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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Page 30
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Page 40
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Page 44
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
×
Page 45
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
×
Page 46
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
×
Page 47
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Page 48
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
×
Page 49
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
×
Page 50
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
×
Page 51
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
×
Page 52
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
×
Page 53
Suggested Citation:"2 Surveillance." National Academies of Sciences, Engineering, and Medicine and National Academy of Medicine. 2021. Public Health Lessons for Non-Vaccine Influenza Interventions: Looking Past COVID-19. Washington, DC: The National Academies Press. doi: 10.17226/26283.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

2 Surveillance The coronavirus disease 2019 (COVID-19) pandemic has exposed gaps in the capacity of worldwide national health systems and global-level systems to detect emerging and reemerging pathogens—including possible zoonotic threats, new strains of influenza with pandemic potential, and an- tigenic drifts in known viruses—before an outbreak occurs and a response is required. To close these gaps, countries need to collaborate to create early warning systems that are supported by political commitment, stable governance, and sustainable financing. Effective outbreak surveillance is urgently needed, since epidemics and pandemics are likely to become more frequent due to factors such as the expansion of urbanization, the growth and intensification of livestock production, more extensive and rapid global travel and trade connections, the effects of climate change, and pervasive socioeconomic inequities. Fortunately, the COVID-19 pandemic has also revealed the benefits of leveraging political will and financial resources to realize this early warning surveillance network for future emergent patho- gens (Carroll et al., 2021). PREPAREDNESS FOR SURVEILLANCE DURING THE COVID-19 PANDEMIC Various indicators and indexes have been developed in recent years to evaluate countries’ level of preparedness, identify gaps and weaknesses, and support strengthening their capabilities to prevent, detect, and respond to outbreaks, epidemics, and pandemics of infectious diseases. For example, the Global Health Security Index (GHSI) draws on open-source informa- 19 PREPUBLICATION COPY—Uncorrected Proofs

20 NON-VACCINE INFLUENZA INTERVENTIONS tion to assess and benchmark health security and related capacities in 195 countries (JHU, 2019). Similarly, WHO’s Joint External Evaluations (JEEs) have been used for evaluating a country’s ability to prevent, detect, and respond to infectious diseases and outbreaks. Countries ranked higher in terms of preparedness according to GHSI, JEEs, and other indicators would be expected to respond more effectively to an actual pandemic event; how- ever, that was not the case for COVID-19. An evaluation of the predictive value of GHSI and JEEs found that countries’ health preparedness scores were not correlated with detection response times or mortality outcomes (Haider et al., 2020). Furthermore, responsibilities for countries to act on their GHSI scores and improve preparedness are not necessarily delineated. A rank-based analysis of Organisation for Economic Co-operation and De- velopment (OECD) countries’ ability to respond to COVID-19—based on total cases, deaths, tests, and recovery rates—found that their pre-pandemic GHSI preparedness scores did not predict their actual response; the scores tended to overestimate some countries’ preparedness and underestimate oth- ers (Abbey et al., 2020). A study evaluating the correlation between coun- tries’ GHSI scores and measures of COVID-19 burden found no association between GHSI and rate of testing and, unexpectedly, a positive association between GHSI and cases and deaths (Aitken et al., 2020). An analysis of im- ported COVID-19 cases reported across 49 countries in sub-Saharan Africa (SSA) as of April 2020 found that the countries with high (1) GHSI scores, (2) likelihood of severe cases, and (3) government effectiveness rankings were not necessarily reporting a higher incidence of cases or more informa- tion per case. Such gaps in information could indicate undetected transmis- sion and illustrate the difficulty of detecting and responding to asymptomatic cases (Skrip et al., 2021). More broadly, these disparities between predicted preparedness and actual response to a pandemic highlight shortcomings in the way preparedness has been assessed. Furthermore, given the increasing degree of global interconnectedness, “identifying and controlling spread of newly arising infectious agents is only as effective as the practices within the poorest performing countries” (Aitken et al., 2020, p. 354). ROLE OF SURVEILLANCE IN MITIGATING RESPIRATORY VIRUS OUTBREAKS Surveillance has different functional roles in the context of a respiratory virus outbreak: (1) detecting potential new threats outside of a jurisdiction that could potentially be imported and spread locally, including epizootic, zoonotic, and epidemic threats, (2) detecting the importation and community transmission of an identified outbreak threat in animals and humans, and (3) assessing the extent and severity of an outbreak using forecasts and models. The first two roles focus on detecting a threat quickly and accurately, while PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 21 also minimizing false-positive test results; in some scenarios, these efforts may warrant oversampling high-risk locations (e.g., ports of entry). In con- trast, activities to fulfill the third role—quantifying the spread—are situated in the realm of systems-based processing of large volumes of human samples, collected in a representative way, to understand the current number of cases and how that epidemiological landscape is changing. Syndromic Surveillance for Infectious Diseases Over the past two decades, syndromic surveillance has been used as a strategy for detecting and monitoring public health events based on indi- vidual- or population-level indicators in advance of confirmed diagnoses of an emerging infectious disease. For example, data on symptoms or clinical diagnoses such as influenza-like illness (ILI) or severe acute respiratory ill- ness could serve as early indicators that an unusual respiratory pathogen is circulating (van den Wijngaard et al., 2008). These indicators and data on pneumonia of unknown origin were used retrospectively by Chinese authorities and WHO to assess evidence for early cases of COVID-19 (WHO, 2021b). Syndromic surveillance is theorized as advantageous for early detection of infectious disease outbreaks, given the time lags between initial symptoms and a clinically or laboratory-confirmed diagnosis (Chu et al., 2012). A retrospective study found that syndromic data from health registries—including on work absenteeism, general practice consultations, prescription medications dispensed, diagnostic test requests, hospital diagno- ses, and deaths—correspond to patterns in respiratory pathogen activity and thus can be used for surveillance (van den Wijngaard et al., 2008). Beyond the early detection value, syndromic surveillance data can also inform public health actions, contribute to improved situational awareness, and bolster the credibility of public communications. Clinical laboratory testing that includes signs and symptoms, with data aggregated in the cloud, can also serve as a type of surveillance when syndromic trends are reported, includ- ing negative test results (Meyers et al., 2018). If enough negative results are reported in a certain region linked to people with severe symptoms, it could be a signal of a new pathogen and trigger the need for additional testing. This was used during H1N1 in 2009 and again in Wuhan when the out- break first began; syndromic panels were negative for pneumonia, leading to identifying a novel virus. Incorporating health systems, including academic health institutions and the data they collect, can strengthen global public health infrastructure, even for pathogens not often targeted by surveillance. This is an opportunity to add scale and capacity to public health. Box 2-1 describes some of the major influenza surveillance collaboratives that serve to do so. Syndromic surveillance conducted alone, such as with ILI systems, or in combination with viral testing can be and has been used to PREPUBLICATION COPY—Uncorrected Proofs

