of Congo, 55, 60, 62 Department of Defense, 189 Department of Energy, 152, 155, 156, 214

Department of Health and Human Services (HHS), vii, viii, 43, 114, 140, 141, 142, 143, 144, 166, 167, 170, 171, 172, 173, 174, 176, 184, 185, 187, 188, 189, 195, 197, 198, 199, 201, 202, 206, 207, 208, 209, 217, 218, 219, 227, 228, 229, 230, 231, 232, 233, 234, 236, 244, 248 Department of Homeland Security, 170, 174, 196, 221 Department of Transportation, 174, 189 detection, 10, 24, 25, 27, 28, 29, 33, 34, 35, 36, 38, 40, 98, 105, 110 disease activity, 194 disease control, viii, 4, 5, 7, 8, 15, 18, 20, 32, 39, 128, 129, 144, 148, 165, 166, 167, 168, 169, 170, 178, 179, 189, 199, 200, 211, 214, 215, 218, 231, 243, 251 disease distribution, vii, 166 disease model, viii, 112, 113, 166, 169, 170, 172, 175, 176, 181, 182, 184, 186, 195, 197, 199, 200, 201, 204, 205, 206, 208, 210, 216, 220, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 237, 252 disease progression, 35 diseases, vii, viii, 2, 3, 4, 10, 11, 15, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 38, 39, 40, 45, 48, 51, 53, 58, 60, 68, 74, 80, 86, 106, 108, 109, 110, 112, 114, 116, 126, 142, 154, 155, 166, 168, 170, 171, 172, 173, 174, 175, 178, 181, 183, 186, 187, 189, 191, 192, 193, 197, 201, 204, 205, 206, 225, 230, 231, 232, 233, 234, 235, 237, 252 distribution, vii, 166, 190, 230 drugs, 13, 17, 19, 21, 69, 93, 98, 152, 154, 155, 174, 176, 193, 196

E ebola, vii, 12, 19, 21, 25, 28, 31, 34, 35, 36, 39, 55, 56, 60, 62, 63, 68, 69, 72, 86, 87,

Index 89, 91, 94, 97, 99, 125, 165, 166, 167, 168, 170, 172, 174, 175, 176, 177, 178, 182, 184, 185, 186, 188, 189, 190, 194, 195, 196, 197, 198, 199, 200, 202, 205, 206, 210,211, 212, 214, 215, 216, 220, 221, 222, 223, 224, 225, 231, 232, 236, 238, 243, 250, 251 economic development, 4, 68, 126 economic systems, 108 ecosystem, 47, 81, 83, 93 education, 25, 28, 36, 105 emergency, 5, 7, 37, 49, 77, 78, 90, 109, 116, 126, 140, 142, 143, 144, 146, 147, 170, 174, 176, 194, 196, 205, 226 emergency management, 140, 146, 174 emergency preparedness, 49, 170, 205 emergency response, 174 employees, 128, 155, 175, 224 epidemic, 4, 12, 25, 28, 37, 42, 49, 52, 61, 64, 72, 76, 78, 87, 88, 154, 178, 181, 183, 221, 226, 231 epidemiology, 23, 26, 44 equipment, 76, 98, 142, 186 evidence, 32, 37, 38, 58, 63, 67, 144, 149, 151, 173, 179, 183, 237 evidence-based practices, 67 expertise, 15, 20, 22, 58, 59, 60, 66, 84, 146, 175, 195, 200, 201, 204, 206, 231 exposure, 18, 20, 116, 181, 191 extreme poverty, 45, 48

F false negative, 78 false positive, 32 FDA, 47, 71, 92, 165, 170, 171, 175, 176, 195, 200, 201, 206, 208, 227, 231, 232, 233, 235 federal government, 16, 40, 65, 140, 141, 142, 180, 204, 222, 224

257

financial, 6, 25, 28, 88, 128, 169, 193, 217, 227 financial crisis, 6 financial sector, 6 first generation, 46 first responders, 74, 79, 90 Food and Drug Administration, 47, 165, 170, 200, 231 forecasting, 35, 55, 56, 58, 59, 60, 61, 65, 85, 95, 98, 107, 111, 113, 184, 202, 203, 205, 214, 221, 226 forecasting model, 221 fundamental needs, 33, 40 funding, 7, 39, 46, 51, 57, 64, 76, 79, 82, 86, 108, 111, 113, 115, 171, 195, 233 fundraising, 151 funds, 7, 16, 50, 51, 80, 171, 175, 206, 232, 235

G global consequences, 24, 27 global economy, 28, 50 global scale, 25, 28, 39 global security, 51 globalization, 24, 27 governments, 11, 56, 62, 63, 86, 91, 105, 204, 224 guidance, 10, 11, 20, 22, 69, 76, 77, 106, 174, 183, 184, 189, 217, 219, 235 guidelines, 167, 186, 209, 217, 218, 219, 228, 229

