Abstract

Spatio-temporal counts of infectious disease cases often contain an excess of zeros. It is important for decision makers to identify periods of persistence (presence to presence) and reemergence (absence to presence) of a disease. Similarly, when modelling hospital admissions it is of interest to identify epidemic or endemic periods to predict hospital capacity. In this talk I will discuss a class of coupled nonhomogeneous Markov switching models that addresses these issues. Inference and prediction are performed under the Bayesian paradigm. To showcase the ability of the proposed models in addressing the above issues we analyze spatio-temporal counts of dengue fever cases in Rio de Janeiro and COVID-19 hospital admissions in the 30 largest Quebec hospitals. This is joint work with Dirk Douwes-Schultz, PhD student in the Program of Biostatistics, McGill University.