Screening Strategies to Reduce COVID-19 Mortality in Nursing Homes.

Importance: Nursing home residents continue to bear a disproportionate share of COVID-19 morbidity and mortality, accounting for 9% of all US COVID-19 deaths in 2023, despite comprising only 0.4% of the population. Objective: To evaluate the cost-effectiveness of screening strategies in reducing COVID-19 mortality in nursing homes. Design and Setting: An agent-based model was developed to simulate SARS-CoV-2 transmission in the nursing home setting. Parameters were determined using SARS-CoV-2 virus data and COVID-19 data from the Centers for Medicare & Medicaid Services and US Centers for Disease Control and Prevention that were published between 2020 and 2023, as well as data on nursing homes published between 2010 and 2023. The model used in this study simulated interactions and SARS-CoV-2 transmission between residents, staff, and visitors in a nursing home setting. The population used in the simulation model was based on the size of the average US nursing home and recommended staffing levels, with 90 residents, 90 visitors (1 per resident), and 83 nursing staff members. Exposure: Screening frequency (none, weekly, and twice weekly) was varied over 30 days against varying levels of COVID-19 community incidence, booster uptake, and antiviral use. Main Outcomes and Measures: The main outcomes were SARS-CoV-2 infections, detected cases per 1000 tests, and incremental cost of screening per life-year gained. Results: Nursing home interactions were modeled between 90 residents, 90 visitors, and 83 nursing staff over 30 days, completing 4000 to 8000 simulations per parameter combination. The incremental cost-effectiveness ratios of weekly and twice-weekly screening were less than $150 000 per resident life-year with moderate (50 cases per 100 000) and high (100 cases per 100 000) COVID-19 community incidence across low-booster uptake and high-booster uptake levels. When COVID-19 antiviral use reached 100%, screening incremental cost-effectiveness ratios increased to more than $150 000 per life-year when booster uptake was low and community incidence was high. Conclusions and Relevance: The results of this cost-effectiveness analysis suggest that screening may be effective for reducing COVID-19 mortality in nursing homes when COVID-19 community incidence is high and/or booster uptake is low. Nursing home administrators can use these findings to guide planning in the context of widely varying levels of SARS-CoV-2 transmission and intervention measures across the US.

Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations.

Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and have often lacked transparency in terms of prioritization of false-positive versus false-negative signals. They have also struggled to maintain relevance over time due to slow and infrequent updates addressing new variants and shifts in vaccine- and infection-induced immunity. We make two contributions to address these weaknesses. We first present a framework to evaluate predictive accuracy based on policy targets related to severe disease and mortality, allowing for explicit preferences toward false-negative versus false-positive signals. This approach allows policymakers to optimize metrics for specific preferences and interventions. Second, we propose a method to update risk thresholds in real time. We show that this adaptive approach to designating areas as "high risk" improves performance over static metrics in predicting 3-wk-ahead mortality and intensive care usage at both state and county levels. We also demonstrate that with our approach, using only new hospital admissions to predict 3-wk-ahead mortality and intensive care usage has performed consistently as well as metrics that also include cases and inpatient bed usage. Our results highlight that a key challenge for COVID-19 risk prediction is the changing relationship between indicators and outcomes of policy interest. Adaptive metrics therefore have a unique advantage in a rapidly evolving pandemic context.

First Do No Harm? Modeling Risks and Benefits of Challenge Trials for Hepatitis C Vaccine Development.

BACKGROUND: In 2019, about 58 million individuals were chronically infected with hepatitis C virus. Some experts have proposed challenge trials for hepatitis C virus vaccine development. METHODS: We modeled incremental infections averted through a challenge approach, under varying assumptions regarding trial duration, number of candidates, and vaccine uptake. We computed the benefit-risk ratio of incremental benefits to risks for challenge versus traditional approaches. We also benchmarked against monetary costs of achieving incremental benefits through treatment. RESULTS: Our base case assumes 3 vaccine candidates, each with an 11% chance of success, corresponding to a 30% probability of successfully developing a vaccine. Given this probability, and assuming a 5-year difference in duration between challenge and traditional trials, a challenge approach would avert an expected 185 000 incremental infections with 20% steady-state uptake compared to a traditional approach and 832 000 with 90% uptake (quality-adjusted life-year benefit-risk ratio, 72 000 & 323 000). It would cost at least $92 million and $416 million, respectively, to obtain equivalent benefits through treatment. BRRs vary considerably across scenarios, depending on input assumptions. CONCLUSIONS: Benefits of a challenge approach increase with more vaccine candidates, faster challenge trials, and greater uptake.

