ABSTRACT
Bayesian methods have become commonplace in modern statistical applications. In the area of risk estimation and modeling the development of Bayesian models with random effects fitted via Markov chain Monte Carlo was first proposed by J. Besag et al. The widespread use of Bayesian methods in most areas of disease mapping is well established and there is a need to review and summarize these disparate strands in one place. Disease mapping goes under a variety of names, some of which are: spatial epidemiology, environmental epidemiology, disease mapping, small area health studies. However at the center of these different names are two characteristics. First a spatial or geographical distribution is the focus and so the relative location of events is important. The second ingredient is disease and the spatial distribution of disease is the focus. Hence the fundamental issue is how to analyze disease incidence or prevalence when public health specialists have geographical information.