H health, 5, 6, 8, 11, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 32, 35, 36, 37, 38, 39, 43, 44, 51, 52, 54, 56, 58, 59, 60, 61, 62, 63, 67, 68, 69, 75, 76, 77, 78, 80, 81, 83, 84, 86, 99, 104, 108, 109, 111, 112, 113, 115, 116, 125, 126, 127,

258

Index

129, 130, 139, 140, 142, 144, 146, 147, 148, 152, 169, 171, 174, 177, 181, 194, 196, 205, 215, 216, 220, 222, 225, 231, 235, 237, 250 health care, 13, 17, 54, 69, 109, 174, 177, 222, 250 health care professionals, 109 health care system, 109, 222 health condition, 8, 112 health information, 111 history, 5, 16, 18, 20, 24, 27, 31, 35, 36, 95, 107 human, 3, 12, 16, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 33, 34, 38, 40, 47, 50, 68, 71, 75, 80, 81, 84, 85, 99, 104, 105, 106, 107, 108, 109, 116, 154, 178, 181, 183 human behavior, 24, 27, 85, 183 human health, 33, 38, 40, 75, 80, 85, 106, 107, 181

I Immigration and Customs Enforcement, 174 immune response, 152, 153 immune system, 179, 213 immunity, 112, 181, 182 immunocompromised, 92, 106 immunosuppressive drugs, 93 incidence, 150, 185, 188, 203, 213, 221 individuals, 8, 11, 13, 17, 18, 20, 31, 36, 60, 73, 76, 90, 92, 106, 109, 111, 116, 129, 178, 179, 182, 187, 191, 226, 231, 235 infection, 26, 35, 53, 56, 77, 104, 110, 112, 116, 148, 150, 174, 179, 180, 182, 191, 194, 213 infectious agents, 2, 16, 201 infectious disease model, v, viii, 9, 112, 113, 165, 166, 169, 170, 172, 175, 176, 181, 182, 184, 185, 186, 190, 195, 197, 199, 200, 201, 204, 205, 206, 208, 209,

210, 216, 220, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 237, 252 infectious diseases, v, vii, 1, 2, 3, 4, 9, 11, 15, 18, 21, 23, 26, 29, 30, 31, 32, 33, 34, 38, 39, 40, 46, 48, 68, 74, 82, 106, 110, 114, 116, 142, 153, 154, 155, 166, 168, 170, 171, 173, 175, 176, 178, 179, 181, 183, 186, 187, 191, 192, 197, 198, 201, 225, 230,231, 233, 234, 237, 238, 239, 240, 241, 242, 243, 244, 252 influenza, 7, 31, 34, 60, 102, 109, 168, 171, 176, 178, 180, 181, 184, 187, 188, 189, 190, 193, 194, 195, 198, 202, 203, 205, 208, 210, 211, 212, 216, 217, 221, 225, 226, 232, 233, 240, 244, 252 influenza a, 109, 171, 187, 196, 225, 233 influenza vaccine, 180, 189 influenza virus, 60, 178, 180, 190 information exchange, 35, 200, 201 information sharing, 208 information technology, 111 institutions, 52, 154, 155, 205, 224 intelligence, 60, 88, 184 intensive care unit, 52, 74 interagency coordination, 174 intervention, 34, 104, 169, 182, 189, 190, 215, 221, 252, 253 investment, 25, 28, 30, 33, 38, 39, 40, 46, 49, 50, 105, 111 issues, 23, 26, 29, 33, 41, 60, 62, 81, 105, 112, 126, 169, 170, 180, 181, 218, 230

L leadership, 15, 17, 33, 41, 70, 86, 109, 155, 170, 188, 193, 195, 196, 197, 203, 206, 208 local authorities, 184 local government, 204

Index M machine learning, 31, 33, 34, 35 management, 63, 169, 174, 176, 218, 234 manufacturing, 46, 50, 83, 98, 174, 175 matter, iv, viii, 25, 28, 32, 38, 166, 186, 187, 196 media, 9, 11, 36, 37, 39, 42, 62, 68, 69, 87, 114, 124, 125, 126, 127, 140, 151, 225 medical, 7, 8, 17, 24, 27, 29, 31, 36, 90, 101, 127, 130, 142, 143, 169, 170, 174, 176, 180, 182, 184, 188, 192, 193, 196, 197, 201, 215, 250 medicine, 19, 21, 23, 26, 30, 60 methodology, 171, 237, 245 microcephaly, 179, 213, 223 model development, vii, viii, 166, 198, 201, 209, 210, 211, 216, 236, 237 mortality, 7, 30, 78, 190, 231 mortality rate, 7, 190, 231 multinational corporations, 127

