  Current risk mapping models for pooled data focus on the estimated risk for
each geographical unit. A risk classification, that is, grouping of
geographical units with similar risk, is then necessary to easily draw
interpretable maps, with clearly delimited zones in which protection measures
can be applied. As an illustration, we focus on the Bovine Spongiform
Encephalopathy (BSE) disease that threatened the bovine production in Europe
and generated drastic cow culling. This example features typical animal disease
risk analysis issues with very low risk values, small numbers of observed cases
and population sizes that increase the difficulty of an automatic
classification. We propose to handle this task in a spatial clustering
framework using a nonstandard discrete hidden Markov model prior designed to
favor a smooth risk variation. The model parameters are estimated using an EM
algorithm and a mean field approximation for which we develop a new
initialization strategy appropriate for spatial Poisson mixtures. Using both
simulated and our BSE data, we show that our strategy performs well in dealing
with low population sizes and accurately determines high risk regions, both in
terms of localization and risk level estimation.
