Center for Pandemic Decision Science

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Browsing Center for Pandemic Decision Science by Subject "Bayesian hierarchical model"

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    How Timing of Stay-at-home Orders and Mobility Reductions Impacted First-Wave COVID-19 Deaths in US Counties
    (2021-09) Audirac, Michelle; Tec, Mauricio; Meyers, Lauren Ancel; Fox, Spencer J.; Zigler, Cory
    As SARS-CoV-2 transmission continues to evolve, understanding how location-specific variations in non-pharmaceutical interventions and behaviors contributed to disease transmission during the initial epidemic wave will be key for future control strategies. We offer a rigorous statistical analysis of the relative effectiveness of the timing of both official stay-at-home orders and population mobility reductions during the initial stage of the US epidemic. We use a Bayesian hierarchical regression to fit county-level mortality data from the first case on Jan 21 2020 through Apr 20 2020 and quantify associations between the timing of stay-at-home orders and population mobility with epidemic control. We find that among 882 counties with an early local epidemic, a 10-day delay in the enactment of stay-at-home orders would have been associated with 14,700 additional deaths by Apr 20 (95% credible interval: 9,100, 21,500), whereas shifting orders 10 days earlier would have been associated with nearly 15,700 fewer lives lost (95% credible interval: 11,350, 18,950). Analogous estimates are available for reductions in mobility which typically occurred before stay-at-home orders and are also stratified by county urbanicity, showing significant heterogeneity. Results underscore the importance of timely policy and behavioral action for early-stage epidemic control.