N National Academy of Sciences, 4, 27, 241 National Institutes of Health, 19, 148, 154, 166, 170, 201, 214, 231 national security, 67 natural disaster, 67 natural habitats, 107 natural resources, 107 nursing, 73, 78, 81, 90, 96 nursing home, 73, 78, 90, 96

O officials, viii, 31, 34, 36, 76, 113, 115, 139, 140, 141, 142, 144, 146, 166, 170, 171, 172, 173, 174, 175, 176, 179, 182, 185, 186, 187, 188, 189, 190, 191, 192, 193,

259

194, 195, 196, 197, 198, 199, 200, 201, 202, 204, 205, 206, 207, 208, 209, 215, 216, 220, 221, 222, 223, 224, 225, 226, 227, 232, 233, 234, 235, 236, 237, 240, 247, 252 operations, 144, 175, 195 opportunities, 19, 21, 77, 88, 89, 110, 167 organizational culture, 198 overlap, 98, 167, 170, 207, 227 oversight, 15, 39, 196

P pandemic viruses, vii, 89 participants, 60, 199, 200, 202, 207 pathogens, 3, 12, 24, 25, 27, 28, 74, 105, 106, 107, 168, 178, 189, 191, 231 peer review, 192, 199, 215, 219 personal hygiene, 108, 115 personal relations, 221 personal relationship, 221 pharmaceutical, 45, 60, 71, 82, 87, 88, 149 physicians, 81, 106, 109, 148, 150 platform, 31, 34, 35, 55, 58, 59, 60, 61, 71, 88, 129 pneumonia, 31, 35, 37, 130 policy, vii, viii, 5, 51, 60, 67, 68, 166, 167, 170, 171, 175, 184, 185, 188, 189, 192, 212, 217, 218, 219, 222, 227, 228, 230, 231 policymakers, 64, 65, 67, 88, 107, 113, 198 population, 9, 19, 21, 25, 45, 77, 92, 93, 96, 105, 107, 109, 112, 113, 181, 182, 183, 237 prediction models, 216 predictor variables, 183 preparedness, 3, 12, 13, 35, 39, 40, 49, 58, 59, 67, 85, 88, 113, 141, 144, 169, 170, 174, 176, 181, 186, 189, 201, 203, 227, 230, 231, 236, 250

260

Index

prevention, 13, 25, 28, 34, 43, 57, 64, 69, 105, 108, 149, 150, 151, 186, 191, 205 prior knowledge, 183 private investment, 50 professionals, 24, 27, 29, 76, 108 programming, 211, 217, 246 programming languages, 217 project, 31, 34, 36, 40, 60, 61, 83, 110, 114, 125, 183, 252 public health, vii, viii, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 26, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 45, 49, 50, 54, 55, 56, 57, 58, 59, 60, 62, 65, 67, 68, 69, 70, 71, 72, 74, 76, 78, 79, 87, 88, 89, 99, 102, 109, 110, 111, 114,115, 129, 130, 166, 167, 169, 170, 171, 172, 174, 176, 177, 178, 180, 181, 183, 184, 185, 186, 188, 192, 193, 194, 195, 196, 197, 201, 204, 205, 212, 218, 220, 222, 229, 230, 231, 232, 235, 236, 242, 250, 251 public policy, 68 public support, 51

188, 190, 191, 193, 194, 198, 202, 206, 220, 227, 234, 252 respiratory syncytial virus, 45 response, 3, 6, 10, 12, 15, 16, 17, 19, 21, 24, 25, 27, 28, 30, 31, 32, 35, 36, 38, 39, 40, 49, 55, 56, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 69, 70, 76, 79, 82, 89, 100, 105, 108, 110, 115, 140, 141, 154, 167, 169, 170, 171, 174, 176, 177, 178, 180, 181, 183, 184, 185, 186, 187, 188, 191, 192, 193, 194, 195, 196, 197, 198, 200, 201, 203, 205, 206, 212, 214, 216, 218, 220, 221, 222, 224, 226, 227, 229, 230, 232, 233, 236, 250, 251, 252 risk, 11, 17, 18, 20, 24, 25, 27, 28, 37, 38, 75, 76, 79, 80, 81, 84, 85, 92, 93, 101, 105, 106, 107, 110, 112, 116, 151, 167, 177, 180, 181, 189, 198, 213, 223, 226, 250 risk assessment, 151 risk factors, 24, 27, 75, 81, 107