22 NON-VACCINE INFLUENZA INTERVENTIONS track activity in real time during a pandemic (Brammer et al., 2011; Lipsitch et al., 2009; Shaman et al., 2011). However, a qualitative study of syndromic surveillance during the 2009 H1N1 pandemic in Ontario, Canada, found that it had only a limited impact on decision making about public health response activities, which were largely informed by logistics (e.g., vaccine availability) and traditional forms of surveillance using laboratory data (Chu et al., 2012). BOX 2-1 Summary of Major Surveillance Collaborations Influenza Surveillance System: The U.S. Centers for Disease Control and Prevention (U.S. CDC) collects, compiles, and analyzes influenza data through a voluntary collaboration between the organization and its state, local, and territo- rial health departments, laboratories, statistics offices, and care organizations. The system (1) conducts virologic surveillance, where respiratory illnesses are sampled for influenza virus types, subtypes, lineages, and the age groups af- fected, characterizing the genetic and antigenic composition of the virus, and conducts surveillance for novel influenza A viruses. The surveillance system also reports (2) outpatient influenza-like illness, (3) the geographic spread of influenza, (4) influenza-associated hospitalizations, and (5) mortality surveillance associated with influenza, COVID-19, or pneumonia (CDC, 2020). Severe Acute Respiratory Infections Network (SARInet): In the Americas, a diverse range of professionals across countries, organizations, and health- related organizations participate in the SARInet. These efforts are supported by the World Health Organization, the Pan American Health Organization, and U.S. CDC and are intended to complement and, where needed, compensate for ministry of health capacity in the region. They share, learn, and collaborate to enhance the epidemiological understanding of influenza and other respiratory viruses (SARInet, 2021). European Influenza Surveillance Network (EISN): In Europe, the EISN uses reports from sentinel general practitioners, pediatricians, and other specialty physicians. Each EISN member reports new cases of either influenza-like illness or acute respiratory infections, but some members report both (ECDC, 2021). Global Influenza Surveillance and Response System (GISRS): Since 1952, the GISRS protects people from seasonal, pandemic, and zoonotic influenza through collaboration and virus and data sharing. The system serves as a global platform for monitoring influenza epidemiology, an alert system for novel influenza viruses and other respiratory pathogens of concern, and a mechanism for influenza surveil- lance, preparedness, and response. The GISRS consists of collaborating centers across 123 countries, primarily National Influenza Centers and WHO Collaborating Centers for (1) influenza research and reference material; (2) influenza epidemiol- ogy, surveillance, and control; and (3) ecological studies on influenza in animals. There are also regulatory and reference influenza laboratories (WHO, 2021c). PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 23 ONE HEALTH APPROACH The existing wealth of knowledge and evidence-based strategies for mitigating epidemic and pandemic threats remains largely untapped and un- derused. For instance, “One Health” is a collaborative, multilevel, transdis- ciplinary approach that aims to achieve optimal health outcomes between people, plants, and animals in their shared environment (CDC, n.d.). It is increasingly recognized by governments, scientists, the private sector, non- governmental organizations, academic partners, and others as an effective way to combat health threats that affect people, animals, plants and the shared environment. One Health approaches are particularly relevant to emerging infectious diseases, of which >60 percent are zoonotic, and to dis- eases that have a strong link to environmental conditions (e.g., water- and vector-borne diseases). Many of these diseases spill over to humans through a complex and multi-step process (see Figure 2-1). This type of surveillance effort is especially critical in low- and middle-income countries (LMICs) and places that have an extensive human–animal interface. In one review FIGURE 2-1 Figurative description of the multi-scale, multi-step process of pandemic emergence. SOURCE: Bogich et al., 2012. PREPUBLICATION COPY—Uncorrected Proofs

24 NON-VACCINE INFLUENZA INTERVENTIONS of nearly 400 public health events of international concern, a breakdown or absence of public health infrastructure was identified as the driving fac- tor for just under 40 percent of outbreaks. Though many outbreaks do not result in global pandemics, pandemic prevention at the local level should include stronger public health infrastructure, expanded surveillance, and incorporation of development agencies into strategies that target where populations intersect with the environment (Bogich et al., 2012). Evidence for One Health Approaches and the Need for This Type of Surveillance Earlier studies have recommended active surveillance through One Health approaches to mitigate infectious disease threats as discussed dur- ing an Institute of Medicine workshop on emerging viral threats (IOM, 2015). Some of these recommendations have been implemented, but not to the extent that outbreaks such as COVID-19 could have been prevented, despite the warning signs and knowledge about how to use One Health approaches to intervene. For example, forming One Health outbreak inves- tigation teams that involve veterinarians, medics, social scientists, wildlife biologists, and ecologists could enable more rapid investigation of the zoo- notic origins of emerging diseases, something that was not a focus of early COVID-19 investigations. Done well, One Health approaches can lead to higher returns on investment through joint human–animal disease surveil- lance and control measures (Kelly et al., 2020). Ongoing exercises working across sectors also help to facilitate collaboration and connect stakeholders that do not typically interact, improving future communications. Interagency One Health platforms have been launched in LMICs spe- cifically to link operations of ministries of health, agriculture, and the environment and wildlife, while also maximizing surveillance for influenza and other emerging zoonoses. Because South Asia has been identified as a “hot spot” for emerging zoonotic disease, it has focused on strengthening One Health efforts since the early 2000s, along with many bilateral and multilateral partners. For example, Bangladesh has seven One Health re- search programs, offers three One Health postgraduate degrees at various universities, and has field epidemiology training programs for public health and laboratory personnel through the U.S. CDC (McKenzie et al., 2016). It also developed a Strategic Framework for One Health Approach to Infec- tious Diseases in 2012, which was endorsed by the Ministry of Health with widespread support (IEDCR, 2012). Through multi-sector collaboration in Kenya, the government devel- oped an institutional framework to highlight the importance of several types of diseases that informs capacity building programs, surveillance, and workforce development, among other areas. It has noted improved PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 25 outbreak response and newly generated data that informed disease control programs and increased preparedness (Munyua et al., 2019). The ability to more easily share disease outbreak information across sectors and rapid response at the county level has been credited with reducing spillover to humans in an anthrax outbreak in 2016. More recently, the country has deployed a syndromic surveillance system in domestic and wild animals, using a mobile phone application for reporting and analysis, with hopes to improve real-time surveillance within the animal health sector. Stakehold- ers involved in this effort believe that “the adoption of the One Health program and approach in Kenya has led to rapid detection and control of zoonotic disease outbreaks at their source and thereby enhanced global health security” (Munyua et al., 2019). Lessons from COVID-19 The emergence of SARS-CoV-2 has reinforced the rising risk of patho- gens that are capable of jumping species to humans. Despite a wealth of evidence on different wildlife species and the types of viruses they carry, the connection between that knowledge and what measures are needed to reduce the risk of spillover is more tenuous. After SARS in 2003, substan- tial research in China and Southeast Asia demonstrated a wide diversity of related viruses in wildlife (bats in particular) and that some of these were able to infect human cells in vitro and cause SARS-like disease in mice with human ACE2 receptors (Ge et al., 2013; Latinne et al., 2020; Menachery et al., 2015). Farming wildlife known to act as SARS intermediate hosts con- tinued to expand, with around 14 million people employed in the industry in China alone in 2016 (UNDP China, 2017). Furthermore, published evi- dence revealed that people in rural China were infected by bat SARS-related coronaviruses even without direct involvement in hunting or consuming wildlife (Wang et al., 2018). These studies were widely cited in the litera- ture and cited by WHO in the rationale to include SARS-related corona- viruses as “priority pathogens” for vaccine and therapeutic development through the R&D Blueprint effort. A small number of researchers globally were funded to develop therapeutics (e.g., remdesivir). However, efforts to close down wildlife farms, markets, and trade networks or remove known coronavirus hosts were not widely undertaken until after the COVID-19 outbreak began. Likewise, widespread funding of vaccine or therapeutic development through the U.S. National Institutes of Health, Coalition for Epidemic Preparedness Innovation, or R&D Blueprint did not occur prior to the outbreak. This example demonstrates that the knowledge generated through One Health approaches and research can provide important insight, but until the political motivation exists to act on the findings and provide funding, the problems will remain unsolved and likely surface again. PREPUBLICATION COPY—Uncorrected Proofs

