|Rapid Expert Consultation
Societal Experts Action Network (SEAN)
Evaluating COVID-19-Related Surveillance Measures for Decision-Making
|Mary T. Bassett2|
|This rapid expert consultation was produced through the Societal Experts Action Network (SEAN), an activity of the National Academies of Sciences, Engineering, and Medicine that is sponsored by the National Science Foundation.|
|SEAN links researchers in the social, behavioral, and economic sciences with decision makers to respond to policy questions arising from the COVID-19 pandemic. This project is affiliated with the National Academies’ Standing Committee on Emerging Infectious Diseases and 21st Century Health Threats, sponsored by the U.S. Department of Health and Human Services, Office of the Assistant Secretary for Preparedness and Response.|
|SEAN is interested in your feedback. Was this rapid expert consultation useful? For further inquiries regarding this rapid expert consultation or to send comments, contact email@example.com or (202) 334-3440.|
1 Princeton University and Member of SEAN Executive Committee.
2 New York State Department of Health and Co-chair of SEAN Executive Committee, and Member of Standing Committee on Emerging Infectious Diseases and 21st Century Health Threats.
3 University of Washington and Member of SEAN Executive Committee.
Copyright 2022 by the National Academy of Sciences. All rights reserved.
Decision makers continue to use data to inform COVID-19 policy and mitigation decisions, with an eye toward protecting the public. As the COVID-19 pandemic has continued to evolve, the types of data available have changed with the identification of new variants, the availability of COVID-19 vaccines, the introduction of new COVID-19 therapeutics, the reopening of the economy, and the relaxing of mitigation measures. Enhanced understanding of these data types can lead to more informed decisions. Accordingly, guidance is needed on how to collect, interpret, and report on the various types of COVID-19 data. The key COVID-19–related surveillance measures are summarized in Box 1.
This rapid expert consultation builds upon a previous consultation, Evaluating Data Types: A Guide for Decision Makers Using Data to Understand the Extent and Spread of COVID-19. When Evaluating Data Types was published in June 2020, the world was grappling with the original strain of SARSCoV-2, and COVID-19 vaccines did not yet exist. The Evaluating Data Types consultation applied five criteria (representativeness; bias; uncertainty, measurement, and sampling error; time; and space) to seven data types (number of confirmed cases, hospitalizations, emergency department visits, reported confirmed COVID-19 deaths, excess deaths, fraction of viral tests that are positive, and representative prevalence surveys [including both viral and antibody tests]) as a way of enabling decision makers to gain insight into the strengths and limitations of the available data.
Since Evaluating Data Types was published, the World Health Organization (WHO) (2022) has identified several SARS-CoV-2 variants of concern; the U.S. Food and Drug Administration (FDA) has authorized or approved the use of three COVID-19 vaccines (manufactured by Pfizer-BioNTech, Moderna, and Johnson & Johnson4); and therapeutics5 have been developed. Given the significant evolution in understanding of the virus and emerging tools for preventing and treating the disease, it is appropriate to revisit Evaluating Data Types to discuss the changing COVID-19 landscape and how data can be used to inform decision-making as the nation enters its third year of the pandemic. This rapid expert consultation highlights new and updated data measures and surveillance strategies that have emerged as knowledge of the coronavirus pandemic has evolved. It draws on the social, behavioral, and economic sciences to identify actionable guidance and best practices that state and local decision makers can use when collecting, interpreting, and reporting on COVID-19 data to inform policy decisions.6
THE CHANGING COVID-19 LANDSCAPE
The identification of multiple variants of concern, the deployment of COVID-19 vaccines, and the availability of COVID-19 tests have greatly changed the COVID-19 landscape in the United States. As a result, as the United States enters the third year of the pandemic, much more is known about
4 The Pfizer-BioNTech vaccine has full FDA approval for individuals 16 years of age and older and is available under emergency use authorization for individuals 5 years of age and older. The Moderna vaccine has full FDA approval for adults 18 and older. Johnson & Johnson’s Janssen vaccine is available under emergency use authorization for adults 18 and older.
6 The full statement of task for this rapid expert consultation states: “The National Academies of Sciences, Engineering, and Medicine will produce a rapid expert consultation to assist decision makers in understanding and interpreting data in light of the evolving COVID-19 pandemic. Drawing from research in the social, behavioral, and economic sciences, this document will identify actionable guidance and best practices that state and local government decision makers can use when collecting, interpreting, and reporting on COVID-19 data to inform policy decisions. This document will draw on findings in a previous rapid expert consultation, Evaluating Data Types: A Guide for Decision Makers using Data to Understand the Extent and Spread of COVID-19, and will be designed to be of practical use to decision makers, but will not recommend specific actions or include other recommendations. It will be reviewed in accordance with institutional guidelines.”
SARS-CoV-2, including the way it behaves and mutates, as well as how to combat it with mitigation measures, vaccines, and therapeutics.
COVID-19 vaccines have proven effective against hospitalization, severe illness, and death, and those who are unvaccinated are at greater risk of all three of these outcomes (Andrews et al., 2022; Bruxvoort et al., 2021).7 According to vaccination data from the Centers for Disease Control and Prevention (CDC),8 77 percent of adults have received at least one dose of a COVID-19 vaccine and 45 percent of adults have received a booster dose, although the rate of vaccination for youth under age 18 is substantially lower.9
In the early months of the COVID-19 pandemic, both rapid antigen and polymerase chain reaction (PCR) tests were difficult to access and often reserved for those who were symptomatic (NASEM, 2020a). The FDA started to grant emergency use authorization for rapid antigen tests only in late 2020.10 Throughout 2021 and early 2022,11 rapid antigen and PCR tests became more widely available and testing guidance evolved, taking into account safety requirements for such activities as school, work, travel, and social gatherings. At-home diagnostic tests12 have also greatly expanded testing capabilities, although these tests have at times been challenging to access, and a shortage was experienced during the omicron surge.13 As test accessibility has expanded, individuals who are asymptomatic or presymptomatic, and even those who have not been exposed to COVID-19 but require a test for other purposes, have been able to get tested, which has dramatically increased the number of tests administered.
