Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing.

ABSTRACT: Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia (BC), Canada, and five other jurisdictions, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimated the impact that physical distancing (social distancing) has had on the contact rate and examine

SUBMITTER: Anderson SC 

PROVIDER: S-EPMC7738161 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature