A Bayesian Model for Spatial 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 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.