In the context of the ever-evolving coronavirus pandemic, policy makers must make critical decisions in rapidly changing circumstances, even in the face of limited information and uncertainty about the best available data or evidence (Berger et al., 2021; Fox et al., 2022; NASEM, 2020a). Throughout the pandemic, state and local decision makers have been faced with “making decisions in the interest of public safety and health under conditions of tremendous uncertainty and time pressure” (Schippers and Rus, 2021), often with evolving or conflicting information. The Evaluating Data Types consultation identified some cautions that decision makers may wish to consider when making decisions with imperfect data (Box 2). These cautions remain helpful to consider as the context of the pandemic continues to evolve.
7 During October and November 2021, for example, “unvaccinated persons had 13.9 and 53.2 times the risks for infection and COVID-19-associated death, respectively, compared with fully vaccinated persons who received booster doses” (Johnson et al., 2022).
9 The Kaiser Family Foundation’s (KFF’s) COVID-19 Vaccine Monitor (2022) found that 57 percent of parents of teenagers aged 12–17 said their teen had been vaccinated, and 35 percent of parents with children aged 5–11 said their child had been vaccinated (Sparks et al., 2022).
Like the pandemic itself, data collection around COVID-19 has evolved, complicating the way data are interpreted. The data used to implement or ease COVID-19 mitigation measures earlier in the pandemic (Centers for Disease Control and Prevention [CDC], 2022a), such as positive case counts and test positivity rates, may need to be interpreted differently in the current context of variants, vaccines, therapeutics, and testing availability. During the omicron surge, for example, “although the rapid rise in cases has resulted in the highest number of COVID-19-associated ED [emergency department] visits and hospital admissions since the beginning of the pandemic…disease severity appears to be lower compared with previous high disease-transmission periods” (Iuliano et al., 2022). Seen as well was increased capacity for reinfection and breakthrough infections in individuals who had been vaccinated, which expanded the denominator of who could be infected (Bhattacharyya and Hanage, 2022). Moreover, when percentages are used to characterize some changing attribute of the population, it is necessary to be cautious about whether the denominator has changed in character
over time. For example, the percentage of positive tests among all tests has a very different interpretation when the reason for testing is self-perceived symptoms as opposed to when a regimen of periodic testing is implemented.
While data collection for many metrics has improved over the course of the pandemic, several emerging measures still are not well understood or tracked. The uptake of therapeutics, for example, including the number of prescriptions written by doctors and whether the therapeutics are reaching those in need, is an important measure for understanding the trajectory of the virus but is not currently well documented. Public health officials would also benefit from enhanced reporting of results of at-home tests to inform decision-making.
COVID-19–RELATED SURVEILLANCE MEASURES
As discussed in Evaluating Data Types, there are a number of data types that, when examined in combination, “form a clearer picture of how the disease is spreading and its severity” (NASEM, 2020a). The key COVID-19–related surveillance measures are summarized in Box 3.
In the current context, percent positive COVID-19 cases, hospitalizations, hospital strain, reported confirmed COVID-19 deaths, and vaccination rates are measures that may be considered when deciding whether to implement or ease mitigation measures. Emerging data types can also be used as real-time predictive measures for emerging COVID-19 outbreaks. These new data types can serve as leading indicators for monitoring disease in a population and identifying variants, and can aid in appropriately timing public health interventions (Kogan et al., 2021). Living with the virus demands techniques that allow for earlier and more accurate detection of population transmission and variants. Four surveillance techniques that have been used as leading indicators increasingly throughout the coronavirus pandemic—seroprevalence surveillance, wastewater surveillance, genome sequence testing and surveillance, and nowcasting—are likely to become increasingly important for responding to changes in disease spread and severity.
Percent Positive COVID-19 Cases
The positivity rate, or percent positive, is the percentage of all coronavirus tests performed that are positive, divided by the total number of tests administered and multiplied by 100. The availability of at-home test kits, which has led to a rapid increase in testing in the United States, has raised new challenges for interpreting this measure: because individuals are not required to report the results of home tests to public health agencies, public health officials cannot track the number of tests administered or the results.
To counter this problem, some states and localities have created reporting systems for use by residents to self-report results of COVID-19 at-home tests. Maryland, for example, uses the COVID Positive At-Home Test Report Portal,14 while Johnson County, Kansas, has a self-reporting form15 that allows residents to report their at-home test results voluntarily. States administer or provide both rapid antigen and PCR tests to individuals, but based on reporting requirements, they “may not be distinguishing overall tests administered from the number of individuals who have been tested” (Johns Hopkins University & Medicine, 2022). Both federal and state health agencies track COVID-19 tests administered and their results, but they often have access only to PCR tests administered by approved bodies. Note that COVID-19 is also a notifiable disease,16 which means that health care providers must notify the authorities of all cases of which they become aware.
The increased prevalence of vaccination has raised uncertainty as to whether the reasons for seeking a COVID-19 test have changed over time. People may seek tests because they perceive COVID-19 symptoms more or less severely than was the case when vaccination was less prevalent. Seeking tests may be a function of businesses returning to on-site work or of increasing travel to destinations requiring a negative test. Alternatively, vaccinated people may seek tests only when symptomatic. In any case, comparisons of the positivity rate over time can be confounded by different patterns of test seeking.
