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Current risk mapping models for pooled data focus on the estimated risk foreach geographical unit. A risk classification, that is, grouping ofgeographical units with similar risk, is then necessary to easily drawinterpretable maps, with clearly delimited zones in which protection measurescan be applied. As an illustration, we focus on the Bovine SpongiformEncephalopathy (BSE) disease that threatened the bovine production in Europeand generated drastic cow culling. This example features typical animal diseaserisk analysis issues with very low risk values, small numbers of observed casesand population sizes that increase the difficulty of an automaticclassification. We propose to handle this task in a spatial clusteringframework using a nonstandard discrete hidden Markov model prior designed tofavor a smooth risk variation. The model parameters are estimated using an EMalgorithm and a mean field approximation for which we develop a newinitialization strategy appropriate for spatial Poisson mixtures. Using bothsimulated and our BSE data, we show that our strategy performs well in dealingwith low population sizes and accurately determines high risk regions, both interms of localization and risk level estimation.

Spatial risk mapping for rare disease with hidden Markov fields and variational EM