Prevalence and Risk Factors for School-Associated Transmission of SARS-CoV-2.

Importance: School-associated SARS-CoV-2 transmission is described as uncommon, although the true transmission rate is unknown. Objective: To identify the SARS-CoV-2 secondary attack rate (SAR) in schools and factors associated with transmission. Design, Setting, and Participants: This cohort study examined the risk of school-based transmission of SARS-CoV-2 among kindergarten through grade 12 students and staff in 10 Massachusetts school districts during 2 periods: fall 2020/spring 2021 (F20/S21) and fall 2021 (F21). School staff collected data on SARS-CoV-2 index cases and school-based contacts, and SAR was defined as the proportion of contacts acquiring SARS-CoV-2 infection. Exposure: SARS-CoV-2. Main Outcomes and Measures: Potential factors associated with transmission, including grade level, masking, exposure location, vaccination history, and Social Vulnerability Index (SVI), were analyzed using univariable and multivariable logistic regression models. Results: For F20/S21, 8 school districts (70 schools, >33 000 students) were included and reported 435 index cases (151 staff, 216 students, and 68 missing role) with 1771 school-based contacts (278 staff, 1492 students, and 1 missing role). For F21, 5 districts (34 schools, >18 000 students) participated and reported 309 index cases (37 staff, 207 students, and 65 missing role) with 1673 school-based contacts (107 staff and 1566 students). The F20/S21 SAR was 2.2% (lower bound, 1.6%; upper bound, 26.7%), and the F21 SAR was 2.8% (lower bound, 2.6%; upper bound, 7.4%). In multivariable analysis, during F20/S21, masking was associated with a lower odds of transmission compared with not masking (odds radio [OR], 0.12; 95% CI, 0.04-0.40; P < .001). In F21, classroom exposure vs out-of-classroom exposure was associated with increased odds of transmission (OR, 2.47; 95% CI, 1.07-5.66; P = .02); a fully vaccinated vs unvaccinated contact was associated with a lower odds of transmission (OR, 0.04; 95% CI, 0.00-0.62; P < .001). In both periods, a higher SVI was associated with a greater odds of transmission. Conclusions and Relevance: In this study of Massachusetts schools, the SAR for SARS-CoV-2 among school-based contacts was low during 2 periods, and factors associated with transmission risk varied over time. These findings suggest that ongoing surveillance efforts may be essential to ensure that both targeted resources and mitigation practices remain optimal and relevant for disease prevention.

Projected COVID-19 Mortality Reduction From Paxlovid Rollout.

This decision analytical model study assesses projections of simulated effects of Paxlovid rollout on hospitalizations and mortality using 10 models.

Adaptive metrics for an evolving pandemic A dynamic approach to area-level COVID-19 risk designations.

Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and often lack transparency in terms of prioritization of false positive versus false negative signals. They have also struggled to maintain relevance over time due to slow and infrequent updates addressing new variants and shifts in vaccine- and infection-induced immunity. We make two contributions to address these weaknesses of risk metrics. We first present a framework to evaluate predictive accuracy based on policy targets related to severe disease and mortality, allowing for explicit preferences toward false negative versus false positive signals. This approach allows policymakers to optimize metrics for specific preferences and interventions. Second, we propose a novel method to update risk thresholds in real-time. We show that this adaptive approach to designating areas as "high risk" improves performance over static metrics in predicting 3-week-ahead mortality and intensive care usage at both state and county levels. We also demonstrate that with our approach, using only new hospital admissions to predict 3-week-ahead mortality and intensive care usage has performed consistently as well as metrics that also include cases and inpatient bed usage. Our results highlight that a key challenge for COVID-19 risk prediction is the changing relationship between indicators and outcomes of policy interest. Adaptive metrics therefore have a unique advantage in a rapidly evolving pandemic context. Significance Statement: In the rapidly-evolving COVID-19 pandemic, public health risk metrics often become less relevant over time. Risk metrics are designed to predict future severe disease and mortality based on currently-available surveillance data, such as cases and hospitalizations. However, the relationship between cases, hospitalizations, and mortality has varied considerably over the course of the pandemic, in the context of new variants and shifts in vaccine- and infection-induced immunity. We propose an adaptive approach that regularly updates metrics based on the relationship between surveillance inputs and future outcomes of policy interest. Our method captures changing pandemic dynamics, requires only hospitalization input data, and outperforms static risk metrics in predicting high-risk states and counties.