When discussing the COVID-19 positivity rate, it is important as well to document the sample size and composition to better understand what the rate represents. For example, Maryland’s Department of Health17 (2022) maintains a dashboard of key coronavirus data but notes that its “testing volume data represent the static daily total of PCR COVID-19 tests electronically reported; this count does not include test results submitted by labs and other clinical facilities through non-electronic means,” nor does it include home tests. Caution is therefore needed when using percent positive as a metric as it is prone to selection bias. Those who feel sick, are worried about being infected, or are already hospitalized may be the ones who are tested, thus skewing the percent positive higher. Alternatively, increased screening of asymptomatic individuals (e.g., for travel or social functions) may result in more negative reported results. This metric needs to be understood in the context of how much testing is being conducted in a community and who is being tested.
When it was published in June 2020, Evaluating Data Types characterized hospitalization data as “typically available quickly, but reflect[ing] only the most severe cases of infection and patients who were exposed to the virus several weeks before admission” (NASEM, 2020b). Although this characterization remains true today, available COVID-19 hospitalization data are now more nuanced. Increasingly, a distinction is being made between patients who are admitted for COVID-19 and those who are admitted with COVID-19 (Murray, Croci, and Wachter, 2022). The Massachusetts Department of Public Health’s COVID-19 Dashboard,18 for example, differentiates primary from incidental COVID-19 diagnosis for patients who are hospitalized.19 Incidental COVID-19 hospitalizations are important as they relate to hospital capacity and labor shortages (Carbajal, 2022); but, those data can be difficult for hospitals to track.20 Moreover, while hospitalization data can indicate community transmission, the spread of newer variants with different characteristics may mean those data do not always accurately reflect virus severity (Pananjady, 2022).
The strain placed on hospitals and the health care sector in general has been of great concern since the onset of the pandemic. Throughout 2020, the phrase “flatten the curve” was commonly used to express the idea of using mitigation measures to slow down the transmission of COVID-19 so as not to overburden health care systems. A CDC report measuring the impact of hospital strain on excess deaths during the pandemic found that the “COVID-19 surges have stressed hospital systems and negatively affected health care and public health infrastructures” (French et al., 2021).
The burden on health care infrastructure is an important consideration in decisions about whether to implement or roll back COVID-19 mitigation measures. The U.S. Department of Health and Human
19 Importantly, data differentiating between primary and incidental COVID-19 hospitalizations may not be available in all jurisdictions.
Services (HHS) collects hospital utilization data,21 including inpatient and intensive care unit (ICU) beds in use, which specify use by COVID-19 patients. According to the CDC’s COVID-19 Community Levels (CDC, 2022a), “new COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system.” Such factors as the availability of regular beds, ICU beds, staff, supplies, and equipment, as well as finances, signal the ability of hospitals in a state or region to provide adequate and appropriate services to all patients in need (Hassan and Mahmoud, 2021; Department of Health and Human Services, 2021).
Reported Confirmed COVID-19 Deaths
Hospitals and health care providers typically report COVID-19 deaths, but “these data reflect the state of the outbreak several weeks previously because of the long course of infection” (NASEM, 2020a). While reported deaths are an “accurate reflection of the course of the pandemic” (Irons and Raftery, 2021), there was for most of the pandemic no “standard definition for reporting of associated [COVID-19] deaths” (Council of State and Territorial Epidemiologists [CSTE], 2021). Reporting of COVID-19 deaths has likely improved since the early months of the pandemic, when some deaths among individuals who had not been tested for COVID-19 were misclassified. In collaboration with the CDC, the CSTE released guidance in December 2021 providing a consensus-based definition of COVID-19–associated deaths so health departments nationwide would “use the same criteria to count deaths among fatal COVID-19 cases” (CSTE, 2021).
These data continue to be lagging indicators, rising and falling behind the trends in positive cases and hospitalizations. However, they are an important factor in evaluating the disproportionate impacts of the pandemic across population subgroups. Because of their lagging nature, death rates “were eliminated from the list of potential indicators but retained as a potential outcome to assess the performance of selected community metrics” (CDC, 2022b). When making decisions at the state and local levels, therefore, it is important to consider reported confirmed COVID-19 deaths in relation to other measures in order to have a more contextualized view of the virus’s trajectory, as these data are an indicator of the burden of the pandemic.
The CDC defines “fully vaccinated” people as those who have received all doses in their primary series of COVID-19 vaccine (CDC, 2022c). To be considered “up to date,” however, people must have “received all doses in the primary series vaccine and one booster dose when eligible” (CDC, 2022c). Tracking overall vaccination rates and “up to date” vaccination rates will likely be important for making decisions at the state and local levels going forward as new variants emerge and vaccine-induced immunity wanes. It will also be important to disaggregate vaccination rate data to better understand the level of protection within different populations.
As COVID-19 vaccination and booster dose rates increase, larger portions of the population will be better protected against severe disease, and these data, along with other measures described above, may influence local decisions regarding mitigation strategies. COVID-19 vaccination coverage also can inform community actions with respect to vaccine outreach, campaigns, distribution, and equity, which in turn can inform local prevention decisions (CDC, 2022a). Such local information as community vaccination coverage and results of surveillance testing can help inform decision-making not only at the local but also at the state level (CDC, 2022a).
Seroprevalence surveys use antibody tests to estimate the percentage of people in a population who have antibodies against SARS-CoV-2. They provide estimates of how many people in a specific population, at different points in time, or in different locations, may have been infected previously with SARS-CoV-2 (CDC, 2020). Seroprevalence measures are helpful in that estimates can be made by testing a sample of the population rather than the entire population. The CDC has been leading a variety of seroprevalence surveys including (1) large-scale geographic surveys in which nonrepresentative testing is performed on blood samples that are not linked to names, as well as blood samples that were collected for other purposes (e.g., routine cholesterol tests); (2) community-level surveys that cover smaller areas but allow for a more representative sample; and (3) surveys of special populations, such as health care workers or pregnant women (CDC, 2021a). The CDC has been collaborating with state, local, territorial, academic, and commercial partners across the 50 states to conduct these surveys (CDC, 2021a).
