Bayesian Estimation of the Impacts of Widespread Social Distancing and Other Interventions for COVID-19

• Activity Number: 424 - Inference in Infectious Diseases
• Type: Invited
• Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
• Sponsor: IMS
• Author(s): Carolin Colijn*
• Companies: Simon Fraser University

Abstract

Extensive physical distancing measures have been the main effective intervention against coronavirus disease 2019 (COVID-19) worldwide. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing measures, with the timing of these measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to cases in a number of jurisdictions. We account for underestimation and the delay between symptom onset and reporting. We estimate the impact that physical distancing in March 2020 had on the contact rate. We find that distancing had a strong impact, consistent with declines in reported cases and in hospitalization and intensive care unit numbers. We estimate that approximately 0.78 (0.66-0.89 90% CI) of contacts were removed for individuals practising distancing. We introduce the notion of "leeway": how far below the threshold where cases begin to grow is a jurisdiction that is currently in decline? Because the extent of underestimation is unknown, the data are consistent with a wide range in the prevalence of COVID-19.