Bayesian epidemic models for spatially aggregated count data.Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time-varying factors. 
Bayesian epidemic models
This paper addresses these issues via suitable Bayesian modelling. 
Bayesian modelling
In doing so, we utilize a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. 
stochastic regression models
The developed regression framework does incorporate serial correlation and time-varying covariates through an Ornstein-Uhlenbeck process formulation. 
Ornstein-Uhlenbeck process formulation
In addition, we explore the effect of different priors, including default options and variations of mixtures of g-priors. 
g-priors
The effect of different distance kernels for the epidemic model component is investigated. 
distance kernels