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This work provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease. It explores a range of topics in Bayesian inference and modeling, including Markov chain Monte Carlo methods, Gibbs sampling, the MetropolisHastings algorithm, goodness-of-fit measures, and residual diagnostics. The new edition includes new chapters on putative health hazard modeling and spatial and spatio-temporal surveillance, as well as many other changes and additions. Notably, the computing has been updated to reflect advances in software. The book also includes new datasets and examples.