Caution is necessary, however, in interpreting the results of seroprevalence surveys. The results may not be generalizable nationwide because people who donate or submit blood for laboratory tests may differ from the general population (CDC, 2021a). Because these surveys are not based on a representative sample of the population, they are likely to yield biased results. Representative random sample surveys, currently used in the United Kingdom, for example, avoid these biases and therefore warrant consideration for use in the United States as well. An ongoing infection survey based on a random sample of the national population (such as the UK Office of National Statistics infection survey) would improve U.S. monitoring capabilities (Elliot et al., 2022; Office for National Statistics, 2022a; Office for National Statistics, 2022b).
The CDC established the National Wastewater Surveillance System in September 2020 to “coordinate and build the nation’s capacity to track the presence of SARS-CoV-2…in wastewater samples collected across the country” (CDC, 2022d). According to an Environmental Protection Agency (EPA) report, wastewater surveillance is a “community-level approach for monitoring disease or chemical biomarkers that are excreted in human urine and feces and collected in sewers,” and can detect disease in individuals who are asymptomatic, presymptomatic, and symptomatic (EPA, 2021). Thus the COVID-19 wastewater signal may be a leading indicator that precedes trends in confirmed diagnoses and hospitalization rates (Harris-Lovett et al., 2021; Karthikeyan et al., 2021). Wastewater surveillance is a targeted approach that, by pooling a large population into one sample, is both cost- and time-efficient (CDC, 2022e). Importantly, moreover, it is not dependent on patterns of testing. The use of wastewater surveillance is uneven throughout the United States.22 As shown by the CDC’s SARS-CoV-2 RNA Levels in Wastewater tracker, the majority of wastewater surveillance and reporting is conducted in the Northeast and Midwest. One example is the Sewershed Surveillance Project,23 a collaborative effort of the Missouri Department of Health and Senior Services, the Missouri Department of Natural Resources, and the University of Missouri. The wastewater surveillance team collects samples from 100 participating community water systems across Missouri to track the progression of SARS-CoV-2 in the state. Another example is the collaboration between the Wisconsin Department of Health Services, Wisconsin State Lab of Hygiene, and the University of Wisconsin-Milwaukee,24 which collects and tests wastewater samples from sewersheds across the
state. This technique allows the state to detect transmission trends and emerging variants within communities. These state- and local-driven efforts show the practical feasibility of wastewater surveillance for broader population surveillance.
Wastewater surveillance has also been used on college campuses as part of a multipronged approach to identifying positive cases. Some colleges and universities have their own dedicated wastewater management plants, a situation that lends itself to surveillance of a discrete population. The University of Arizona, for example, started using wastewater monitoring during the fall 2020 term as part of its Test All Test Smart program (NASEM, 2020b). Testing of wastewater samples by the university’s Water and Energy Sustainable Technology Center was able to identify the presence of the coronavirus and trace it to one location, revealing multiple asymptomatic students (EPA, 2021; Betancourt et al., 2021). Wastewater surveillance has been successful in identifying cases in individual dormitory buildings, “in some cases detecting a single presymptomatic individual in a dormitory population of 150+, and allowing for their isolation prior to further spread of the virus” (Gibas et al., 2021).
Wastewater surveillance has also been used by correctional facilities nationwide. The CDC partnered with the Water Environment Federation to launch several pilot programs for COVID-19 monitoring at correctional facilities (EPA, 2021).25 In addition, Ohio’s Department of Rehabilitation and Correction26 started testing wastewater samples in 2020 across its facilities. Wastewater surveillance has helped head off outbreaks in correctional facilities by identifying COVID-19 in sewage several days before an incarcerated person or staff member has tested positive, giving facility personnel time to limit interactions and slow the spread of the virus. Wastewater surveillance can also be a powerful tool in identifying coronavirus mutations and potential variants, particularly when paired with genome sequence testing.
At the same time, however, many factors apart from population COVID-19 prevalence can influence RNA concentrations in wastewater, impeding its epidemiological value. These factors include (1) shedding-related factors, including fecal shedding parameters (i.e., shedding pattern, recovery, rate, and load distribution); (2) population size; (3) in-sewer factors, including solid particles, organic load, travel time, flow rate, and wastewater pH and temperature; and (4) sampling strategy (Bertels et al., 2022). Further research is needed to identify, quantify, and adjust for these factors.
Genome Sequence Testing and Surveillance
Genome sequence testing is “the analysis of viral and microbial genomes in order to make inferences about pathogen evolution, transmission, and spread” (Crits-Christoph et al., 2021). In short, genomic sequencing can be used to “characterize the virus, estimate a particular variant’s prevalence in a population, evaluate how effective medical treatments…are against variants, and investigate the spread of a virus in outbreaks” (CDC, 2022f). In addition to tracking the spread of a virus, genomic sequencing is used to conduct surveillance, or the monitoring of genomic changes over time, by “collecting enough sequence data from representative populations to detect new variants and monitor trends in circulating variants” (CDC, 2022f). Genomic sequencing and surveillance can be used to inform public health decisions. Researchers in South Africa and Botswana, for example, were able to
25 Through this pilot program, the state of Oklahoma has been able to bring testing capabilities onsite at the correctional facility, so “the data can be produced and analyzed in hours, allowing correctional facilities to take action, which can help to make a difference in the spread of COVID-19” (Water Environment Federation, 2021).
detect the beta and delta variants in December 2020 and May 2021, respectively, using genomic surveillance, allowing the Departments of Health to take quick action (WHO Africa, 2021).
