How Timing of Stay-home Orders and Mobility Reductions Impacted First-Wave COVID-19 Deaths in US Counties (preprint)

ABSTRACT

SARS-CoV-2 transmission continues to evolve in the United States following the large second wave in the Summer. Understanding how location-specific variations in non-pharmaceutical epidemic control policies and behaviors contributed to disease transmission will be key for designing effective strategies to avoid future resurgences. We offer a statistical analysis of the relative effectiveness of the timing of both official stay-at-home orders and population mobility reductions, offering a distinct (but complementary) dimension of evidence gleaned from more traditional mechanistic models of epidemic dynamics. Specifically, we use a Bayesian hierarchical model fit to county-level mortality data from the first wave of the pandemic from Jan 21 2020 through May 10 2020 to establish how timing of stay-at-home orders and population mobility changes impacted county-specific epidemic growth. We find that population mobility reductions generally preceded stay-at-home orders, and among 356 counties with a pronounced early local epidemic between January 21 and May 10 (representing 195 million people and 32,000 observed deaths), a 10 day delay in population mobility reduction would have added 16,149 (95% credible interval [CI] 9,517 24,381) deaths by Apr 20, whereas shifting mobility reductions 10 days earlier would have saved 13,571 (95% CI 8,449 16,930) lives. Analogous estimates attributable to the timing of explicit stay-at-home policies were less pronounced, suggesting that mobility changes were the clearer drivers of epidemic dynamics. Our results also suggest that the timing of mobility reductions and policies most impacted epidemic dynamics in larger, urban counties compared with smaller, rural ones. Overall, our results suggest that community behavioral changes had greater impact on curve flattening during the Spring wave compared with stay at home orders. Thus, community engagement and buy-in with precautionary policies may be more important for predicting transmission risk than explicit policies.