Estimating the Effect of Social Distancing Interventions on COVID-19 in the United States

Abstract

Since its global emergence in 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused multiple epidemics in the United States. Because medical treatments for the virus are still emerging and a vaccine is not yet available, state and local governments have sought to limit its spread by enacting various social distancing interventions such as school closures and lockdown, but the effectiveness of these interventions is unknown. We applied an established, semi-mechanistic Bayesian hierarchical model of these interventions on SARS-CoV-2 spread in Europe to the United States, using case fatalities from February 29, 2020 up to April 25, 2020, when some states began reversing their interventions. We estimated the effect of interventions across all states, contrasted the estimated reproduction number, R_t, for each state before and after lockdown, and contrasted predicted future fatalities with actual fatalities as a check on the model’s validity. Overall, school closures and lockdown are the only interventions modeled that have a reliable impact on R_t, and lockdown appears to have played a key role in reducing R_t below 1.0. We conclude that reversal of lockdown, without implementation of additional, equally effective interventions, will enable continued, sustained transmission of SARS-CoV-2 in the United States.