The CDC operates the SARS-CoV-2 Sequencing for Public Health Emergency Response, Epidemiology and Surveillance (SPHERES)27 consortium to coordinate coronavirus sequencing across the United States. Genome sequencing capacity has expanded in the United States through efforts by federal agencies, state and local public health laboratories, academic institutions, corporations, nonprofit public health and research laboratories, and international collaboration (CDC, 2021b). Through these partnerships, the United States submitted 1,189,459 sequences during June 2021–January 2022, with an average of 35,431 sequences per week (Lambrou et al., 2022). At the state level, Wisconsin’s Laboratory of Hygiene, operated at the University of Wisconsin–Madison, maintains the Wisconsin SARS-CoV-2 (hCoV-19) Genomic Database,28 which contains information on the prevalence of COVID-19 variants throughout the state using data provided by the Wisconsin Department of Health Services and the Global Initiative on Sharing All Influenza Data (GISAID) database.29 Another example is California, which developed the SARS-CoV-2 Whole Genome Sequencing Initiative (COVIDNet),30 a public–private collaboration among the California Department of Public Health, private partners, academic institutions, and local public health laboratories.
Wastewater surveillance can be used in tandem with genome sequencing. While wastewater surveillance alone can identify positive COVID-19 cases, “the strength of wastewater-based sampling and sequencing lies in the ability to identify alternative genotypes in the population being sampled” (Crits-Christoph et al., 2021). New York City combines wastewater surveillance and genome sequencing strategies to track variants in its communities (Smyth et al., 2022). Starting in January 2021, Smyth and colleagues (2022) sequenced SARS-CoV-2 RNA from 14 wastewater treatment plants in New York City twice a month, which allowed them to “classify suites of mutations” and identify “the distributions and trends in viral lineages.”
Real-time analyses of disease data are often complicated by the lag in data reporting. Nowcasting31 methods provide “close to real-time estimates of the complete number of events using the incomplete time-series of currently reported events by using information about the reporting delays from the past” (Bergström et al., 2022). This method has been applied to infectious disease outbreaks, including SARS-CoV-2, and is used for disease surveillance by “estimating the number of occurred-but-not-yet-reported events” (McGough et al., 2020). The CDC, for example, uses nowcasting32 to estimate the prevalence of COVID-19 variants across the United States based on genomic surveillance data. And to support policy makers, the COVID-19 Portal33 of the University of Georgia’s Center for the Ecology of Infectious Diseases provides nowcasting estimates of the number of infections for all U.S. states and territories.
Nowcasting has been used at the state and local levels to estimate the size of COVID-19 outbreaks and trends. The New York City Department of Health and Mental Hygiene (DOHMH), for example,
31 Epidemic nowcasting broadly refers to assessing the current state by understanding key pathogenic, epidemiologic, clinical and socio-behavioral characteristics of an ongoing outbreak through the use of statistical adjustments to fill in events that have not yet reported, offering health officials a more up-to-date picture for situational awareness (Mavragani, 2020).
used nowcasting during the first wave of COVID-19, from March to May 2020, to “support real-time situational awareness and resource allocation…and [assist] DOHMH leadership in anticipating the magnitude and timing of hospitalizations and deaths” (Greene et al., 2021).
The COVID-19 landscape has changed greatly since the onset of the pandemic in the United States. COVID-19–related surveillance measures and data can help decision makers detect, track, and monitor changes and trends in disease spread. Taken together and used in combination, these data form a clearer picture of how the disease is spreading and its severity, supporting decision makers in considering whether to implement or ease COVID-19 mitigation measures. As the pandemic extends into its third year, real-time predictive surveillance measures that allow for earlier and more accurate detection of population transmission and variants will provide key data for decision makers. Investments in such critical public health data infrastructure at the local, state, and federal levels are needed to ensure proper deployment of mitigation strategies and protection of the public.
SEAN is interested in your feedback. Was this rapid expert consultation useful? Send comments to firstname.lastname@example.org or (202) 334-3440.
Andrews, N., Stowe, J., Kirsebom, F., Toffa, S., Rickeard, T., Gallagher, E., Gower, C., Kall, M., Groves, N., O’Connell, A.M., Simons, D., Blomquist, P.B., Zaidi, A., Nash, S., Iwani Binti Abdul Aziz, N., Thelwall, S., Dabrera, B., Myers, R., Amirthalingam, … and Lopez Bernal, J. (2022). COVID-19 Vaccine Effectiveness against the omicron (B.1.1.529) variant. New England Journal of Medicine. Available: https://doi.org/10.1056/NEJMoa2119451.
Berger, L., Berger, N., Bosetti, V., Gilboa, I., Hansen, L., Jarvis, C., Marinacci, M., and Smith, R. (2021). Rational policymaking during a pandemic. Proceedings of the National Academy of Sciences, 18(4), e2012704118. Available: https://doi.org/10.1073/pnas.2012704118.
Bergström, F., Günther, F., Höhle, M., and Britton, T. (2022). Flexible Bayesian Nowcasting with application to COVID-19 fatalities in Sweden. ArXiv, 2202.04569v2. Available: https://doi.org/10.48550/arXiv.2202.04569.
Bertels, X., Demeyer, P., Van den Bogaert, S., Boogaerts, T., van Nuijs, A., Delputte, P., and Lahousse, L. (2022). Factors influencing SARS-CoV-2 RNA concentrations in wastewater up to the sampling stage: A systematic review. Science of the Total Environment, 820, 153290. Available: https://doi.org/10.1016/j.scitotenv.2022.153290.
Betancourt, W., Schmitz, B., Innes, G., Prasek, S., Brown, K., Stark, E., Foster, A., Sprissler, R., Harris, D., Sherchan, S., Gerba, C., and Pepper, I. (2021). COVID-19 containment on a college campus via wastewater-based epidemiology, targeted clinical testing and an intervention. Science of the Total Environment, 779, 146408. Available: https://doi.org/10.1016/j.scitotenv.2021.146408.
Bhattacharyya, R. and Hanage, W. (2022). Challenges in inferring intrinsic severity of the SARS-CoV2 omicron variant. New England Journal of Medicine, 386:e14. Available: https:://doi.org/10.1056/NEJMp2119682.