Estimated Testing, Tracing, and Vaccination Targets for Containment of the US Mpox Outbreak.

This decision analytic model estimates the levels of community testing, contract tracing, and vaccination required to reduce the effective reproduction number of the mpox virus among the high-risk group of men who have sex with men.

COVID-19 and Excess All-Cause Mortality in the US and 20 Comparison Countries, June 2021-March 2022.

This study compares the COVID-19 per capita overall and excess mortality rates in the US vs rates for 20 Organization for Economic Co-operation and Development countries and the timing of any increases in excess mortality between June 2021 and December 2021 (Delta) and December 2021 to March 2022 (Omicron).

Estimating deaths averted and cost per life saved by scaling up mRNA COVID-19 vaccination in low-income and lower-middle-income countries in the COVID-19 Omicron variant era: a modelling study.

OBJECTIVES: While almost 60% of the world has received at least one dose of COVID-19 vaccine, the global distribution of vaccination has not been equitable. Only 4% of the population of low-income countries (LICs) has received a full primary vaccine series, compared with over 70% of the population of high-income nations. DESIGN: We used economic and epidemiological models, parameterised with public data on global vaccination and COVID-19 deaths, to estimate the potential benefits of scaling up vaccination programmes in LICs and lower-middle-income countries (LMICs) in 2022 in the context of global spread of the Omicron variant of SARS-CoV2. SETTING: Low-income and lower-middle-income nations. MAIN OUTCOME MEASURES: Outcomes were expressed as number of avertable deaths through vaccination, costs of scale-up and cost per death averted. We conducted sensitivity analyses over a wide range of parameter estimates to account for uncertainty around key inputs. FINDINGS: Globally, universal vaccination in LIC/LMIC with three doses of an mRNA vaccine would result in an estimated 1.5 million COVID-19 deaths averted with a total estimated cost of US$61 billion and an estimated cost-per-COVID-19 death averted of US$40 800 (sensitivity analysis range: US$7400-US$81 500). Lower estimated infection fatality ratios, higher cost-per-dose and lower vaccine effectiveness or uptake lead to higher cost-per-death averted estimates in the analysis. CONCLUSIONS: Scaling up COVID-19 global vaccination would avert millions of COVID-19 deaths and represents a reasonable investment in the context of the value of a statistical life. Given the magnitude of expected mortality facing LIC/LMIC without vaccination, this effort should be an urgent priority.

Evaluating the Performance of Centers for Disease Control and Prevention COVID-19 Community Levels as Leading Indicators of COVID-19 Mortality.

BACKGROUND: Centers for Disease Control and Prevention (CDC) defines low, medium, and high "COVID-19 community levels" to guide interventions, but associated mortality rates have not been reported. OBJECTIVE: To evaluate the diagnostic performance of CDC COVID-19 community level metrics as predictors of elevated community mortality risk. DESIGN: Time series analysis over the period of 30 May 2021 through 4 June 2022. SETTING: U.S. states and counties. PARTICIPANTS: U.S. population. MEASUREMENTS: CDC "COVID-19 community level" metrics based on hospital admissions, bed occupancy, and reported cases; reported COVID-19 deaths; and sensitivity, specificity, and predictive values for CDC and alternative metrics. RESULTS: Mean and median weekly mortality rates per 100 000 population after onset of high COVID-19 community level 3 weeks prior were, respectively, 2.6 and 2.4 (interquartile range [IQR], 1.7 to 3.1) across 90 high episodes in states and 4.3 and 2.1 (IQR, 0 to 5.4) across 7987 high episodes in counties. In 85 of 90 (94%) episodes in states and 4801 of 7987 (60%) episodes in counties, lagged weekly mortality after onset exceeded 0.9 per 100 000 population, and in 57 of 90 (63%) episodes in states and 4018 of 7987 (50%) episodes in counties, lagged weekly mortality after onset exceeded 2.1 per 100 000, which is equivalent to approximately 1000 daily deaths in the national population. Alternative metrics based on lower hospital admissions or case thresholds were associated with lower mortality and had higher sensitivity and negative predictive value for elevated mortality, but the CDC metrics had higher specificity and positive predictive value. Ratios between cases, hospitalizations, and deaths have varied substantially over time. LIMITATIONS: Aggregate mortality does not account for nonfatal outcomes or disparities. Continuing evolution of viral variants, immunity, clinical interventions, and public health mitigation strategies complicate prediction for future waves. CONCLUSION: Designing metrics for public health decision making involves tradeoffs between identifying early signals for action and avoiding undue restrictions when risks are modest. Explicit frameworks for evaluating surveillance metrics can improve transparency and decision support. PRIMARY FUNDING SOURCE: Council of State and Territorial Epidemiologists.

