The analysis of the geographical distribution of disease on the scale of geographic areas such as administrative boundaries plays an important role in veterinary epidemiology. Prevalence estimates of wildlife population surveys are often based on regional count data generated by sampling animals shot by hunters. The observed disease rate per spatial unit is not a useful estimate of the underlying disease prevalence due to different sample sizes and spatial dependencies between neighbouring areas. Therefore, it is necessary to account for extra-sample variation and and spatial correlation in the data to produce more accurate maps of disease incidence. For this purpose a hierarchical Bayesian model in which structured and un-structured overdispersion is modelled explicitly in terms of spatial and non-spatial components was implemented by Markov Chain Monte Carlo methods. The model was empirically compared with the results of the non-spatial beta-binomial model using surveillance data of Pseudorabies virus infections of wildboars in the Federal State of Brandenburg, Germany