Bruxvoort, K., Sy, L., Ackerson, B., Luo, Y., Lee, G., Tian, Y., Florea, A., Aragones, M., Tubert, J., Takhar, H., Ku, J., Paila, Y., Talarico, C., and Tseng, H. (2021). Effectiveness of mRNA-1273 against delta, mu, and other emerging variants of SARS-CoV-2: Test negative case-control study. British Medical Journal, 375, e068848. Available: http://dx.doi.org/10.1136/bmj-2021-068848.
Carbajal, E. (2022, January 10). Where people go wrong with “incidental” COVID-19 hospitalizations. Becker’s Healthcare. Available: https://www.beckershospitalreview.com/public-health/where-people-go-wrong-with-incidental-covid-19-hospitalizations.html
Centers for Disease Control and Prevention (CDC). (2020). What COVID-19 seroprevalence surveys can tell us. Available: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/seroprevalance-surveys-tell-us.html.
———. (2021a). CDC seroprevalence survey types. Available: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/seroprevalence-types.html#geographic-surveys.
_____. (2021b). SARS-CoV-2 Sequencing for Public Health Emergency Response, Epidemiology, and Surveillance. Last updated April 9, 2021. Available: https://www.cdc.gov/coronavirus/2019-ncov/variants/spheres.html.
———. (2022a). COVID-19 community levels. Last updated March 24, 2022. Available: https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels.html.
———. (2022b). Science brief: Indicators for monitoring COVID-19 community levels and making public health recommendations. Last updated March 4, 2022. Available: https://www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/indicators-monitoring-community-levels.html.
———. (2022c). Stay up to date with your COVID-19 vaccines. Last updated April 21, 2022. Available: https://www.cdc.gov/coronavirus/2019-ncov/vaccines/stay-up-to-date.html.
———. (2022d). National Wastewater Surveillance System (NWSS). Available: https://www.cdc.gov/healthywater/surveillance/wastewater-surveillance/wastewater-surveillance.html.
———. (2022e). Targeted wastewater surveillance at facilities and institutions. Available: https://www.cdc.gov/healthywater/surveillance/wastewater-surveillance/targeted-use-case.html.
———. (2022f). What is genomic surveillance? Last updated January 24, 2022. Available: https://www.cdc.gov/coronavirus/2019-ncov/variants/genomic-surveillance.html.
Council of State and Territorial Epidemiologists (CSTE). (2021). Interim guidance for public health surveillance programs for classification of COVID-19-associated deaths among COVID-19 cases. Available:
Crits-Christoph, A., Kantor, R., Olm, M., Whitney, O., Al-Shayeb, B., Lou, Y., Flamholz, A., Kennedy, L., Greenwald, H., Hinkle, A., Hetzel, J., Spitzer, S., Koble, J., Tan, A., Hyde, F., Schroth, G., Kuersten, S., Banfield, J., and Nelson, K. (2021). Genome sequencing of sewage detects regionally prevalent SARS-CoV-2 variants. mBio, 12, e02703-20. Available: https://doi.org/10.1128/mBio.02703-20.
Elliott, P., Eales, O., Steyn, N., Tang, D., Bodinier, B., Wang, H., Elliott, J., Whitaker, M., Atchison, C., Diggle, P., Trotter, A., Ashby, D., Barclay, W., Taylor, G., Ward, H., Darzi, A., Cooke, G., Donnelly, C., and Chadeau-Hyam, M. (2022). Twin peaks: The omicron SARS-CoV-2 BA. 1 and BA. 2 epidemics in England. Available: http://hdl.handle.net/10044/1/96170.
Environmental Protection Agency (EPA). (2021). A compendium of U.S. wastewater surveillance for COVID-19 public health efforts. EPA-830-R-21-004. Available: https://www.epa.gov/sustainable-water-infrastructure/compendium-us-wastewater-surveillance-support-covid-19-public.
Fox, S., Lachmann, M., Tec, M., Pasco, R., Woody, S., Du, Z., Wang, X., Ingle, T., Javan, E., Dahan, M., Gaither, K., Escott, M., Adler, S., Johnson, S.C., Scott, J., and Meyers, L. (2022). Real-time pandemic surveillance using hospital admissions and mobility data. Proceedings of the National Academy of Sciences, 119(7), e2111870119. Available: https://doi.org/10.1073/pnas.2111870119.
French, G., Hulse, M., Nguyen, D., Sobotka, K., Webster, K., Corman, J., Aboagye-Nyame, B., Dion, M., Johnson, M., Zalinger, B., and Ewing, M. (2021). Impact of Hospital Strain on Excess Deaths During the COVID-19 Pandemic - United States, July 2020-July 2021. Morbidity and Mortality Weekly Report, 70(46), 1613–1616. https://doi.org/10.15585/mmwr.mm7046a5
Gibas, C., Lambirth, K., Mittal, N., Juel, M.A., Barua, V., Brazell, L., Hinton, K., LOntai, J., Stark, N., Young, I., Quach, C., Russ, M., Kauser, J., Nicolosi, B., Chen, D., Akella, S., Tang, W., Schleuter, J., and Munir, M. (2021). Implementing building-level SARS-CoV-2 wastewater surveillance on a university campus. Science of the Total Environment, 782, 146749. Available: https://doi.org/10.1016/j.scitotenv.2021.146749.
Greene, S., McGough, S., Culp, G., Graf, L., Lipsitch, M., Menzies, N., and Kahn, R. (2021). Nowcasting for real-time COVID-19 tracking in New York City: An evaluation using reportable disease data from early in the pandemic. JMIR Public Health Surveillance, 17(1), e25538. Available: https://doi.org/10.2196/25538.
