Changes in population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States between December 2021 and November 2022.

BACKGROUND: While a substantial fraction of the US population was infected with SARS-CoV-2 during December 2021 - February 2022, the subsequent evolution of population immunity reflects the competing influences of waning protection over time and acquisition or restoration of immunity through additional infections and vaccinations. METHODS: Using a Bayesian evidence synthesis model of reported COVID-19 data (diagnoses, hospitalizations), vaccinations, and waning patterns for vaccine- and infection-acquired immunity, we estimate population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States, by location (national, state, county) and week. RESULTS: By November 9, 2022, 97% (95%-99%) of the US population were estimated to have prior immunological exposure to SARS-CoV-2. Between December 1, 2021 and November 9, 2022, protection against a new Omicron infection rose from 22% (21%-23%) to 63% (51%-75%) nationally, and protection against an Omicron infection leading to severe disease increased from 61% (59%-64%) to 89% (83%-92%). Increasing first booster uptake to 55% in all states (current US coverage: 34%) and second booster uptake to 22% (current US coverage: 11%) would increase protection against infection by 4.5 percentage points (2.4-7.2) and protection against severe disease by 1.1 percentage points (1.0-1.5). CONCLUSIONS: Effective protection against SARS-CoV-2 infection and severe disease in November 2022 was substantially higher than in December 2021. Despite this high level of protection, a more transmissible or immune evading (sub)variant, changes in behavior, or ongoing waning of immunity could lead to a new SARS-CoV-2 wave.

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.

Vaccination and Voting Patterns in the U.S.: Analysis of COVID-19 and Flu Surveys From 2010 to 2022.

INTRODUCTION: The study assessed the relationship between COVID-19 and influenza (flu) vaccination and voting patterns during the pandemic and the time trends between flu vaccination and voting patterns. METHODS: Flu and COVID-19 vaccination coverage were analyzed using National Immunization Surveys for flu (Years 2010-2022) and COVID-19 (National Immunization Surveys Adult COVID-19 Module 2021-2022), Centers for Disease Control and Prevention surveillance of COVID-19 vaccination coverage (2021-2022) and U.S. COVID-19 Trends and Impact Survey (2021-2022). The study described the correlations between state-level COVID-19 and flu vaccination coverage, examined individual-level characteristics of vaccination for COVID-19 and for flu using logistic regression (COVID-19 Trends and Impact Survey May-June 2022), and analyzed flu vaccination coverage by age (National Immunization Surveys for flu 2010-2022) and its relationship with voting patterns. RESULTS: There was a strong correlation between state-level COVID-19 vaccination coverage and voting share for the Democratic candidate in the 2020 presidential elections. COVID-19 vaccination coverage in June 2022 was higher than flu vaccination coverage, and it had a stronger correlation with voting patterns (R=0.90 vs R=0.60 in COVID-19 Trends and Impact Survey). Vaccinated people were more likely to be living in a county where the majority voted for the Democratic candidate in 2020 elections both for COVID-19 (adjusted OR=1.77, 95% CI=1.71, 1.84) and for flu (adjusted OR=1.27, 95% CI=1.23, 1.31). There is a longstanding correlation between voting patterns and flu vaccination coverage, which varies by age, with the strongest correlation in the youngest ages. CONCLUSIONS: There are existing prepandemic patterns between vaccination coverage and voting patterns. The findings align with research that has identified an association between adverse health outcomes and the political environment in the U.S.

Population immunity to pre-Omicron and Omicron SARS-CoV-2 variants in US states and counties through December 1, 2021.

BACKGROUND: Both SARS-CoV-2 infection and COVID-19 vaccination contribute to population-level immunity against SARS-CoV-2. This study estimates the immunological exposure and effective protection against future SARS-CoV-2 infection in each US state and county over 2020-2021, and how this changed with the introduction of the Omicron variant. METHODS: We used a Bayesian model to synthesize estimates of daily SARS-CoV-2 infections, vaccination data and estimates of the relative rates of vaccination conditional on infection status to estimate the fraction of the population with (i) immunological exposure to SARS-CoV-2 (ever infected with SARS-CoV-2 and/or received one or more doses of a COVID-19 vaccine), (ii) effective protection against infection, and (iii) effective protection against severe disease, for each US state and county from January 1, 2020, to December 1, 2021. RESULTS: The estimated percentage of the US population with a history of SARS-CoV-2 infection or vaccination as of December 1, 2021, was 88.2% (95% Credible Interval (CrI): 83.6%-93.5%). Accounting for waning and immune escape, effective protection against the Omicron variant on December 1, 2021, was 21.8% (95%CrI: 20.7%-23.4%) nationally and ranged between 14.4% (95%CrI: 13.2%-15.8%, West Virginia) to 26.4% (95%CrI: 25.3%-27.8%, Colorado). Effective protection against severe disease from Omicron was 61.2% (95%CrI: 59.1%-64.0%) nationally and ranged between 53.0% (95%CrI: 47.3%-60.0%, Vermont) and 65.8% (95%CrI: 64.9%-66.7%, Colorado). CONCLUSIONS: While over four-fifths of the US population had prior immunological exposure to SARS-CoV-2 via vaccination or infection on December 1, 2021, only a fifth of the population was estimated to have effective protection against infection with the immune-evading Omicron variant.

