A cluster model for space–time disease counts

Modelling disease clustering over space and time can be helpful in providing indications of possible exposures and planning corresponding public health practices. Though a considerable number of studies focus on modelling spatio‐temporal patterns of disease, most of them do not directly model a spatio‐temporal clustering structure and could be ineffective for detecting clusters. In this paper, we extend a purely spatial cluster model to accommodate space–time clustering. Inference is performed in a Bayesian framework using reversible jump Markov chain Monte Carlo. This idea is illustrated using data on female breast cancer mortality from Japan. A hierarchical parametric space–time model for mapping disease is used for comparison. Copyright © 2006 John Wiley & Sons, Ltd.