Harris-Lovett, S., Nelson, K., Beamer, P., Bischel, H., Bivins, A., Bruder, A., Butliner, C., Camenisch, T., De Long, S., Karthikeyan, S., Larsen, D., Meierdiercks, K., Mouser, P., Pagsuyoin, S., Prasek, S., Radniecki, T., Ram, J., Roper, D., Safford, H., …and Korfmacher, K. (2021). Wastewater surveillance for SARS-CoV-2 on college campuses: Initial efforts, lessons learned, and research needs. International Journal of Environmental Research and Public Health, 18(9), 4455. Available: https://doi.org/10.3390/ijerph18094455.
Hassan, E. and Mahmoud, H. (2021). Impact of multiple waves of COVID-19 on healthcare networks in the United States. PLoS ONE, 16(3), e0247463. Available: https://doi.org/10.1371/journal.pone.0247463.
Hughes, H.E., Edeghere, O., O’Brien, S.J., Vivancos, R., and Elliot, A. (2020). Emergency department syndromic surveillance systems: A systematic review. BMC Public Health, 20, 1891. Available: https://doi.org/10.1186/s12889-020-09949-y.
Irons, N., and Raftery, A. (2021). Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys. Proceedings of the National Academy of Sciences, 118, 31. Available: https://www.pnas.org/doi/pdf/10.1073/pnas.2103272118.
Iuliano, A. D., Brunkard, J. M., Boehmer, T. K., Peterson, E., Adjei, S., Binder, A. M., Cobb, S., Graff, P., Hidalgo, P., Panaggio, M.J., Rainey, J.J., Rao, P., Soetebier, K., Wacaster, S., Ai, C., Gupta, V., Molinari, N.M., Matthew D., and Ritchey, M.D. (2022). Trends in disease severity and health care utilization during the early omicron variant period compared with previous SARS-CoV-2 high transmission periods—United States, December 2020–January 2022. Morbidity and Mortality Weekly Report, 71, 4. Available: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm.
Johns Hopkins University & Medicine. (2022). Daily state-by-state testing trends. Coronavirus Resource Center. Available: https://coronavirus.jhu.edu/testing/individual-states.
Karthikeyan, S., Nguyen, A., McDonald, D., Zong, Y., Ronquillo, N., Ren, J., Zou, J., Farmer, S., Humphrey, G., Henderson, D., Javidi, T., Messer, K., Anderson, C., Schooley, R., Martin, N., and Knight, R. (2021). Rapid, large-scale wastewater surveillance and automated reporting system enable early detection of nearly 85% of COVID-19 cases on a university campus.
mSystems, 6, e00793-21. Available: https://doi.org/10.1128/mSystems.00793-21.
Kogan, N., Clemente, L., Liautaud, P., Kaashoek, J., Link, N., Nguyen, A., Lu, F., Huybers, P. Resch, B., Havas, C., Petutschnig, A., Davis, J., Chinazzi, M., Mustafa, B., Hanage, W., Vespignani, A., and Santillana, M. (2021). An early warning approach to monitor COVID-19 activity with multiple digital traces in near time. Science Advances, 7(10), eabd6989. Available: https://doi.org/10.1126/sciadv.abd6989.
Lambrou, A.S., Shirk, P., Steele, M.K., Paul, P., Paden, C., Cadwell, B., Reese, H., Aoki, Y., Hassell, N., Caravas, J., Kovacs, N., Gerhart, J., Ng, H., Zheng, K-Y., Beck, A., Chau, R., Cintron, R., Cook, P., Gulvik, C., Howard, D., …and Wentworth, E. (2022). Genomic surveillance for SARS-CoV-2 Variants: Predominance of the Delta (B.1.617.2) and Omicron (B.1.1.529) Variants—United States, June 2021–January 2022. Morbidity and Mortality Weekly Report, 71, 206–211. Available: http://dx.doi.org/10.15585/mmwr.mm7106a4
Maryland Department of Health. (2022). Maryland COVID-19 Data Dashboard. Coronavirus Disease 2019 (COVID-19) Outbreak. Available: https://coronavirus.maryland.gov/.
Mavragani, A. (2020). Tracking COVID-19 in Europe: Infodemiology approach. JMIR public health and surveillance, 6(2), e18941.
McGough, S., Johansson, M., Lipsitch, M., and Menzies, N. (2020). Nowcasting by Bayesian smoothing: A flexible, generalizable model for real-time epidemic tracking. PLoS Computational Biology, 16(4), e1007735. Available: https://doi.org/10.1371/journal.pcbi.1007735.
Murray, S., Croci, R., and Wachter, R. (2022, January 7). Is a patient hospitalized ‘with’ COVID or ‘for’ COVID? It can be hard to tell. The Washington Post. Available: https://www.washingtonpost.com/outlook/2022/01/07/hospitalization-covid-statistics-incidental/.
National Academies of Sciences, Engineering, and Medicine (NASEM). (2020a). Evaluating data types: A guide for decision makers using data to understand the extent and spread of COVID-19. Washington, DC: The National Academies Press. Available: https://doi.org/10.17226/25826.
______. (2020b). Evaluating data types: A guide for decision makers using data to understand the extent and spread of COVID-19. Washington, DC: The National Academies Press. Available: https://doi.org/10.17226/25826.
Office for National Statistics (2022a). Coronavirus (COVID-19) infection survey: Methods and further information. Available: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/methodologies/covid19infectionsurveypilotmethodsandfurtherinformation.
_____. (2022b). Coronavirus (COVID-19) latest insights: Antibodies. Available: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/antibodies.
Pananjady, K. (2022, February 14). How to use and understand always-evolving COVID-19 data. The Philadelphia Inquirer. Available: https://www.inquirer.com/health/coronavirus/covid-numbers-data-cases-hospitalizations-deaths-guide-20220214.html.
Schippers, M., and Rus, D. (2021). Times of COVID-19: Using reflexivity to counteract information-processing failures. Frontiers in Psychology, 12, 650525. Available: https://doi.org/10.3389/fpsyg.2021.650525.