26 NON-VACCINE INFLUENZA INTERVENTIONS After SARS in 2003, China instituted a program of syndromic sur- veillance that did form part of the early warning system for clusters of pneumonia cases that were later diagnosed as COVID-19. The improved surveillance and laboratory capacity in 2020 was able to recognize the novel outbreak within just a few weeks of syndromic surveillance clusters (Chan et al., 2020). More areas for potential research in One Health are included below in Box 2-5. Challenges to Achieving an Effective One Health Approach Many experts recognize the barriers to widespread, practical implemen- tation of a One Health approach across contexts. Ultimately, inadequate funding mechanisms, lack of incentives for collaboration, and competing interests across different government ministries pose major barriers to imple- menting One Health principles. Although some countries have created dedi- cated One Health task forces and crosscutting mechanisms linking ministries of health, environment, agriculture and wildlife, they have been ineffective at scale and at the global level. Additionally, a review of One Health literature in 2017 found few efforts to systematize metrics and truly evaluate outcomes versus merely modeling projections (Baum et al., 2017). Of more than 1,800 papers, only 7 reported quantitative outcomes, and even these did not use a shared methodology. Without a standardized framework to capture metrics for these types of approaches, it will be difficult to encourage more wide- spread adoption of One Health. Another concern highlighted by multiple sources is sustainability of programming. A collection of three case studies in Africa concluded that broad institutional changes and sufficient funding are needed for One Health to become a more common approach to health policy at the national levels, and each country will need its own individual- ized plan based on its needs and capacities (Okello, 2014). A key challenge that has been highlighted by the COVID-19 pandemic is a lack of connection between the evidence of a potential pandemic threat and forming policies to deal with it. Evidence that viruses related to SARS-CoV were present in wildlife and livestock in China was funded by research agencies and published in scientific papers but not brought into a formal risk assessment framework. Similarly, wildlife farming and trade were considered the likely causes of the emergence of SARS, but policies to conduct coronavirus surveillance as a routine for wildlife hunters, farmers, or traders, or the animals they sold were not formalized into the public health system. Collecting influenza samples and identifying strains from wild birds and farmed animals is routine in some countries but could be ex- panded in many others. However, challenges include difficulties in assessing the pandemic potential of novel strains, as sequencing and pursuing all of those identified would quickly exhaust available resources and workforce. PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 27 To be optimally effective, One Health collaborative approaches should be truly international—not just interagency—and leverage the power of es- tablished regional and global health organizations. Expanded collaboration among national development agencies, such as the United States Agency for International Development, as well as multilateral organizations (e.g., WHO, Food and Agriculture Organization of the United Nations, World Organisation for Animal Health, United Nations Environment Programme, and World Bank and regional equivalents), should also be encouraged in the One Health sphere. More areas for additional research to inform this approach can be found in Box 2-2. RELEVANT FINDINGS AND CASE STUDIES FROM THE COVID-19 PANDEMIC This section provides an overview of COVID-19 findings and case studies that demonstrate successes, highlight innovations, and illustrate challenges related to surveillance for respiratory pathogens. Core Public Health Functions for Surveillance Core public health functions for surveillance include identification and notification, sampling and genomics, and testing and contact tracing for event notification and control. Strengthening these capacities will be critical to more effectively prepare for and respond to future epidemic and pandemic events. BOX 2-2 Examples of Research Topics Regarding One Health • Developing a risk assessment framework for novel viruses or strains discov- ered in wildlife, farmed, or traded animals. • Identifying key interfaces where spillover and then spread are most likely to occur, using data on wildlife species distribution, livestock, and human population surveys. • Identifying animal species that are likely to be viral reservoirs to specifi- cally target surveillance programs via phylogenetic and molecular virological approaches. • Enhancing target surveillance and identifying pathways for viral spillover through behavioral risk surveys in people. PREPUBLICATION COPY—Uncorrected Proofs

28 NON-VACCINE INFLUENZA INTERVENTIONS Tracking Outbreak Progress Pandemic statistics—particularly the proportion of the population infected—are believed to be generally and substantially underestimated. Contributing factors related to the limitations of classical surveillance ap- proaches include insufficient diagnostic capacity, failure to detect asymp- tomatic cases rapidly enough, and political shortcomings of following through on outbreak predictions. The COVID-19 pandemic has included cases in which traditional surveillance methods have underestimated the actual prevalence. For example, polymerase chain reaction (PCR) testing for detecting infections is hampered by limited testing capacity, high rates of false-negative results, and the test’s inability to detect asymptomatic and subclinical infections (Silverman et al., 2020). An analysis of the use of influenza surveillance networks to estimate U.S.-state-specific SARS-CoV-2 prevalence has suggested that during the early phases of the pandemic, more than 80 percent of infections were undetected (Silverman et al., 2020). Hospital-based surveillance has limited utility in accurately estimating the number of cases, because many people who test positive are not hospital- ized (Alwan, 2020), or there are delays in obtaining timely clinical data (Garg et al., 2020). However, in some localities, it may have contributed to mitigating the initial spread. For instance, in Singapore, such a surveillance and containment strategy has been documented as contributing to improved case ascertainment and slowing transmission (Ng et al., 2020). Telehealth data could also contribute to surveillance systems in a pandemic context, particularly if many patients are not hospitalized and virtual visits are en- couraged as an infection control measure (Koonin et al., 2020). Sampling and Genotyping Laboratory science is a cornerstone of successfully controlling an epi- demic or pandemic. Core components of an effective laboratory response include (1) building testing capacity early, (2) preparing the workforce for a dynamic response, (3) strengthening information management systems, and (4) creating laboratory partnerships that can be leveraged during an event (McLaughlin et al., 2021). During the COVID-19 pandemic, genotyping and genomic surveillance have been valuable tools for detecting new variants and understanding their potential effect on infectivity and health outcomes (CDC, 2021a). Understanding the genomic diversity of an infectious pathogen can inform more effective strategies to contain its spread during the initial stages of an outbreak. For example, in the highly interconnected region spanning Maryland and Washington, DC, in the United States, more than 2,500 cases of COVID-19 were reported within 3 weeks of the first detected case in PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 29 March 2020. Genomic sequencing analysis of 114 complete viral genomes revealed a broad diversity that included all the lineages that were known to be circulating globally at the time, signifying that multiple introductions of the virus into the region were likely. Moreover, a combined analysis of those genomes with clinical metadata determined that clinically severe cases had originated from all the major lineage strains (Thielen et al., 2021). Genomic sequencing is also valuable for seasonal and pandemic influenza (CDC, 2021b). However, this level of genomic surveillance is not universally con- ducted, making it difficult to obtain a global view of dominant strains in different areas. While SARS-CoV-2 led to an acceleration of efforts, without supplemental epidemiology and surveillance data, the genomic sequencing is not sufficient to show which strains are more transmissible or more lethal (Morgan et al., 2021). Some countries cannot afford the technology, nor do they have sufficient workforce; others, such as the United States, have not invested in the infrastructure because it was not seen as widely important until recently and fragmented data systems make it difficult to coordinate and share across institutions. For example, the United States was ranked thirty-third in the world during this pandemic, with less than two percent of cases sequenced. The COVID-19 Genomics UK Consortium (COG-UK), set up in April 2020, is an example of what a well-functioning system would look like and that others could model.1 After just 1 year, COG-UK had sequenced more than 450,000 genomes, contributing to the United King- dom’s rank of fifth in the world, sequencing more than eight percent of its cases (Maxmen, 2021). COG-UK has a long-term goal of developing a sus- tainable sequencing network across the United Kingdom. The consortium includes partners from the National Health Service, public health agencies, academic partners, lighthouse labs, and the Wellcome Sanger Institute. Individual- and Population-Level Testing At the individual level, COVID-19 testing strategies include quantita- tive PCR (qPCR), loop-mediated isothermal amplification, and antigen testing performed at the point of care, central laboratories, or through rapid testing modalities. At the population level, testing strategies range from pooled testing to screening to surveillance of wastewater and sur- faces. However, the optimal strategy for a given setting is context specific and not necessarily universal—different approaches are warranted to serve various purposes (Mina and Andersen, 2021). Diagnostic testing aims to identify people with SARS-CoV-2 infection, for both clinical management 1  For more on the COVID-19 Genomics UK Consortium, see https://www.cogconsortium. uk/cog-uk/about-us (accessed August 28, 2021). PREPUBLICATION COPY—Uncorrected Proofs

