Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19.

ABSTRACT: Epidemiological forecasts are beset by uncertainties about the underlying epidemiological processes, and the surveillance process through which data are acquired. We present a Bayesian inference methodology that quantifies these uncertainties, for epidemics that are modelled by (possibly) non-stationary, continuous-time, Markov population processes. The efficiency of the method derives from a functional central limit theorem approximation of the likelihood, valid for large populations. We demonstrate the methodology by analysing the early stages of the COVID-19 pandemic in the UK, based on age-structured data for the number of deaths. This includes maximum a posteriori estimates, Markov chain Monte Carlo sampling of the posterior, computation of the model evidence, and the determina

SUBMITTER: Li YI 

PROVIDER: S-EPMC8355677 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature