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Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-21266871


Comprehensive data on transmission mitigation behaviors and both SARS-CoV-2 infection and serostatus are needed from large, community-based cohorts to identify COVID-19 risk factors and the impact of public health measures. From July 2020-March 2021, approximately 5,500 adults from the East Bay Area, California were followed over three data collection rounds to investigate the association between geographic and demographic characteristics and transmission mitigation behavior with SARS-CoV-2 prevalence. We estimated the populated-adjusted prevalence of antibodies from SARS-CoV-2 infection and COVID-19 vaccination, and self-reported COVID-19 test positivity. Population-adjusted SARS-CoV-2 seroprevalence was low, increasing from 1.03% (95% CI: 0.50-1.96) in Round 1 (July-September 2020), to 1.37% (95% CI: 0.75-2.39) in Round 2 (October-December 2020), to 2.18% (95% CI: 1.48-3.17) in Round 3 (February-March 2021). Population-adjusted seroprevalence of COVID-19 vaccination was 21.64% (95% CI: 19.20-24.34) in Round 3, with Whites having 4.35% (95% CI: 0.35-8.32) higher COVID-19 vaccine seroprevalence than non-Whites. No evidence for an association between transmission mitigation behavior and seroprevalence was observed. Despite >99% of participants reporting wearing masks, non-Whites, lower-income, and lower-educated individuals had the highest SARS-CoV-2 seroprevalence and lowest vaccination seroprevalence. Results demonstrate that more effective policies are needed to address these disparities and inequities.

Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20169797


BackgroundLarge-scale school closures have been implemented worldwide to curb the spread of COVID-19. However, the impact of school closures and re-opening on epidemic dynamics remains unclear. MethodsWe simulated COVID-19 transmission dynamics using an individual-based stochastic model, incorporating social-contact data of school-aged children during shelter-in-place orders derived from Bay Area (California) household surveys. We simulated transmission under observed conditions and counterfactual intervention scenarios between March 17-June 1, and evaluated various fall 2020 K-12 reopening strategies. FindingsBetween March 17-June 1, assuming children <10 were half as susceptible to infection as older children and adults, we estimated school closures averted a similar number of infections (13,842 cases; 95% CI: 6,290, 23,040) as workplace closures (15,813; 95% CI: 9,963, 22,617) and social distancing measures (7,030; 95% CI: 3,118, 11,676). School closure effects were driven by high school and middle school closures. Under assumptions of moderate community transmission, we estimate that fall 2020 school reopenings will increase symptomatic illness among high school teachers (an additional 40.7% expected to experience symptomatic infection, 95% CI: 1.9, 61.1), middle school teachers (37.2%, 95% CI: 4.6, 58.1), and elementary school teachers (4.1%, 95% CI: -1.7, 12.0). Results are highly dependent on uncertain parameters, notably the relative susceptibility and infectiousness of children, and extent of community transmission amid re-opening. The school-based interventions needed to reduce the risk to fewer than an additional 1% of teachers infected varies by grade level. A hybrid-learning approach with halved class sizes of 10 students may be needed in high schools, while maintaining small cohorts of 20 students may be needed for elementary schools. InterpretationMultiple in-school intervention strategies and community transmission reductions, beyond the extent achieved to date, will be necessary to avoid undue excess risk associated with school reopening. Policymakers must urgently enact policies that curb community transmission and implement within-school control measures to simultaneously address the tandem health crises posed by COVID-19 and adverse child health and development consequences of long-term school closures. FundingJVR, JRH, QC, PAC, SP, AKH, CMH, and KC were supported in part by National Science Foundation grant no. 2032210, National Institutes of Health grants nos. R01AI125842, R01TW010286 and R01AI148336, and by the University of California Multicampus Research Programs and Initiatives award # 17-446315. JAL received support from the Berkeley Population Center (grant number P2CHD073964 from the National Institute of Child Health & Human Development, National Institutes of Health). Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSGiven the urgent need to enact quick public health interventions to curb transmission of SARS-CoV-2, large-scale school closures were implemented globally. We searched the terms "school", "children", "closure", "coronavirus", and "COVID-19" in PubMed to assess the current evidence evaluating the role of school closures in mitigating SARS-CoV-2 transmission. Data motivating the decision to close schools remained largely limited to experiences with influenza outbreaks, where children are highly susceptible to infection, are key drivers of transmission, and experience severe outcomes. At the time of writing, no modeling studies to our knowledge have quantified the net impact of COVID-19 related school closures in the United States, and observational studies that documented decreases in COVID-19 incidence associated with statewide school closures are subject to confounding by other concurrently implemented non-pharmaceutical interventions. Further, the scientific consensus remains fragmented in its understanding of key epidemiological parameters, namely the relative susceptibility and infectiousness of children compared to adults, exacerbating uncertainties around the risks of opening schools. As policymakers weigh the negative consequences of school closures on child health and development against the risks of reopening, it becomes critical to discern the range of potential impacts of school reopenings on the COVID-19 epidemic accounting for uncertainty in epidemiological parameters and plausible strategies for risk mitigation. Added value of this studyThis study uses an individual-based transmission model parameterized with contact patterns we derived from a web-based contact survey administered to Bay Area (California) households with children during school closures to advance the understanding of the relative impact of Bay Area spring 2020 school closures compared to other non-pharmaceutical interventions, and projects the potential impact of school reopening strategies in the fall 2020 semester. Within the context of our model, we found that school closures averted a similar number of cases as workplace closures in spring 2020, with most of the averted cases attributable to high school closures. We found that COVID-19 risks associated with reopening schools in fall 2020 are highly dependent on the relative susceptibility of children and the level of community transmission at the time of reopening. Strategies necessary to reduce school transmission such that fewer than an additional 1% of teachers would be infected varied across school divisions. Safely reopening high schools may require combining multiple strict contact reduction measures, including staggering school days, halving class sizes, or maintaining small, stable cohorts, while safely reopening elementary schools may be achieved with a more limited set of interventions, including use of stable cohorts and masks. Implications of all the available evidenceUnder plausible assumptions regarding the susceptibility and infectiousness of school-aged children and teenagers, this study highlights heterogeneity of COVID-19 risks, and necessary mitigation strategies, associated with reopening across levels of schooling. It also highlights the urgency of resolving uncertain parameters, especially those pertaining to the relative susceptibility and infectiousness of children. Research is needed to quantify the role of children in transmission of COVID-19 in schools or similar settings to enumerate the risk of school-based outbreaks, particularly as transmission remains high in many regions of the United States. To balance both the adverse long-term consequence of school closures on child development and concerns about safe reopening, policy makers must quickly devote resources to ensure schools that choose to reopen amid uncertain evidence can adopt and adhere to strict infection, prevention, and control strategies that are critical to ensuring students, teachers, and community members remain healthy.

Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20091744


Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Current confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. We used a semi-Bayesian method to perform a probabilistic bias analysis on cumulative confirmed COVID-19 counts, incorporating relevant prior knowledge from existing studies about SARS-CoV-2 testing probabilities and diagnostic accuracy parameters while accounting for uncertainty. We estimate 6,275,072 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) as of April 18, 2020. Accounting for uncertainty, the number of infections was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64-99%) of this difference was due to incomplete testing, while 14% (0.3-36%) was due to imperfect test accuracy. Estimates of SARS-CoV-2 infections that transparently account for testing practices and diagnostic accuracy reveal that the pandemic is larger than confirmed case counts suggest.

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