30 NON-VACCINE INFLUENZA INTERVENTIONS and isolation, contact tracing, and contact testing. Although the relatively lengthy time to results from laboratory-based PCR testing reduces its utility in preventing transmission, rapid point-of-care tests can enable more wide- spread testing coverage. The goals of surveillance testing are to conduct representative sampling to estimate prevalence and inform response activi- ties at the population level. Antibody testing can be used to understand the breadth of historical exposure, while ongoing community transmission can be monitored through PCR testing of wastewater or pooled testing in low- prevalence settings, for example. Screening, which includes entry screening and public health mass screening efforts, can be used to detect people who are a- or pauci-symptomatic but may be infectious. Rapid antigen tests can offer reduced costs and short turnaround times, particularly in places where RT-PCR capacity is limited. For COVID-19 screening, rapid antigen tests perform best in presymptomatic and early symptomatic cases with high vi- ral load up to 5 days from symptom onset. Their shorter turnaround times for results and lower costs can facilitate community testing regardless of symptoms in homes, care settings, and workplaces that may face risks of high levels of community transmission (ECDC, 2020). The COVID-19 pandemic has demonstrated the extent to which viro- logical, genotyping, and population-wide serological surveillance can be limited by testing capacity (de Lusignan et al., 2020). Efforts to bolster preparedness for future events are ongoing in countries in Africa and other regions to strengthen testing and other capacities to enhance public health surveillance systems, such as by integrating pathogen genomics (Inzaule et al., 2021). Evidence suggests that programs with expanded testing capacity during COVID-19 effectively curtailed transmission. In late 2020, Slova- kia implemented a strategy of population-wide rapid antigen testing and additional restrictions on social contact in 45 counties; modeling suggests that these measures—as well as isolation of household contacts—were as- sociated with a 58 percent reduction prevalence within one week of imple- mentation (Pavelka et al., 2021). Contact Tracing Contact tracing can contribute to not only curbing transmission of an infectious disease threat—via identifying and isolating exposed contacts— but also reducing case fatality rates through early detection and referral to care (Yalaman et al., 2021). Drawing on evidence from 138 countries, an analysis of different contact tracing strategies and COVID-19 case fatality rates found that comprehensive contact tracing—along with appropriate case isolation—was associated with significantly reduced case fatality rates, even after controlling for public health and social measures and the number of tests performed (Yalaman et al., 2021). PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 31 Contact tracing was also demonstrated to be successful during the West African Ebola outbreak in 2014–2015, most notably in Nigeria. Thanks to establishing a field epidemiology and laboratory training program in 2008, hundreds of Nigerian health workers were trained in contact tracing, outbreak investigations, and report development. The Nigerian program was the first to apply the concept of One Health in full, because it included epidemiology, laboratory, and veterinary tracks (Oleribe et al., 2015). The teams had worked together through Lassa Fever and polio outbreaks and were designed to be interdisciplinary, so they were poised to respond rap- idly to the Ebola outbreak and can be a model for future outbreaks. During the COVID-19 pandemic, few countries had a sufficient num- ber of trained personnel to conduct contact tracing, particularly during the early stages. Even settings where contact tracing was initially effective faced difficulties in sustaining those efforts as the pandemic unfolded. In Ger- many, initial contact tracing was largely successful until capacity became overwhelmed as infections peaked (Loh, 2020; Reintjes, 2020). In summer 2020, as case numbers began to decline, many countries devoted more resources to increasing testing capacity than to building contact tracing capacity or ensuring that people who became infected could appropriately isolate (Loh, 2020). A review of COVID-19 contact tracing efforts in Nigeria, Rwanda, South Africa, and Uganda provided several best practices, challenges, and lessons informing future implementation of these efforts. The common challenges identified across all five countries include internal stigma, com- munity resistance, and apathy driven by mistrust and perceived and inter- nal stigma. Another critical common challenge was misinformation and an overwhelming load of contact tracing and case detection workload for health care workers. For example, the number of contact tracers per 100,000 population ranged from a low of three in some areas to a high of 186. Other challenges identified included fears around contact tracers’ risk of COVID-19 infection, limited testing and health care capacity, mistrust of political entities, and poor adherence to quarantine and isolation guide- lines and rules. Lessons learned from these nations broadly included the effectiveness of decentralizing and building capacity for communication, contact tracing, testing, and their associated human resources at the local and community levels. Additionally, the authors found that interoperable data and technology should complement traditional contact tracing efforts to improve decision making. Partnerships, meaningful community engage- ment, and coherent political leadership were identified as mechanisms to build trust, combat misinformation, and scale interventions (Nachega et al., 2021). The general effectiveness of contact tracing varies across settings and contexts. An analysis of COVID-19 countermeasures in Yamagata Pre- PREPUBLICATION COPY—Uncorrected Proofs

32 NON-VACCINE INFLUENZA INTERVENTIONS fecture, Japan, found that retrospective contact tracing efforts to identify epidemiological links were likely effective in halting the first wave (Janu- ary–May 2020) (Seto et al., 2021). According to a mathematical modeling study, a combination of highly effective contact tracing and isolation was sufficient to bring a new outbreak under control within 3 months, but the likelihood of control declines if fewer cases are detected through contact tracing (Hellewell et al., 2020). A simulation study found that a testing capacity of 0.7–9.1 tests per 1,000 population would be needed to contain the spread of SARS-CoV-2, depending on public health and social measures in place, and that the number of new daily infections did not always de- cline—it could exponentially increase if contact tracing and testing efficacy fell lower than 60 percent (Fiore et al., 2021). Variations in the effectiveness of contact tracing can be attributable to the number of observed asymptomatic infections, transmission efficiency, population distribution and size, and the size of the secondary infection cluster. This suggests that when developing testing and contact tracing strat- egies, policy makers should consider population-level density, geographical distribution, and travel behavior (Fiore et al., 2021). Moreover, most strate- gies mainly employ a “forward-tracing” protocol to notify people that they were exposed to a known case. However, a bidirectional tracing approach also includes reverse tracing, which seeks to identify the parent case who infected the known case, as well as other cases related to that parent case. A modeling study has suggested that bidirectional tracing is a more robust approach to outbreak control for COVID-19, yielding a reduction in the effective reproduction number (Rt) more than twofold greater than forward tracing alone (Bradshaw et al., 2021). CHALLENGES IN PUBLIC HEALTH SURVEILLANCE HIGHLIGHTED DURING THE COVID-19 PANDEMIC COVID-19 has revealed multiple limitations of current public health surveillance systems and tools, which were primarily designed for ongoing surveillance of known pandemics and seasonal influenza rather than for early detection and mitigation of respiratory pathogens with pandemic potential. These existing systems are also unable to accommodate the sustained surge capacity necessitated by a large-scale global pandemic event. Specific challenges that undermine the ability to conduct syndromic surveillance and interpret surveillance data include (1) the effect of me- dia reporting early on; (2) changes in health-seeking behavior driven by pandemic-control measures, such as physical distancing; and (3) changes in systems for clinical coding and patient management (Elliot et al., 2020). Strengthening central systems for data use, collection, and sharing would al- low for more effective quantification of the spread of infection and optimal PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 33 parameterizing for forecasts and models. Furthermore, in addition to the strategies described below, core surveillance capacities can be strengthened through reiterative testing via simulations and other exercises (Reddin et al., 2021). Ascertainment Bias Ascertainment bias is a consequence of biased sampling that has likely undermined efforts to estimate the burden, infectiousness, and fatality of SARS-CoV-2 since the outset of the pandemic (see Box 2-3). Such biases can misinform the public about the severity of a disease and the utility of public health interventions in general or for particular subgroups (Peixoto et al., 2020). For example, using case fatality rates based on hospital fatality rates, which include only a small subset of individuals with the disease, has led to misunderstanding that alarmed the public and inaccurate comparisons of disease severity between COVID-19 and Ebola (Winters et al., 2020). Inaccurate epidemiological estimates based on nonrepresentative or inaccurate data have also led to ill-advised policy decisions and ineffective responses. For months at the outset of the pandemic, the U.S. government was unable to estimate how many people were sick with COVID-19, were hospitalized, or had died (Meyer and Madrigal, 2021). Levels of com- BOX 2-3 Ascertainment Bias and COVID-19 Case Fatality Ratios A case fatality ratio (CFR) is the proportion of deaths from a disease with the number of individuals diagnosed with the disease. Calculating accurate CFRs is critical in supporting the COVID-19 pandemic response. CFRs can help quantify the risk for different demographics and enable practical and accurate resource planning and allocation (Angelopolous et al., 2020). Also called “sampling bias,” underascertainment of mild cases can incor- rectly increase CFRs. For example, the cumulative number of confirmed cases in New York City, with a population of 8.55 million (NYC.gov, 2021), reached 72,181 on April 6, 2020 (NBC, 2020). However, a state seropositivity study, measuring the amount of virus in the blood, estimated that around 21 percent of city resi- dents had contracted COVID-19 (Goodman and Rothfeld, 2020). Collecting randomized data by testing close contacts of positive individuals regardless of symptomatic presentation could mitigate sampling bias by limiting the covariance, or the relationship, between diagnosis and death (Angelopou- los et al., 2021), which could help better communicate the risk of death from COVID-19 and how the fatality risk varies across different demographic groups within a population (Kobayashi et al., 2020). PREPUBLICATION COPY—Uncorrected Proofs

