Bayesian mechanistic model of COVID-19 transmission dynamics including the effect of vaccination

Abstract

COVID-19 (coronavirus disease 2019 ) is a pandemic disease caused by a new type of coronavirus called SARS-CoV-2, which has caused unprecedented medical, economical and social burden worldwide for the last two years. A variety of models to describe the transmission dynamics of the virus and the impact of non-pharmacological measures have been reported. Among them, Bayesian mechanistic models using MCMC optimization have shown good description of the transmission dynamics and have potential for accurate predictions of future evolution of the pandemics. In our group, we have been working on optimizing a previously reported COVID-19 transmission model, which has been extended here for the analysis of multiple periods of different transmission rates, enabling the inclusion of an arbitrary number of non-pharmacological measures. Additionally, the model has been extended to include the effect of vaccination and the impact of the different virus variants on the transmission dynamics.