Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Oﬀs
Abstract: We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely on a Markov chain Monte Carlo to sample from the posterior distribution. We show how to use the posterior simulation outputs as inputs for exercises in causality assessment. We apply our approach to Belgian data for the COVID-19 epidemic during 2020. Our estimated time-varying-parameters SIRD model captures the data dynamics very well, including the three waves of infections. We use the estimated (true) number of new cases and the time-varying eﬀective reproduction number from the epidemiological model as information for structural vector autoregressions and local projections. We document how additional government-mandated mobility curtailments would have reduced deaths at zero cost or a very small cost in terms of output.
File(s): File format is text/html https://www.philadelphiafed.org/-/media/frbp/assets/working-papers/2021/wp21-18.pdf
Provider: Federal Reserve Bank of Philadelphia
Part of Series: Working Papers
Publication Date: 2021-05-06