Markov Chain Monte Carlo Methods for Clustering in Case Event and Count Data in Spatial Epidemiology

The analysis of clustering in small area data in epidemiology is considered. A modelling paradigm which is based on point process models is proposed for both case event and count data observed in arbitrary regions. Use is made of combinations of Markov Chain Monte Carlo (MCMC) methods, including forms of data augmentation, to provide a common approach. Examples of case event and count data are provided.

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