Generating simple classification rules to predict local surges in COVID-19 hospitalizations.

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (all ≥ 80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

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.

Protection against Omicron from Vaccination and Previous Infection in a Prison System.

BACKGROUND: Information regarding the protection conferred by vaccination and previous infection against infection with the B.1.1.529 (omicron) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is limited. METHODS: We evaluated the protection conferred by mRNA vaccines and previous infection against infection with the omicron variant in two high-risk populations: residents and staff in the California state prison system. We used a retrospective cohort design to analyze the risk of infection during the omicron wave using data collected from December 24, 2021, through April 14, 2022. Weighted Cox models were used to compare the effectiveness (measured as 1 minus the hazard ratio) of vaccination and previous infection across combinations of vaccination history (stratified according to the number of mRNA doses received) and infection history (none or infection before or during the period of B.1.617.2 [delta]-variant predominance). A secondary analysis used a rolling matched-cohort design to evaluate the effectiveness of three vaccine doses as compared with two doses. RESULTS: Among 59,794 residents and 16,572 staff, the estimated effectiveness of previous infection against omicron infection among unvaccinated persons who had been infected before or during the period of delta predominance ranged from 16.3% (95% confidence interval [CI], 8.1 to 23.7) to 48.9% (95% CI, 41.6 to 55.3). Depending on previous infection status, the estimated effectiveness of vaccination (relative to being unvaccinated and without previous documented infection) ranged from 18.6% (95% CI, 7.7 to 28.1) to 83.2% (95% CI, 77.7 to 87.4) with two vaccine doses and from 40.9% (95% CI, 31.9 to 48.7) to 87.9% (95% CI, 76.0 to 93.9) with three vaccine doses. Incremental effectiveness estimates of a third (booster) dose (relative to two doses) ranged from 25.0% (95% CI, 16.6 to 32.5) to 57.9% (95% CI, 48.4 to 65.7) among persons who either had not had previous documented infection or had been infected before the period of delta predominance. CONCLUSIONS: Our findings in two high-risk populations suggest that mRNA vaccination and previous infection were effective against omicron infection, with lower estimates among those infected before the period of delta predominance. Three vaccine doses offered significantly more protection than two doses, including among previously infected persons.

Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model.

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.

All-cause versus cause-specific excess deaths for estimating influenza-associated mortality in Denmark, Spain, and the United States.

BACKGROUND: Seasonal influenza-associated excess mortality estimates can be timely and provide useful information on the severity of an epidemic. This methodology can be leveraged during an emergency response or pandemic. METHOD: For Denmark, Spain, and the United States, we estimated age-stratified excess mortality for (i) all-cause, (ii) respiratory and circulatory, (iii) circulatory, (iv) respiratory, and (v) pneumonia, and influenza causes of death for the 2015/2016 and 2016/2017 influenza seasons. We quantified differences between the countries and seasonal excess mortality estimates and the death categories. We used a time-series linear regression model accounting for time and seasonal trends using mortality data from 2010 through 2017. RESULTS: The respective periods of weekly excess mortality for all-cause and cause-specific deaths were similar in their chronological patterns. Seasonal all-cause excess mortality rates for the 2015/2016 and 2016/2017 influenza seasons were 4.7 (3.3-6.1) and 14.3 (13.0-15.6) per 100,000 population, for the United States; 20.3 (15.8-25.0) and 24.0 (19.3-28.7) per 100,000 population for Denmark; and 22.9 (18.9-26.9) and 52.9 (49.1-56.8) per 100,000 population for Spain. Seasonal respiratory and circulatory excess mortality estimates were two to three times lower than the all-cause estimates. DISCUSSION: We observed fewer influenza-associated deaths when we examined cause-specific death categories compared with all-cause deaths and observed the same trends in peaks in deaths with all death causes. Because all-cause deaths are more available, these models can be used to monitor virus activity in near real time. This approach may contribute to the development of timely mortality monitoring systems during public health emergencies.

