Statistical methods for geographical surveillance in veterinary epidemiology.

Spatial clustering and cluster detection are statistical analysis developed to address relevant scientific hypothesis. The difficulty stays in the large number of alternative hypothesis due to the different mechanisms that could generate the anomalous cases aggregation. We review methods for marked point data (case/control) aimed to describe spatial intensity of disease risk, to test for randomness and to locate significant excesses. Bayesian Gaussian Spatial Exponential models are used to illustrate probabilistic aspects and the link with simpler non parametric tools are shown. We develop an informal guideline to the analysis and used data on faecal contamination and dog parasitic diseases in the city of Naples, Italy. Kernel density estimation resulted very sensitive to bandwidth choice and overemphasized localized excess, Ripley'K function and Cuzick-Edwards test were very consistent each other while the SatScan failed to detect excesses. The spatial range was around 600 meters and justifies several small clusters. Bayesian models were very powerful in reconstructing the phenomenon and allow inference on model parameters in good agreement with the non parametric analysis.