34 NON-VACCINE INFLUENZA INTERVENTIONS munity and background transmission were underestimated in many U.S. localities, which could have been rectified by routine standardized testing, which can mitigate sampling bias (Angelopoulos et al., 2021). Instead, the White House Coronavirus Task Force relied on forecasts from the Institute for Health Metrics and Evaluation during the initial days of the pandemic (IHME, 2020). This model made a number of inaccurate assumptions: that the epidemic curve would follow the outbreaks in China and Italy (Hol- mdahl and Buckee, 2020) and assumes that physical distancing measures would remain effective and that social distancing was being implemented the same everywhere (Jewell et al., 2020). Its initial projection—a death toll of only around 60,000 U.S. individuals (which was actually surpassed by May 2020)—influenced state and federal officials to pivot to reopening the economy instead of prolonging physical distancing and other public health measures (Cancryn, 2020). Other countries, such as Brazil, reduced testing, resulting in underreporting disease incidence (Fonseca, 2021). In contrast, China demonstrated the value of improved testing strategies. Early in the outbreak, diagnosis was based only on testing that had severe capacity constraints; when the case definition was expanded to include radiological criteria as an adjunct, it contributed to elucidating the true infection rates (Tsang et al., 2020). Additional research needs related to ascertainment biases can be found in Box 2-4. Variability in Estimating Infectiousness and Fatality Rates Wide variability has been observed between country- and state-level COVID-19 infectiousness and fatality rates. For instance, a Bayesian model- ing study—which was designed to minimize ascertainment bias—analyzed confirmed data on COVID-19 cases, deaths, and recoveries from U.S. states BOX 2-4 Examples of Research Topics Regarding Ascertainment Biases • Studying the shape and size of the “clinical iceberg” in different contexts • Understanding sampling frames of reported statistics and how to harmonize data from different sources to derive robust incidence/prevalence estimates • Determining clinical severity estimates (e.g., case fatality risks) that have well characterized denominators and monitoring their evolution by stage of epidemic and with different treatment interventions • Developing novel methods to take into account sampling biases, particularly in genomic surveillance where low- and middle-income countries receive poor coverage PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 35 and countries in all world regions. By April 2020, estimates of infectivity ranged from 9–13 unreported cases for every confirmed case globally. At the outset of the pandemic, the estimated mean global reproduction num- ber and total infection fatality rate were 3.3 (confidence interval [CI] 1.5, 8.3) and 0.17 percent (CI 0.05–0.9 percent), respectively. By mid-April, estimates for those values had evolved to 1.2 (CI 0.6, 2.5) and 0.8 (CI 0.2–4.0) percent. Moreover, the variability observed between the country- and state-level values implies substantial uncertainty about the ability to accurately assess an epidemic’s current state or trajectory (Chow et al., 2020). In terms of the earliest fatality risk estimates, a crude case fatality rate of 3.67 percent was found among cases from mainland China (Verity et al., 2020) and a case fatality risk of 1.4 percent in Wuhan (Wu et al., 2020). In contrast, an early analysis in Italy found crude case fatality rates of 10.6 percent nationwide and 18.3 percent in Lombardy, much higher than the rates estimated based on data from outbreaks in China and aboard the Diamond Princess cruise ship (1 percent) (Vicentini et al., 2020). Clinical Icebergs COVID-19 exemplifies challenges caused by the “clinical iceberg” phe- nomenon: the relative proportions of clinically observed infections versus unobserved infections. Quantifying those proportions is critical to develop- ing the parameters for models to elucidate population-level transmission dynamics and epidemic trajectories that are needed to inform public health policy (Wu et al., 2020). Most cases of respiratory pathogens in particular go undiagnosed and not notified, underscoring the need for surveillance and detection systems that “search for the unexpected” and unseen. Underrepresentative Sampling Frames A general limitation of survey-type seroprevalence studies is the under- representation of vulnerable populations at high risk of COVID-19 infec- tion and/or mortality (e.g., residents of nursing homes, persons who are homeless, persons who are incarcerated, or those living in large urban slums or refugee camps) due to challenges related to reaching and sampling those populations. Lack of representativeness in sampling frames undermines the ability to accurately estimate COVID-19 prevalence (Bendavid et al., 2021) and SARS-CoV-2 seroprevalence, as exemplified in a study that inferred the total number of people infected in all of Croatia from a serosurvey of just two factories (Ioannidis, 2021). However, some approaches have achieved a much greater degree of sampling representativeness, thus yielding more accurate estimates. Luxembourg implemented a mass screening program during the early summer 2020 wave of COVID-19 that aimed to capture PREPUBLICATION COPY—Uncorrected Proofs

36 NON-VACCINE INFLUENZA INTERVENTIONS a representative sample of the entire population—including residents and cross-border workers—and found a significant attack rate among asymp- tomatic cases (Wilmes et al., 2021). Similarly, a representative, nationwide, population-based serological survey in Spain reported that at least one-third of people with COVID-19 were asymptomatic, underscoring the impor- tance of early testing and detection (Pollán et al., 2020). Related, although it was against the convention early in the pandemic to only test individuals with a connection to China, the Seattle Flu Study was one of the first to find community transmission in the United States by testing study samples for SARS-CoV-2 (Chu et al., 2020). This further illustrates how diagnostic testing can be an effective response, that each community will have different levels of risk and testing may be more useful in some circumstances more than others (Sharfstein et al., 2020). Further needs for research related to transmission are highlighted in Box 2-5. Strengthening Reporting Institutional hierarchies and bureaucracies can stifle reporting on the progress of an outbreak. For instance, officials may be hesitant to trigger investigations by reporting on diseases of concern due to fear of stigma or the economic implications, as observed with Ebola in central and West Af- rica. Other layers of bureaucracy in reporting can also delay a response. For instance, within the African integrated disease surveillance response system, health facilities report to district and national-health levels of health authori- ties on priority diseases of importance; these then report to global institu- tions, which can be slow to respond. Reporting could also be strengthened by involving the communities—training and providing tools to frontline public health workers and community workers in accurately detecting, re- porting, and analyIng during routine public health surveillance for priority diseases; this can help to ensure timely, complete, and accurate data for deci- sion making. Using a case study designed to train resident epidemiologists BOX 2-5 Examples of Research Topics Regarding Transmission • Determining the mode of transmission of emerging new strains (e.g., airborne versus large droplets). • Understanding temporal dynamics in viral shedding and transmissibility to determine relative contributions of presymptomatic versus asymptomatic versus pauci- or full symptomatic spread. PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 37 in Liberia, it is possible for 1–2 instructors to train up to 20 students in col- lecting useful data, auditing data quality, and conducting Strengths, Weak- nesses, Opportunities, Threats analyses (Frimpong et al., 2017). Incentives are another mechanism to strengthen reporting during outbreaks. Different incentives may be most effective at different levels, such as country-level incentives of funding and resources, increased prestige for effective systems, and international encouragement (IOM and NRC, 2009). Zoonotic Surveillance The interface between humans, domesticated animals, and wild animals is a major locus for the emergence of zoonotic diseases, which comprise the majority of emerging infectious disease threats reported worldwide. Coronaviruses and avian influenza viruses are among the foremost zoonotic threats to human health (Huong et al., 2020), with evidence suggesting that SARS-CoV-2 may be due to zoonotic transmission that may have originated in or been subsequently amplified at a live animal market in Wuhan (Tiwari et al., 2020; WHO, 2021b). The COVID-19 pandemic and other epidem- ics of zoonotic origin of recent decades have underscored the need for One Health approaches to strengthen zoonotic surveillance efforts, detect viral strains with larger antigenic drifts, and develop better strategies to under- stand the degree of potential threat posed by emerging strains. Live Animal Markets Live animal markets provide ideal conditions for zoonotic transmission through an intimate interface among animals and people, leading to ampli- fication of pathogen load and the potential for recombination or selection pressure for evolution of virulence. These factors highlight the need for more effective surveillance strategies in these settings (Tiwari et al., 2020). Multiple zoonotic influenza viruses have been associated with human ex- posure to animals at these types of markets and further down wildlife and poultry supply chains. An evaluation of wildlife supply chains for human consumption in Vietnam (2013–2014) used PCR testing to detect corona- virus sequences, finding high proportions of positive samples in field rats (34.0 percent) to be consumed by humans and among bats in guano farms near human residences (74.8 percent) (Huong et al., 2020). The analysis also found a mix of different types of bat and avian coronaviruses in rodent feces, suggesting that the mixture and amplification of coronaviruses along the wildlife supply chain to retail and restaurant settings could increase the potential for zoonotic spillover to consumers. During the epidemic of avian influenza A H7N9 (2013–2015), which causes human infections primarily via zoonotic transmission, closing live PREPUBLICATION COPY—Uncorrected Proofs