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.

Uptake of COVID-19 Vaccination Among Frontline Workers in California State Prisons.

Importance: Prisons and jails are high-risk environments for COVID-19. Vaccination levels among workers in many such settings remain markedly lower than those of residents and members of surrounding communities. The situation is troubling because prison staff are a key vector for COVID-19 transmission. Objective: To assess patterns and timing of staff vaccination in California state prisons and identify individual-level and community-level factors associated with remaining unvaccinated. Design Setting and Participants: This cohort study used data from December 22, 2020, through June 30, 2021, to quantify the fractions of staff and incarcerated residents who remained unvaccinated among 23 472 custody and 7617 health care staff who worked in roles requiring direct contact with residents at 33 of the 35 prisons operated by the California Department of Corrections and Rehabilitation. Multivariable probit regressions assessed demographic, community, and peer factors associated with staff vaccination uptake. Main Outcomes and Measures: Remaining unvaccinated throughout the study period. Results: Of 23 472 custody staff, 3751 (16%) were women, and 1454 (6%) were Asian/Pacific Islander individuals, 1571 (7%) Black individuals, 9008 (38%) Hispanic individuals, and 6666 (28%) White individuals. Of 7617 health care staff, 5434 (71%) were women, and 2148 (28%) were Asian/Pacific Islander individuals, 1201 (16%) Black individuals, 1409 (18%) Hispanic individuals, and 1771 (23%) White individuals. A total of 6103 custody staff (26%) and 3961 health care staff (52%) received 1 or more doses of a COVID-19 vaccine during the first 2 months vaccines were offered, but vaccination rates stagnated thereafter. By June 30, 2021, 14 317 custody staff (61%) and 2819 health care staff (37%) remained unvaccinated. In adjusted analyses, remaining unvaccinated was positively associated with younger age (custody staff: age, 18-29 years vs ≥60 years, 75% [95% CI, 73%-76%] vs 45% [95% CI, 42%-48%]; health care staff: 52% [95% CI, 48%-56%] vs 29% [95% CI, 27%-32%]), prior COVID-19 infection (custody staff: 67% [95% CI, 66%-68%] vs 59% [95% CI, 59%-60%]; health care staff: 44% [95% CI, 42%-47%] vs 36% [95% CI, 36%-36%]), residing in a community with relatively low rates of vaccination (custody staff: 75th vs 25th percentile:, 63% [95% CI, 62%-63%] vs 60% [95% CI, 59%-60%]; health care staff: 40% [95% CI, 39%-41%] vs 34% [95% CI, 33%-35%]), and sharing shifts with coworkers who had relatively low rates of vaccination (custody staff: 75th vs 25th percentile, 64% [95% CI, 62%-66%] vs 59% [95% CI, 57%-61%]; health care staff: 38% [95% CI, 36%-41%] vs 35% [95% CI, 31%-39%]). Conclusions and Relevance: This cohort study of California state prison custody and health care staff found that vaccination uptake plateaued at levels that posed ongoing risks of further outbreaks in the prisons and continuing transmission from prisons to surrounding communities. Prison staff decisions to forgo vaccination appear to be multifactorial, and vaccine mandates may be necessary to achieve adequate levels of immunity in this high-risk setting.

Effectiveness of Coronavirus Disease 2019 (COVID-19) Vaccines Among Incarcerated People in California State Prisons: Retrospective Cohort Study.

