Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19
Epidemiological forecasts are beset by uncertainties about the underlying epidemiological processes, and icon track bar f250 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 p