38 NON-VACCINE INFLUENZA INTERVENTIONS poultry markets in mainland China temporarily halted outbreaks (Peiris et al., 2016). However, such measures are not feasible over the long term, due to the country’s existing systems for live poultry production and marketing. In China and other countries in Asia, live poultry systems dominate poultry consumption. These systems, which are complex and do not tend to be intensively regulated, span a large network of farm production, transporta- tion to wholesale markets, and retail distribution (Peiris et al., 2016). More sustainable and less disruptive approaches to reducing the risk of emergence and transmission of zoonotic influenza include instituting market “rest days,” banning live poultry in markets overnight, and separating terrestrial poultry from live ducks and geese; such strategies have been progressively implemented in Hong Kong (Peiris et al., 2016). Alternative strategies for reducing the risk of zoonotic viruses, beyond simply banning all live animal markets, have concomitant environmental and social benefits, including encouraging smaller-scale meat production, improving market hygiene, implementing more stringent regulations at markets, and outlawing the trade of certain wildlife (Petrikova et al., 2020). Others have argued that banning wildlife trade would effectively bolster the black market, so tighter regulation would be more effective (Tiwari et al., 2020). Coordination and Assessment An analysis of WHO’s JEE reports looked at trends in preparedness for high-consequence zoonotic infectious diseases among SSA countries (Elton et al., 2021). The veterinary workforce had the highest average score in all categories across all countries evaluated; response mechanisms had the lowest average score. Most countries provide public health training courses for veterinarians. The Southern African region had the highest mean score for all zoonotic disease categories. All five of the most frequently cited zoonoses on priority pathogen lists in SSA were neglected diseases: rabies, highly pathogenic avian influenza, anthrax, brucellosis, and bovine tuber- culosis (TB). These findings suggest that SSA countries should leverage the convergence of public health, veterinary, and environmental government departments across African and global health organizations—such as the One Health consortia and the Pan-African network PANDORA-ID-Net— to implement a collaborative One Health approach to pandemic prepared- ness and response (Elton et al., 2021). With increased genomic capacity for detection of novel viruses, a bet- ter strategy to assess risk of novel agents is needed. The recently published SpillOver is a comprehensive, publicly accessible risk assessment tool for systematically evaluating novel infectious viruses’ potential for zoonotic spillover and spread. Although data gaps limit the ability of SpillOver to rank relative animal–human transmission risk, among other challenges, PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 39 the tool and its associated watch lists can support virus discovery efforts to detect new animal viruses, assess and communicate risk, and inform pan- demic preparedness and response efforts (Grange et al., 2021). It also offers opportunities for global collaborative research to understand the biology of pathogens that may be emergent and screen therapeutic agents in advance. Ideally, this would be available as a global repository of information that can be accessed widely by researchers. Data Collection, Use, and Sharing Digitally enabled public health strategies augmented by data science can aid in population surveillance, case identification, contact tracing, and evaluation of interventions based on mobility data and public communica- tions. Harnessing the power of digital technologies through a combination of mobile phones, large online datasets, interconnected devices, low-cost computing resources, machine learning, and natural language processing underpins these efforts. Efforts are now focusing on ways to effectively and ethically incorporate data from digital and Web-based sources into public health surveillance—for example, through hybrid approaches that integrate data from traditional sources with data collected from Internet search que- ries, posts on social media networks, and other forms of open-source and crowdsourced data (Aiello et al., 2020). Metadata and line lists can be one informative way of linking data streams (Xu et al., 2020). In a pandemic, social media can serve as a powerful mechanism for communicating and disseminating information. However, a scoping review found that social media data were not leveraged for real-time surveillance to detect or predict cases during the COVID-19 pandemic as they have been for other infectious diseases, such as malaria and influenza (Tsao et al., 2021). Within nations, sharing surveillance data across communities can be vital for identifying an outbreak’s impact (Liverani et al., 2018). However, two-way accountability is needed for entities with capacity to take immedi- ate action on data and surveillance information that is shared by countries. Inefficiencies in collecting and sharing data among health agencies and across countries impeded the flow of information on critical treatment, patient, and event-level data during COVID-19 (Cossgriff et al., 2020). Another critical need is to standardize and harmonize data to enable data sharing (Fukushima et al., 2018). Additionally, practical issues, such as the location and method of long-term storage and maintenance, access control, and funding, are unresolved for epidemiologists and public health research- ers (Pisani and AbouZahr 2010). Nevertheless, a model that has been used for event notification is Participatory One Health Digital Disease Detection; community members use smartphone and other web applications to report unusual disease events in humans and both wild and backyard animals; PREPUBLICATION COPY—Uncorrected Proofs

