An approach to model monitoring and surveillance data of wildlife diseases-exemplified by Classical Swine Fever in wild boar.

The analysis of epidemiological field data from monitoring and surveillance systems (MOSSs) in wild animals is of great importance in order to evaluate the performance of such systems. By parameter estimation from MOSS data, conclusions about disease dynamics in the observed population can be drawn. To strengthen the analysis, the implementation of a maximum likelihood estimation is the main aim of our work. The new approach presented here is based on an underlying simple SIR (susceptible-infected-recovered) model for a disease scenario in a wildlife population. The three corresponding classes are assumed to govern the intensities (number of animals in the classes) of non-homogeneous Poisson processes. A sampling rate was defined which describes the process of data collection (for MOSSs). Further, the performance of the diagnostics was implemented in the model by a diagnostic matrix containing misclassification rates. Both descriptions of these MOSS parts were included in the Poisson process approach. For simulation studies, the combined model demonstrates its ability to validly estimate epidemiological parameters, such as the basic reproduction rate R0. These parameters will help the evaluation of existing disease control systems. They will also enable comparison with other simulation models. The model has been tested with data from a Classical Swine Fever (CSF) outbreak in wild boars (Sus scrofa scrofa L.) from a region of Germany (1999-2002). The results show that the hunting strategy as a sole control tool is insufficient to decrease the threshold for susceptible animals to eradicate the disease, since the estimated R0 confirms an ongoing epidemic of CSF.