A Bayesian model for spatial wildlife disease prevalence data.

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 an 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 spatial correlations in the data to produce more accurate maps of disease incidence. The detection of spatial patterns is complicated by missing data in many of the geographical areas as the complete coverage of all areas is nearly impossible in wildlife surveys. For this purpose a hierarchical Bayesian model in which structured and unstructured over dispersion 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 a non-spatial beta-binomial model using surveillance data of pseudorabies virus infections of European wild boars (Sus scrofa scrofa L.) in the Federal State of Brandenburg, Germany.

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