BACKGROUND: Prisons and jails are high-risk settings for coronavirus disease 2019 (COVID-19). Vaccines may substantially reduce these risks, but evidence is needed on COVID-19 vaccine effectiveness for incarcerated people, who are confined in large, risky congregate settings. METHODS: We conducted a retrospective cohort study to estimate effectiveness of messenger RNA (mRNA) vaccines, BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna), against confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections among incarcerated people in California prisons from 22 December 2020 through 1 March 2021. The California Department of Corrections and Rehabilitation provided daily data for all prison residents including demographic, clinical, and carceral characteristics, as well as COVID-19 testing, vaccination, and outcomes. We estimated vaccine effectiveness using multivariable Cox models with time-varying covariates, adjusted for resident characteristics and infection rates across prisons. RESULTS: Among 60 707 cohort members, 49% received at least 1 BNT162b2 or mRNA-1273 dose during the study period. Estimated vaccine effectiveness was 74% (95% confidence interval [CI], 64%-82%) from day 14 after first dose until receipt of second dose and 97% (95% CI, 88%-99%) from day 14 after second dose. Effectiveness was similar among the subset of residents who were medically vulnerable: 74% (95% CI, 62%-82%) and 92% (95% CI, 74%-98%) from 14 days after first and second doses, respectively. CONCLUSIONS: Consistent with results from randomized trials and observational studies in other populations, mRNA vaccines were highly effective in preventing SARS-CoV-2 infections among incarcerated people. Prioritizing incarcerated people for vaccination, redoubling efforts to boost vaccination, and continuing other ongoing mitigation practices are essential in preventing COVID-19 in this disproportionately affected population.

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.

Adventures in COVID-19 Policy Modeling: Education Edition.

PURPOSE OF REVIEW: To introduce readers to policy modeling, a multidisciplinary field of quantitative analysis, primarily used to help guide decision-making. This review focuses on the choices facing educational administrators, from K-12 to universities in the USA, as they confronted the COVID-19 pandemic. We survey three key model-based approaches to mitigation of SARS-CoV-2 spread in schools and on university campuses. RECENT FINDINGS: Frequent testing, coupled with strict attention to behavioral interventions to prevent further transmission can avoid large outbreaks on college campuses. K-12 administrators can greatly reduce the risks of severe outbreaks of COVID-19 in schools through various mitigation measures including classroom infection control, scheduling and cohorting strategies, staff and teacher vaccination, and asymptomatic screening. Safer re-opening of college and university campuses as well as in-person instruction for K-12 students is possible, under many though not all epidemic scenarios if rigorous disease control and screening programs are in place.

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.

Impact Evaluation of Coronavirus Disease 2019 Policy: A Guide to Common Design Issues.

Policy responses to coronavirus disease 2019 (COVID-19), particularly those related to nonpharmaceutical interventions, are unprecedented in scale and scope. However, evaluations of policy impacts require a complex combination of circumstance, study design, data, statistics, and analysis. Beyond the issues that are faced for any policy, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and a multiplicity of interventions. The methods needed for policy-level impact evaluation are not often used or taught in epidemiology, and they differ in important ways that may not be obvious. Methodological complications of policy evaluations can make it difficult for decision-makers and researchers to synthesize and evaluate the strength of the evidence in COVID-19 health policy papers. Here we 1) introduce the basic suite of policy-impact evaluation designs for observational data, including cross-sectional analyses, pre-/post- analyses, interrupted time-series analysis, and difference-in-differences analysis; 2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19; and 3) provide decision-makers and reviewers with a conceptual and graphical guide to identifying these key violations. Our overall goal is to help epidemiologists, policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence.

Dependence of COVID-19 Policies on End-of-Year Holiday Contacts in Mexico City Metropolitan Area: A Modeling Study.

Background. Mexico City Metropolitan Area (MCMA) has the largest number of COVID-19 (coronavirus disease 2019) cases in Mexico and is at risk of exceeding its hospital capacity in early 2021. Methods. We used the Stanford-CIDE Coronavirus Simulation Model (SC-COSMO), a dynamic transmission model of COVID-19, to evaluate the effect of policies considering increased contacts during the end-of-year holidays, intensification of physical distancing, and school reopening on projected confirmed cases and deaths, hospital demand, and hospital capacity exceedance. Model parameters were derived from primary data, literature, and calibrated. Results. Following high levels of holiday contacts even with no in-person schooling, MCMA will have 0.9 million (95% prediction interval 0.3-1.6) additional COVID-19 cases between December 7, 2020, and March 7, 2021, and hospitalizations will peak at 26,000 (8,300-54,500) on January 25, 2021, with a 97% chance of exceeding COVID-19-specific capacity (9,667 beds). If MCMA were to control holiday contacts, the city could reopen in-person schools, provided they increase physical distancing with 0.5 million (0.2-0.9) additional cases and hospitalizations peaking at 12,000 (3,700-27,000) on January 19, 2021 (60% chance of exceedance). Conclusion. MCMA must increase COVID-19 hospital capacity under all scenarios considered. MCMA's ability to reopen schools in early 2021 depends on sustaining physical distancing and on controlling contacts during the end-of-year holiday.