Semi-Mechanistic Bayesian modeling of COVID-19 with Renewal Processes

Abstract

Abstract We propose a general Bayesian approach to modeling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular an analysis concerning the effects of non-pharmaceutical interventions (NPIs) in reducing COVID-19 transmission in 11 European countries (Flaxman et al., 2020b). The model parameterizes the time varying reproduction number Rt through a multilevel regression framework in which covariates can be governmental interventions, changes in mobility patterns, or other behavioural measures. Bayesian multilevel modelling allows a joint fit across regions, with partial pooling to share strength. This innovation was critical to our timely estimates of the impact of lockdown and other NPIs in the European epidemics: estimates from countries at later stages in their epidemics informed those of countries at earlier stages. Originally released as Imperial College Report 13 Flaxman et al. (2020a) on 30 March 2020, the validity of this approach was borne out by the subsequent course of the epidemic. Our framework provides a fully generative model for latent infections and derived observations, including deaths, cases, hospitalizations, ICU admissions and seroprevalence surveys. One issue surrounding our model’s use during the COVID-19 pandemic is the confounded nature of NPIs and mobility. We explore this issue using our R package epidemia which implements the approach in Stan. Versions of our model were used in an ongoing way by New York State, Tennessee and Scotland to estimate the current epidemic situation and make policy decisions.