Identifying patterns of a disease is important to inform policy, programs and interventions at both local and global health authority and government levels. To address the handling of heterogeneous populations in the modeling of temporal spread of an infectious disease, we consider a temporal model for infectious disease which is built around infection behavior how these influence changes to the susceptible population. In this talk, we present a Bayesian framework for modelling infectious diseases based on a discrete-time version of susceptible-infectious-recovered-susceptible (TSIRS) type epidemic model, and fitted to observed disease incidence time series. A key feature of the novel model formulation is that it explicitly takes into account both heterogeneous mixing of individuals in the population and the inherent stochasticity in the transmission of the disease. We utilize this model to analyze the weekly incidence of influenza from 2012 to 2015 in Province of Manitoba, and discuss results from such an analysis.

This is the joint work with Charmaine Dean and Mahmoud Torabi. This work was supported by NSERC and CANSSI-CRT grants.