R

safety, 45, 50, 71, 88, 92, 98, 99, 102 school, 45, 76, 130, 180, 192 science, 14, 15, 17, 43, 45, 48, 68, 69, 70, 85, 87, 126, 155, 156, 183, 192, 203, 224 scientific knowledge, 24, 26, 109 scientific publications, 217 scientific understanding, 183, 225 sensitivity, 32, 37, 110, 211, 212, 214 severe acute respiratory syndrome, 4, 49 signals, 31, 35, 36, 71, 114, 115 species, 16, 24, 25, 26, 28, 80, 84, 105, 107, 178 stakeholders, 32, 37, 67, 87, 113, 221, 235 surveillance, 10, 15, 19, 21, 25, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 43, 55, 56, 57, 59, 60, 61, 64, 65, 74, 99, 105, 107, 110,

recommendations, iv, 9, 20, 22, 52, 56, 63, 67, 108, 115, 150, 167, 176, 197, 198, 228 recovery, 13, 38, 89, 104, 182 reliability, 167, 217, 219, 229 replication, 97, 152, 153, 154 requirements, 65, 66, 169, 184, 193, 194, 206, 217 researchers, 9, 13, 19, 21, 32, 36, 39, 62, 66, 81, 125, 126, 155, 169, 201, 203, 213, 219, 224, 229 resource allocation, vii, viii, 166, 167, 170, 171, 185, 193, 195, 230, 231 resources, viii, 7, 10, 40, 61, 80, 90, 105, 110, 154, 166, 167, 169, 175, 176, 186,

S

Index 111, 113, 114, 115, 176, 185, 191, 203, 205, 222, 231 symptoms, 9, 13, 17, 36, 73, 77, 101, 106, 109, 116, 169, 178, 179, 194, 223 syndrome, 4, 49, 179, 213

261

173, 174, 178, 180, 189, 198, 203, 205, 232, 237, 238, 239, 240, 241, 242, 244 universities, 19, 21, 52, 81, 155, 175, 192, 199, 204, 205, 224

V T technical assistance, 178 technical comments, 230 techniques, 9, 30, 34, 35, 40, 69, 110, 128, 183 testing, 9, 14, 15, 16, 35, 36, 64, 66, 70, 76, 77, 78, 83, 95, 96, 98, 99, 102, 116, 151, 168, 176, 193, 194, 211, 217, 223, 251 therapeutics, 185, 186, 188, 190 thoughts, 18, 20, 52, 81, 82, 83 threat assessment, 170 threats, 10, 24, 27, 36, 45, 48, 49, 51, 67, 79, 144, 169, 170, 174, 175, 197, 198 time constraints, 211, 216, 224 training, 29, 113, 142, 145, 146, 224, 252 transmission, 8, 13, 17, 32, 38, 40, 47, 50, 62, 63, 71, 76, 80, 95, 96, 97, 106, 107, 108, 111, 112, 115, 116, 180, 183, 186, 202 transparency, 12, 109, 116, 209, 216, 217, 218, 219, 228 treatment, 9, 14, 15, 36, 57, 65, 69, 101, 149, 155, 188, 190, 198, 215

U U.S. Department of Agriculture, 174 U.S. economy, 6 United Kingdom, 199 United Nations, 146, 188 United States, v, 4, 5, 6, 8, 12, 15, 18, 20, 23, 32, 33, 38, 39, 40, 41, 43, 48, 53, 55, 58, 60, 75, 81, 88, 90, 102, 106, 109, 110, 112, 148, 156, 165, 168, 169, 170,

vaccine, 7, 13, 19, 21, 22, 24, 27, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 57, 65, 70, 71, 72, 78, 81, 82, 83, 85, 87, 89, 91, 92, 98, 99, 100, 101, 102, 125, 169, 180, 182, 184, 186, 189, 190, 196, 200, 208 validation, 167, 209, 210, 212, 214, 216, 245, 247 vector, 4, 60, 178, 189, 191, 203, 205 viral infection, 16, 27, 150 virology, 26, 80 viruses, vii, 4, 9, 10, 12, 16, 19, 21, 24, 25, 27, 28, 50, 69, 74, 75, 78, 79, 80, 83, 84, 89, 97, 98, 99, 105, 106, 108, 115, 146, 154, 155, 178, 180, 196, 203 vitamin A, 151 vitamin C, 150 vitamins, 151

W wild animals, 107 wildlife, 23, 24, 25, 26, 27, 28, 29, 40, 75, 84, 85, 104, 105, 107, 169 witnesses, 14, 15, 18, 20, 22, 68, 79, 87, 89, 94, 97, 103 workers, 8, 32, 36, 62, 76, 92, 142, 177, 250 workforce, 74, 89, 224 working groups, 115, 197, 199, 224, 227 worldwide, 18, 20, 23, 29, 53, 140, 154, 168

262

Index Z

zika, vii, 9, 19, 21, 34, 39, 69, 86, 89, 91, 166, 167, 168, 170, 171, 172, 174, 175,

176, 178, 179, 180, 184, 186, 188, 189, 190, 191, 194, 195, 196, 199, 202, 203, 205, 206, 210, 211, 212, 213, 216, 217, 220, 221, 222, 223, 224, 225, 226, 231, 232, 236, 239,240, 243, 244