40 NON-VACCINE INFLUENZA INTERVENTIONS these reports lead to a local response from health experts (Ending Pandem- ics, 2021). Another possible model to incentivize data reporting and sharing is the Global Initiative on Sharing Avian Influenza Data, which fosters col- laboration by requiring data users to provide credit to submitters and also work to include them in joint viral data analyses (LoTempio et al., 2020). Digital Contact Tracing Technologies Functions of digital contact tracing technologies include outbreak re- sponse, proximity tracing, and symptom tracking (Anglemyer et al., 2020). During the COVID-19 pandemic, digital contact tracing has successfully complemented traditional tracing methods by using smartphone applica- tion technology to identify exposed social contacts, particularly if they are strangers (Rodríguez et al., 2021). To augment traditional approaches, countries such as South Korea, China, and Singapore have implemented digital contact tracing strategies that are regarded as having contributed to successfully controlling spread (Lancet Digital Health, 2020). Modeling studies suggest that digital contact tracing can break chains of transmission (Salathé et al., 2020), but robust evidence for its effectiveness in real-world outbreak settings is currently lacking (Anglemyer et al., 2020). Widespread implementation of this approach has been hindered by poor integration of the technology with existing surveillance tools (Anglemyer et al., 2020) and by ethical and legal concerns, particularly around privacy, that can undermine public trust and discourage uptake. The public health benefit of these digital tools in outbreak responses needs to be further explored and better understood, especially for unin- tended consequences. In addition to the lack of evidence for real-world effectiveness, serious concerns remain that providing access to private infor- mation about health, behavior, and location can violate a user’s privacy— especially if these do not follow the critical principle of confidentiality and are repurposed for illegitimate surveillance purposes—as well as autonomy, if such technology is mandated (Gasser et al., 2020). Without deliberate investment and incentives to develop appropriate privacy preserving tech- nologies for surveillance and contact tracing, this field will not advance as needed due to fear and ethical questions. Mistrust in these technologies would be a barrier to effective implementation and use. If digital technol- ogy is thus used at national or global scale during epidemics or pandemics, developing and instituting best practices and standards for responsible data collection and processing will be critical for engendering public trust (Ienca and Vayena, 2020). Moreover, many settings lack the capacity for local-, national-, and international-level data transmission and sharing through electronic platforms (Gao et al., 2020; Holmgren et al., 2020). Some em- pirical demonstration is beginning to support the potential real-world util- PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 41 ity of digital contact tracing during an infectious disease outbreak scenario, however. A population-based study looked at the impact of a digital contact tracing app implemented in the Canary Islands, Spain, during the sum- mer of 2020 (Rodríguez et al., 2021). The app detected around six close contacts per simulated infection—most of whom were strangers—and the technology had relatively high adherence and compliance. Alongside these promising advances was a controversy regarding the United Kingdom’s National Health Service application, which, until mid-August 2021, could advise large groups and clusters of people to self-isolate. The use of this geolocator app placed worker shortages and continued COVID-19 related closures in conflict with the government’s wide-scale reopening plans (BBC, 2021). SURVEILLANCE INNOVATIONS The COVID-19 pandemic has underscored the need to broaden core capacities for surveillance by leveraging technological advances, including crowdsourced data streams, wastewater surveillance, metadata and line lists that link across data streams, and other innovative approaches. To strengthen preparedness and response to future epidemic and pandemic threats, these approaches—if determined to be effective and ethical—should be consolidated and routinized into central systems to complement tradi- tional surveillance. Aligning international strategies for regulating, evaluat- ing, and using digitally enabled public health is a key step toward realizing the full potential of public health in the future (Budd et al., 2020). Wastewater Surveillance The discovery that SARS-CoV-2 was present in infected patients’ feces and wastewater (Polo et al., 2020) has given rise to innovations in wastewa- ter-based epidemiological surveillance that employ near-source tracking of sewage drains for specific buildings to detect individual cases or small clus- ters (Hassard et al., 2021). Wastewater surveillance for infectious diseases holds great potential value for population-wide monitoring and enabling detection of early signals of transmission dynamics, particularly when test- ing capacity is limited or the time to reporting of diagnostic test results is lengthy or delayed (Peccia et al., 2020). A study in Seattle, Washington compared seven methods for concentrating and recovering SARS-CoV-2 from municipal wastewater and sludge (Philo et al., 2021). Skimmed milk flocculation without Vertrel extraction yielded the most consistent virus de- tection results and low variability, although the same may not hold in other contexts. Concentration and detection methods need to be appropriately validated for the setting’s specific water matrix to evaluate its performance. PREPUBLICATION COPY—Uncorrected Proofs

42 NON-VACCINE INFLUENZA INTERVENTIONS Crowdsourcing Surveillance Crowdsourcing surveillance by compiling lists of suspected, probable, and confirmed cases could enable quick preliminary assessments of epidemic growth, the potential for spread, appropriate periods of quarantine and isolation, and the efficiency of detection based on the current and evolving evidence base. A crowdsourcing approach was implemented in China in January 2020, when Kaiyuan Sun and colleagues compiled individual-level data from patients with COVID-19—which they mined from a Chinese so- cial media network used by health care professionals—with province-level data about daily case counts (Leung and Leung, 2020). The information was synthesized into a crowdsourced line list that was well aligned with the official epidemiological reports released by the national government. In the future, such crowdsourcing strategies could help mitigate the spread of epidemics, dispel misinformation, and counteract the detrimental impacts of geopolitical tensions and nationalistic populations on science-based epi- demic control efforts (Leung and Leung, 2020). Rapid Epidemic Intelligence Rapid epidemic intelligence draws on open-source data (e.g., news reports, social media) to supplement traditional surveillance methods and enable early detection of epidemic signals, thus supporting early investiga- tion and accelerating the development of diagnostics. Algorithms for clini- cal syndromes or diseases, machine learning, and artificial intelligence can be used to establish a baseline threshold for detecting abnormal signals. For instance, an open-source epidemic observatory, EpiWatch, was able to detect early signals of pneumonia or severe acute respiratory illness as a proxy for COVID-19 at the outset of the pandemic in China and Indonesia (Kpozehouen et al., 2020; Thamtono et al., 2021). Similar sources of rapid epidemic intelligence include ProMED, Healthmap in the United States, the Global Public Health Intelligence Network (GPHIN) in Canada (Carter et al., 2020), and Epiwabak in Malaysia. Nowcasting Surveillance “Nowcasting” is an innovative framework for assessing the current state of an ongoing outbreak or epidemic by leveraging advances in com- putational and laboratory sciences to elucidate the event’s pathogenic, epi- demiologic, clinical, and sociobehavioral characteristics; this approach can enhance situational awareness and inform decisions about response efforts (Wu et al., 2021). PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 43 Other Surveillance Innovations Other surveillance-related innovations during COVID-19 have demon- strated success and feasibility. These include innovative partnerships, such as the COVID-19 Healthcare Coalition,2 data aggregation networks (Budd et al., 2020), blockchain technologies (Idrees et al., 2021; Mashamba- Thompson and Crayton, 2020), artificial intelligence surveillance tools (Al- lam et al., 2020), and pooled testing (FDA, 2020). Web-based dashboards can serve as dynamic tools for communicating data, informing decision making, and encouraging behavior change (Ivankovi c´ et al., 2021). Har- vard has developed a smartphone app that detects loss of taste and smell, a strong indicator of COVID-19 (Hassard et al., 2021). WHO has explored using dogs to screen for COVID-19 (WHO, 2021a). CONCLUSIONS AND RECOMMENDATIONS Detection of Potential Threats Conclusion: COVID-19 has further emphasized the need to use the One Health approach to better target surveillance, including by build- ing on currently existing platforms for influenza surveillance in wild birds, poultry, and livestock. This includes programs for detection of new zoonotic strains with pandemic potential and large antigenic drifts and shifts and research to better understand the pandemic potential of new strains. Conclusion: One Health programs need to identify new viral strains, assess the risk they pose to people, and analyze where cases are likely to be found and outbreaks are likely to begin. Interdisciplinary collabora- tion among U.S. agencies, academic institutions, national governments, and multilateral partners has been successful in performing this surveil- lance in several countries with a One Health approach. Recommendation 2-1: The World Health Organization, the World Bank, and regional public health organizations should work collabora- tively with countries (particularly for low- and middle-income countries and those with extensive animal–human interfaces) to build sustainable capacity for routine surveillance in animals (wildlife, livestock, and domestic), and develop and support interagency One Health platforms. 2  For more on the COVID-19 Healthcare Coalition, see https://dsd.c19hcc.org (accessed August 20, 2021). PREPUBLICATION COPY—Uncorrected Proofs