Estimated Transmission Outcomes and Costs of SARS-CoV-2 Diagnostic Testing, Screening, and Surveillance Strategies Among a Simulated Population of Primary School Students.

Importance: In addition to illness, the COVID-19 pandemic has led to historic educational disruptions. In March 2021, the federal government allocated $10 billion for COVID-19 testing in US schools. Objective: Costs and benefits of COVID-19 testing strategies were evaluated in the context of full-time, in-person kindergarten through eighth grade (K-8) education at different community incidence levels. Design, Setting, and Participants: An updated version of a previously published agent-based network model was used to simulate transmission in elementary and middle school communities in the United States. Assuming dominance of the delta SARS-CoV-2 variant, the model simulated an elementary school (638 students in grades K-5, 60 staff) and middle school (460 students grades 6-8, 51 staff). Exposures: Multiple strategies for testing students and faculty/staff, including expanded diagnostic testing (test to stay) designed to avoid symptom-based isolation and contact quarantine, screening (routinely testing asymptomatic individuals to identify infections and contain transmission), and surveillance (testing a random sample of students to identify undetected transmission and trigger additional investigation or interventions). Main Outcomes and Measures: Projections included 30-day cumulative incidence of SARS-CoV-2 infection, proportion of cases detected, proportion of planned and unplanned days out of school, cost of testing programs, and childcare costs associated with different strategies. For screening policies, the cost per SARS-CoV-2 infection averted in students and staff was estimated, and for surveillance, the probability of correctly or falsely triggering an outbreak response was estimated at different incidence and attack rates. Results: Compared with quarantine policies, test-to-stay policies are associated with similar model-projected transmission, with a mean of less than 0.25 student days per month of quarantine or isolation. Weekly universal screening is associated with approximately 50% less in-school transmission at one-seventh to one-half the societal cost of hybrid or remote schooling. The cost per infection averted in students and staff by weekly screening is lowest for schools with less vaccination, fewer other mitigation measures, and higher levels of community transmission. In settings where local student incidence is unknown or rapidly changing, surveillance testing may detect moderate to large in-school outbreaks with fewer resources compared with schoolwide screening. Conclusions and Relevance: In this modeling study of a simulated population of primary school students and simulated transmission of COVID-19, test-to-stay policies and/or screening tests facilitated consistent in-person school attendance with low transmission risk across a range of community incidence. Surveillance was a useful reduced-cost option for detecting outbreaks and identifying school environments that would benefit from increased mitigation.

Affordability and Value in Decision Rules for Cost-Effectiveness: A Survey of Health Economists.

OBJECTIVES: New health technologies are often expensive, but may nevertheless meet standard thresholds for cost effectiveness, a situation exemplified by recent hepatitis C cures. Currently, cost-effectiveness analysis (CEA) does not supply practical means of weighing trade-offs between cost-effectiveness and affordability, particularly when costs and benefits are temporally separated and in health systems with multiple payers, such as the United States. We formally characterized disagreements in CEA theory and identified how these trade-offs are presently addressed in practice. METHODS: We surveyed 170 health economics researchers. RESULTS: When presented with a hypothetical cost-effective drug therapy in the United States that would require 20% of a state's Medicaid budget over 5 years, 34% of survey respondents recommended that policy makers fund the drug for all patients and 26% for a subset. By contrast, 26% recommended against funding the drug. We found additional disagreement regarding whether the willingness-to-pay threshold should be based on the budget (42%) or societal preferences (41%) and identified 4 approaches to weighing cost-effectiveness and affordability. A total of 61% of respondents did not believe that the threshold used in their last article (most often 1×-3× per capita gross domestic product) represented either the budget or societal willingness-to-pay threshold. CONCLUSIONS: We use these findings to recommend metrics that can inform translation of CEA theory into practice. By contextualizing cost and value, researchers can provide more actionable policy recommendations.

Model-Estimated Association Between Simulated US Elementary School-Related SARS-CoV-2 Transmission, Mitigation Interventions, and Vaccine Coverage Across Local Incidence Levels.

