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Epidemic data often possess certain characteristics, such as the presence ofmany zeros, the spatial nature of the disease spread mechanism or environmentalnoise. This paper addresses these issues via suitable Bayesian modelling. Indoing so we utilise stochastic regression models appropriate forspatio-temporal count data with an excess number of zeros. The developedregression framework can incorporate serial correlation and time varyingcovariates through an Ornstein Uhlenbeck process formulation. In addition, weexplore the effect of different priors, including default options andtechniques based upon variations of mixtures of $g$-priors. The effect ofdifferent distance kernels for the epidemic model component is investigated. Weproceed by developing branching process-based methods for testing scenarios fordisease control, thus linking traditional spatio-temporal models with epidemicprocesses, useful in policy-focused decision making. The approach isillustrated with an application to a sheep pox dataset from the Evros region,Greece.

Bayesian spatio-temporal epidemic models with applications to sheep pox