Estimating long-term vaccine effectiveness against SARS-CoV-2 variants: a model-based approach (preprint)

With the ongoing evolution of the SARS-CoV-2 virus, variant-adapted vaccines are likely to be required. Given the challenges of conducting clinical trials against a background of widespread infection-induced immunity, updated vaccines are likely to be adopted based on immunogenicity data. We extended a modelling framework linking immunity levels and protection and fitted the model to vaccine effectiveness data from England for three vaccines (Oxford/AstraZeneca AZD1222, Pfizer-BioNTech BNT162b2, Moderna mRNA-1273) and two variants (Delta and Omicron) to predict longer-term effectiveness against mild disease, hospitalisation and death. We use these model fits to predict the effectiveness of the Moderna bivalent vaccine (mRNA1273.214) against the Omicron variant using immunogenicity data. Our results suggest sustained protection against hospitalisation and death from the Omicron variant over the first six months following boosting with the monovalent vaccines but a gradual waning to moderate protection after 1 year (median predicted vaccine effectiveness at 1 year in 65+ age group: AZD1222 38.9%, 95% CrI 31.8%-46.8%; BNT162b2 53.3%, 95% CrI 49.1%-56.9%; mRNA-1273 60.0%, 95% CrI 56.0%-63.6%). Furthermore, we predict almost complete loss of protection against mild disease over this period (mean predicted effectiveness at 1 year 7.8% for AZD1222, 13.2% for BNT162b2 and 16.7% for mRNA-1273). Switching to a second booster with the bivalent mRNA1273.214 vaccine against Omicron BA.1/2 is predicted to prevent nearly twice as many hospitalisations and deaths over a 1-year period compared to administering a second booster with the monovalent mRNA1273 vaccine. Ongoing production and administration of variant-specific vaccines are therefore likely to play an important role in protecting against severe outcomes from the ongoing circulation of SARS-CoV-2.

Impact of individual-level characteristics and transmission mitigation behaviors on SARS-CoV-2 infection and seroprevalence in a large Northern California Bay Area cohort (preprint)

Comprehensive data on transmission mitigation behaviors and SARS-CoV-2 infection and serostatus are needed from large, community-based cohorts to identify SARS-CoV-2 risk factors and impact of public health measures. From July 2020 to March 2021, {approx}5,500 adults from the East Bay Area, California were followed over three data collection rounds. We estimated the 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. 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. Our results demonstrate that more effective policies are needed to address these disparities and inequities.

The effect of school closures and reopening strategies on COVID-19 infection dynamics in the San Francisco Bay Area: a cross-sectional survey and modeling analysis (preprint)

Background Large-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. Methods We 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. Findings Between 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. Interpretation Multiple 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.

Substantial underestimation of SARS-CoV-2 infection in the United States due to incomplete testing and imperfect test accuracy (preprint)

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. Using a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy, we estimated 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.