When small area disease data in the form of counts are available, Bayesian hierarchical Poisson models are commonly used to describe the behavior of disease (5) . In this study, we use the convolution model (6) to describe the behavior of disease under endemic conditions. Each time new observations become available, we show how the conditional predictive ordinate (CPO, 7), which is a Bayesian diagnostic tool that detects unusual observations, can be adapted in a surveillance context to detect small areas of unusual disease aggregation (8) .