Smyth, D., Trujillo, M., Gregory, D., Cheung, K., Gao, A., Graham, M., Guan, Y., Guldenpfennig, C., Hoxie, I., Kannoly, S., Kubota, N., Lyddon, T., Markman, M., Rushford, C., San, K., Sompanya, G., Spagnolo, F., Suarez, R., Teixeiro, E., Daniels, M., Johnson, M. and Dennehy, J. (2022). Tracking cryptic SARS-CoV-2 lineages detected in NYC wastewater. Nature Communications, 13, 635. Available: https://doi.org/10.1038/s41467-022-28246-3.
Sparks, G., Kirzinger, A., Hamel, L., Stokes, M., Montero, A., and Brodie, M. (2022). KFF COVID-19 Vaccine Monitor: February 2022. Kaiser Family Foundation. Available: https://www.kff.org/coronavirus-covid-19/poll-finding/kff-covid-19-vaccine-monitor-february-2022.
U.S. Department of Health and Human Services. (2021). Hospitals reported that the COVID-19 pandemic has significantly strained health care delivery—Results of a national pulse survey February 22–26, 2021. Office of the Inspector General. OEI-09-21-00140. Available: https://oig.hhs.gov/oei/reports/OEI-09-21-00140.pdf.
Water Environment Federation. (2021). Innovative pilot program for COVID-19 monitoring launched at Oklahoma Correctional Facilities. Available: https://www.wef.org/resources/pressroom/press-releases2/wef-press-releases/innovative-pilot-program-for-covid-19-monitoring-launched-at-oklahoma-correctional-facilities/.
World Health Organization (WHO). (2022). Tracking SARS-CoV-2 variants. Available: https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/.
World Health Organization Africa (WHO Africa) (2021, October 4). Why genomic sequencing is crucial in COVID-19 response. Available: https://www.afro.who.int/news/why-genomic-sequencing-crucial-covid-19-response.
We thank the sponsors of SEAN—the National Science Foundation—and of the Standing Committee on Emerging Infectious Diseases and 21st Century Health Threats—the U.S. Department of Health and Human Services, Assistant Secretary for Preparedness and Response.
Special thanks go to the SEAN executive committee, who dedicated time and thought to this project: Mary T. Bassett (co-chair), New York State Department of Health; Robert M. Groves (co-chair), Georgetown University; Dolores Acevedo-Garcia, Brandeis University; Mahzarin R. Banaji, Harvard University; Dominique Brossard, University of Wisconsin–Madison; Janet Currie, Princeton University; Michael Hout, New York University; Maria Carmen Lemos, University of Michigan; Adrian E. Raftery, University of Washington; and Wendy Wood, University of Southern California. We thank as well the Standing Committee on Emerging Infectious Diseases and 21st Century Health Threats.
We extend gratitude to the staff of the National Academies of Sciences, Engineering, and Medicine, in particular to Emily P. Backes, Malvern T. Chiweshe, and Chelsea Fowler, who contributed research, editing, and writing assistance. We thank Mary Ghitelman, who led the communication and dissemination of the project. Thanks are also due to Elizabeth Tilton who managed the administrative aspects of the project and assisted with report production. From the Division of Behavioral and Social Sciences and Education, we thank Kirsten Sampson Snyder, who shepherded the report through the review process. We thank as well Rona Briere and Allie Boman for their skillful editing.
To supplement their own expertise, the authors received input from several external sources, whose willingness to share their perspectives and expertise was essential to this work. We thank Mollyann Brodie, Kaiser Family Foundation; Ron Carlee, Old Dominion University; Nicholas Christakis, Yale University; Sam Clark, Ohio State University; Natalie Dean, Emory University; David Dowdy, Johns Hopkins University; Bill Hanage, Harvard University; Joneigh Khaldun, CVS Health; Linda Langston, Langston Strategy Group; Rodrick Little, University of Michigan; Andrew Noymer, University of California – Irvine; Colm O’Muircheartaigh, University of Chicago; Ian Pepper, University of Arizona; and Paul Simon, Los Angeles County Department of Public Health.
We also thank the following individuals for their review of this rapid expert consultation: Cherl Bettigole, Health Commissioner, Philadelphia Department of Public Health; James S. House, Survey Research Center, Institute for Social Research, University of Michigan; Christopher J.L. Murray, Institute for Health Metrics and Evaluation, University of Washington; Emilio Zagheni, Executive Director, Max Planck Institute for Demographic Research
Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the conclusions of this document, nor did they see the final draft before its release. The review of this document was overseen by Alicia L. Carriquiry, Department of Statistics, Iowa State University, and Robert A. Moffitt, Department of Economics, Johns Hopkins University. They were responsible for making certain that an independent examination of this rapid expert consultation was carried out in accordance with the standards of the National Academies and that all review comments were carefully considered. Responsibility for the final content rests entirely with the authors and has been reviewed and approved for release by the National Academies.
SOCIETAL EXPERTS ACTION NETWORK (SEAN) EXECUTIVE COMMITTEE
MARY T. BASSETT (Co-chair), New York State Department of Health
ROBERT M. GROVES (Co-chair), Georgetown University
DOLORES ACEVEDO-GARCIA, Brandeis University
MAHZARIN R. BANAJI, Harvard University
DOMINIQUE BROSSARD, University of Wisconsin–Madison
JANET CURRIE, Princeton University
MICHAEL HOUT, New York University
MARIA CARMEN LEMOS, University of Michigan-Ann Arbor
ADRIAN E. RAFTERY, University of Washington
WENDY WOOD, University of Southern California
EMILY P. BACKES, Senior Program Officer
MALVERN T. CHIWESHE, Program Officer
CHELSEA FOWLER, Associate Program Officer
ELIZABETH TILTON, Senior Program Assistant