B0628
Title: Zero-state coupled Markov switching count models for spatio-temporal infectious disease counts
Authors: Alexandra Schmidt - McGill University (Canada) [presenting]
Abstract: Spatio-temporal counts of infectious disease cases often contain an excess of zeros. Existing zero-inflated count models applied to such data are difficult to epidemiologically interpret in terms of how the disease spreads and do not allow for separate dynamics to affect the reemergence and persistence of the disease. As an alternative, we develop a new zero-state coupled Markov switching negative binomial model, under which the disease switches between periods of presence and absence in each area through a series of partially hidden nonhomogeneous, including random effects, Markov chains coupled between neighboring locations. When the disease is present, an autoregressive negative binomial model generates the cases with a possible 0 representing the disease being undetected. Bayesian inference and prediction are illustrated using spatio-temporal courts of dengue fever cases in Rio de Janeiro, Brazil.