44 NON-VACCINE INFLUENZA INTERVENTIONS Quantifying the Spread of a Pandemic Conclusion: Data informing public health surveillance, including for influenza and COVID-19, are vulnerable to ascertainment biases and therefore may not reflect the true underlying epidemiology; these biases happen particularly as a novel strain is first emerging. When the means used to collect data cannot be changed to avoid these problems, they can be taken into consideration during the analysis and interpretation of data being used to inform policy decisions. If not corrected, these biases can misinform the public about a disease’s impact and the likely effects of public health interventions in general and in particular subgroups. Conclusion: Within countries, the sharing of data collected from com- munity-based surveillance is critical for identifying the likely impact of outbreaks. Inefficiencies in collecting and sharing all types and sources of data among countries and global health agencies hampered the flow of information during the COVID-19 pandemic. The rapid sharing of a wide range of data internationally, including syndromic, epide- miologic, clinical, pathogen specific (e.g., genomic), and other (such as open-source intelligence), can provide early warning of an outbreak of concern as well as a picture of how it may develop. Recommendation 2-2: Countries should institute surveillance as the backbone of their health care systems, which should include submitting aggregated clinical data feeding into public health agencies. To ensure that policy makers have access to accurate, timely, and comprehensive risk assessments, national authorities—with the advice and assistance of regional and global public health agencies—should establish more robust surveillance systems, involving public hospitals and academic medical centers, manufacturers of diagnostics, and social network plat- forms. Epidemiologists should be alert to potential ascertainment biases regarding sampling frames and other methodological pitfalls, account for such biases during analysis and interpretation of the data and should notify authorities to take these biases into account and seek support for improving surveillance methods to better achieve represen- tativeness and sufficient geographical coverage. Tracing the Arrival and Community Transmission of a Virus Conclusion: COVID-19 showed that countries and intergovernmen- tal bodies need to bolster their surveillance capacities, especially the ability to look for the unexpected and unobserved and to sustain surveillance during disease surges. These systems can be strengthened PREPUBLICATION COPY—Uncorrected Proofs

SURVEILLANCE 45 by being repeatedly challenged to assess their ability to detect novel threats. Gaps identified can then be followed through and retested iteratively before an actual incident. Current surveillance approaches and tools are designed and more suitable for monitoring of known pandemics or the ongoing surveillance for seasonal influenza than for the early detection of a pandemic-capable pathogen before widespread transmission. Conclusion: COVID-19 showed that the set of core capacities should be broadened to take advantage of technological developments, includ- ing but not limited to, digital mobility data, sewage surveillance, and monitoring of open-access electronic data streams (digital surveillance), as well as to maintain a stockpile of basic supplies (such as nasal swabs) that will be needed to conduct tests). Full reporting of surveillance data, both to higher authorities within a country and to international agencies, is sometimes impeded by negative political or economic repercussions. For example, disciplin- ing local officials for reporting novel pathogens disincentivizes health surveillance. The first step in eliminating such barriers is to recognize their existence; such recognition can come from the parties involved or from observers. Unless such barriers are removed, reporting structures cannot provide complete, accurate, and timely information about pos- sible disease outbreaks. Harmonization of information from multiple data sources is es- sential for quickly identifying the origins and spread of novel agents and strains and for providing useful information for decision makers and the public. Harmonization rests on the development and use of instruments to standardize the data. When diverse data come from many sources and reflect clinical and public health differences at the local level, particularly in the early stages of a pandemic, organizations that collect the data may be able to develop means of standardizing the data after they have been submitted. Recommendation 2-3: National public health agencies should both strengthen the capabilities of local and provincial authorities to ac- curately, rapidly, and transparently report data about novel agents and strains and improve their own reporting of data to such regional orga- nizations and global bodies as the World Health Organization and the One Health Tripartite. The global bodies should develop methods to harmonize data from multiple sources, to enable prompt dissemination of useful, comprehensive data, especially to the national and regional organizations that have contributed to the data pool. Organizations to PREPUBLICATION COPY—Uncorrected Proofs

46 NON-VACCINE INFLUENZA INTERVENTIONS which data are submitted at all levels should work toward removing barriers and disincentives to making full and accurate reports. Recommendation 2-4: The World Health Organization and regional disease control agencies (e.g., European Centre for Disease Prevention and Control, Africa Centres for Disease Control and Prevention) should work with countries, and national governments should work with sub- national entities (counties, states, provinces), to harmonize, coordinate, and optimize surveillance activities, data collection, and sharing. REFERENCES Abbey, E. J., B. A. Khalifa, M. O. Oduwole, S. K. Ayeh, R. D. Nudotor, E. L. Salia, O. Lasisi, S. Bennett, H. E. Yusuf, and A. L. Agwu. 2020. The Global Health Security Index is not predictive of coronavirus pandemic responses among Organizations for Economic Cooperation and Development countries. PLOS ONE 15(10):e0239398. Aiello, A. E., A. Renson, and P. N. Zivich. 2020. Social media- and internet-based disease surveillance for public health. Annual Review of Public Health 41:101–118. Aitken, T., K. L. Chin, D. Liew, and R. Ofori-Asenso. 2020. Rethinking pandemic prepara- tion: Global health security index (GHSI) is predictive of COVID-19 burden, but in the opposite direction. The Journal of Infection 81(2):318–356. Allam, Z., G. Dey, and D. S. Jones. 2020. Artificial intelligence (AI) provided early detection of the coronavirus (COVID-19) in China and will influence future urban health policy internationally. AI 1(2):156–165. Alwan, N. A. 2020. Surveillance is underestimating the burden of the COVID-19 pandemic. The Lancet 396(10252):e24. Angelopoulos, A. N., R. Pathak, R. Varma, and M. I. Jordan. 2020. On identifying and miti- gating bias in the estimation of the COVID-19 case fatality rate. https://assets.pubpub. org/zeiuy9aq/71594236940800.pdf (accessed June 25, 2021). Anglemyer, A., T. H. M. Moore, L. Parker, T. Chambers, A. Grady, K. Chiu, M. Parry, M. Wilczynska, E. Flemyng, and L. Bero. 2020. Digital contact tracing technologies in epi- demics: A rapid review. Cochrane Database of Systematic Reviews (8). Baum, S.E., C. Machalaba, P. Daszak, R. H. Salerno, and W. B. Karesh. 2017. Evaluating One Health: Are we demonstrating effectiveness? One Health (3):5–10. BBC. 2021. Pingdemic: “We got close to complete shutdown.” BBC. 2021. Bendavid, E., B. Mulaney, N. Sood, S. Shah, R. Bromley-Dulfano, C. Lai, Z. Weissberg, R. Saavedra-Walker, J. Tedrow, A. Bogan, T. Kupiec, D. Eichner, R. Gupta, J. P. A. Ioannidis, and J. Bhattacharya. 2021. COVID-19 antibody seroprevalence in Santa Clara County, California. International Journal of Epidemiology 50(2):410–419 Bogich, T. L., R. Chunara, D. Scales, E. Chan, L. C. Pinheiro, A. A. Chmura, D. Carroll, P. Daszak, and J. S. Brownstein. 2012. Preventing pandemics via international development: A systems approach. PLOS Medicine 9(12):e1001354. Bradshaw, W. J., E. C. Alley, J. H. Huggins, A. L. Lloyd, and K. M. Esvelt. 2021. Bidirectional contact tracing could dramatically improve COVID-19 control. Nature Communications 12(1):232. Brammer, L., L. Blanton, S. Epperson, D. Mustaquim, A. Bishop, K. Kniss, R. Dhara, M. Nowell, L. Kamimoto, and L. Finelli. 2011. Surveillance for influenza during the 2009 influenza A (H1N1) pandemic—United States, April 2009–March 2010. Clinical Infec- tious Diseases 52(Suppl 1):S27–S35. PREPUBLICATION COPY—Uncorrected Proofs

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The COVID-19 pandemic has challenged the world's preparedness for a respiratory virus event. While the world has been combating COVID-19, seasonal and pandemic influenza remain imminent global health threats. Non-vaccine public health control measures can combat emerging and ongoing influenza outbreaks by mitigating viral spread.

Public Health Lessons for Non-Vaccine Influenza Interventions examines provides conclusions and recommendations from an expert committee on how to leverage the knowledge gained from the COVID-19 pandemic to optimize the use of public health interventions other than vaccines to decrease the toll of future seasonal and potentially pandemic influenza. It considers the effectiveness of public health efforts such as use of masks and indoor spacing, use of treatments such as monoclonal antibodies, and public health communication campaigns.

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