Importance: With recent surges in COVID-19 incidence and vaccine authorization for children aged 5 to 11 years, elementary schools face decisions about requirements for masking and other mitigation measures. These decisions require explicit determination of community objectives (eg, acceptable risk level for in-school SARS-CoV-2 transmission) and quantitative estimates of the consequences of changing mitigation measures. Objective: To estimate the association between adding or removing in-school mitigation measures (eg, masks) and COVID-19 outcomes within an elementary school community at varying student vaccination and local incidence rates. Design, Setting, and Participants: This decision analytic model used an agent-based model to simulate SARS-CoV-2 transmission within a school community, with a simulated population of students, teachers and staff, and their household members (ie, immediate school community). Transmission was evaluated for a range of observed local COVID-19 incidence (0-50 cases per 100 000 residents per day, assuming 33% of all infections detected). The population used in the model reflected the mean size of a US elementary school, including 638 students and 60 educators and staff members in 6 grades with 5 classes per grade. Exposures: Variant infectiousness (representing wild-type virus, Alpha variant, and Delta variant), mitigation effectiveness (0%-100% reduction in the in-school secondary attack rate, representing increasingly intensive combinations of mitigations including masking and ventilation), and student vaccination levels were varied. Main Outcomes and Measures: The main outcomes were (1) probability of at least 1 in-school transmission per month and (2) mean increase in total infections per month among the immediate school community associated with a reduction in mitigation; multiple decision thresholds were estimated for objectives associated with each outcome. Sensitivity analyses on adult vaccination uptake, vaccination effectiveness, and testing approaches (for selected scenarios) were conducted. Results: With student vaccination coverage of 70% or less and moderate assumptions about mitigation effectiveness (eg, masking), mitigation could only be reduced when local case incidence was 14 or fewer cases per 100 000 residents per day to keep the mean additional cases associated with reducing mitigation to 5 or fewer cases per month. To keep the probability of any in-school transmission to less than 50% per month, the local case incidence would have to be 4 or fewer cases per 100 000 residents per day. Conclusions and Relevance: In this study, in-school mitigation measures (eg, masks) and student vaccinations were associated with substantial reductions in transmissions and infections, but the level of reduction varied across local incidence. These findings underscore the potential role for responsive plans that deploy mitigation strategies based on local COVID-19 incidence, vaccine uptake, and explicit consideration of community objectives.

Problems with evidence assessment in COVID-19 health policy impact evaluation: a systematic review of study design and evidence strength.

INTRODUCTION: Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. METHODS: We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on 26 November 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation. RESULTS: After 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-sectional), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes. DISCUSSION: The reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigour to be actionable by policy-makers. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible.

The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination.

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey-over 20 million responses in its first year of operation-allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

Model-estimated relationship between elementary school-related SARS-CoV-2 transmission, mitigation interventions, and vaccination coverage across community incidence levels.

BACKGROUND: While CDC guidance for K-12 schools recommends indoor masking regardless of vaccination status, final decisions about masking in schools will be made at the local and state level. The impact of the removal of mask restrictions, however, on COVID-19 outcomes for elementary students, educators/staff, and their households is not well known. METHODS: We used a previously published agent-based dynamic transmission model of SARS-CoV-2 in K-12 schools to simulate an elementary school with 638 students across 12 scenarios: combinations of three viral infectiousness levels (reflecting wild-type virus, alpha variant, and delta variant) and four student vaccination levels (0%, 25%, 50% and 70% coverage). For each scenario, we varied observed community COVID-19 incidence (0 to 50 cases/100,000 people/day) and mitigation effectiveness (0-100% reduction to in-school secondary attack rate), and evaluated two outcomes over a 30 day period: (1) the probability of at least one in-school transmission, and (2) average increase in total infections among students, educators/staff, and their household members associated with moving from more to less intensive mitigation measures. RESULTS: Over 30 days in the simulated elementary school, the probability of at least one in-school SARS-CoV-2 transmission and the number of estimated additional infections in the immediate school community associated with changes in mitigation measures varied widely. In one scenario with the delta variant and no student vaccination, assuming that baseline mitigation measures of simple ventilation and handwashing reduce the secondary attack rate by 40%, if decision-makers seek to keep the monthly probability of an in-school transmission below 50%, additional mitigation (e.g., masking) would need to be added at a community incidence of approximately 2/100,000/day. Once students are vaccinated, thresholds shift substantially higher. LIMITATIONS: The interpretation of model results should be limited by the uncertainty in many of the parameters, including the effectiveness of individual mitigation interventions and vaccine efficacy against the delta variant, and the limited scope of the model beyond the school community. Additionally, the assumed case detection rate (33% of cases detected) may be too high in areas with decreased testing capacity. CONCLUSION: Despite the assumption of high adult vaccination, the risks of both in-school SARS-CoV-2 transmission and resulting infections among students, educators/staff, and their household members remain high when the delta variant predominates and students are unvaccinated. Mitigation measures or vaccinations for students can substantially reduce these risks. These findings underscore the potential role for responsive plans, where mitigation is deployed based on local COVID-19 incidence and vaccine uptake.

SARS-CoV-2 testing strategies to contain school-associated transmission: model-based analysis of impact and cost of diagnostic testing, screening, and surveillance.

Background: In March 2021, the Biden administration allocated $10 billion for COVID-19 testing in schools. We evaluate the costs and benefits of testing strategies to reduce the infection risks of full-time in-person K-8 education at different levels of community incidence. Methods: We used an agent-based network model to simulate transmission in elementary and middle school communities, parameterized to a US school structure and assuming dominance of the delta COVID-19 variant. We assess the value of different strategies for testing students and faculty/staff, including expanded diagnostic testing ("test to stay" policies that take the place of isolation for symptomatic students or quarantine for exposed classrooms); screening (routinely testing asymptomatic individuals to identify infections and contain transmission); and surveillance (testing a random sample of students to signaling undetected transmission and trigger additional investigation or interventions). Main outcome measures: We project 30-day cumulative incidence of SARS-CoV-2 infection; proportion of cases detected; proportion of planned and unplanned days out of school; and the cost of testing programs and of childcare costs associated with different strategies. For screening policies, we further estimate cost per SARS-CoV-2 infection averted in students and staff, and for surveillance, probability of correctly or falsely triggering an outbreak response at different incidence and attack rates. Results: Accounting for programmatic and childcare costs, "test to stay" policies achieve similar model-projected transmission to quarantine policies, with reduced overall costs. Weekly universal screening prevents approximately 50% of in-school transmission, with a lower projected societal cost than hybrid or remote schooling. The cost per infection averted in students and staff by weekly screening is lower for older students and schools with higher mitigation and declines as community transmission rises. In settings where local student incidence is unknown or rapidly changing, surveillance may trigger detection of moderate-to-large in-school outbreaks with fewer resources compared to screening. Conclusions: "Test to stay" policies and/or screening tests can facilitate consistent in-person school attendance with low transmission risk across a range of community incidence. Surveillance may be a useful reduced-cost option for detecting outbreaks and identifying school environments that may benefit from increased mitigation.

Passing the Test: A Model-Based Analysis of Safe School-Reopening Strategies.

BACKGROUND: The COVID-19 pandemic has induced historic educational disruptions. In April 2021, about 40% of U.S. public school students were not offered full-time in-person education. OBJECTIVE: To assess the risk for SARS-CoV-2 transmission in schools. DESIGN: An agent-based network model was developed to simulate transmission in elementary and high school communities, including home, school, and interhousehold interactions. SETTING: School structure was parametrized to reflect average U.S. classrooms, with elementary schools of 638 students and high schools of 1451 students. Daily local incidence was varied from 1 to 100 cases per 100 000 persons. PARTICIPANTS: Students, faculty, staff, and adult household members. INTERVENTION: Isolation of symptomatic individuals, quarantine of an infected individual's contacts, reduced class sizes, alternative schedules, staff vaccination, and weekly asymptomatic screening. MEASUREMENTS: Transmission was projected among students, staff, and families after a single infection in school and over an 8-week quarter, contingent on local incidence. RESULTS: School transmission varies according to student age and local incidence and is substantially reduced with mitigation measures. Nevertheless, when transmission occurs, it may be difficult to detect without regular testing because of the subclinical nature of most children's infections. Teacher vaccination can reduce transmission to staff, and asymptomatic screening improves understanding of local circumstances and reduces transmission. LIMITATION: Uncertainty exists about the susceptibility and infectiousness of children, and precision is low regarding the effectiveness of specific countermeasures, particularly with new variants. CONCLUSION: With controlled community transmission and moderate mitigation, elementary schools can open safety, but high schools require more intensive mitigation. Asymptomatic screening can facilitate reopening at higher local incidence while minimizing transmission risk. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention through the Council of State and Territorial Epidemiologists, National Institute of Allergy and Infectious Diseases, National Institute on Drug Abuse, and Facebook.