Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. A biostatistics professor and WHO advisor, the author illustrates the use of Bayesian hierarchical modeling in the geographical analysis of disease through a range of real-world datasets. New to the Second Edition Three new chapters on regression and ecological analysis, putative hazard modeling, and disease map surveillance Expanded material on case event modeling and spatiotemporal analysis New and updated examples Two new appendices featuring examples of integrated nested Laplace approximation (INLA) and conditional autoregressive (CAR) models In addition to these new topics, the book covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. WinBUGS and R are used throughout for data manipulation and simulation.

Table of Contents:
BACKGROUND Introduction Datasets Bayesian Inference and Modeling Likelihood Models Prior Distributions Posterior Distributions Predictive Distributions Bayesian Hierarchical Modeling Hierarchical Models Posterior Inference Exercises Computational Issues Posterior Sampling Markov Chain Monte Carlo Methods Metropolis and Metropolis-Hastings Algorithms Perfect Sampling Posterior and Likelihood Approximations Exercises Residuals and Goodness-of-Fit Model GOF Measures General Residuals Bayesian Residuals Predictive Residuals and the Bootstrap Interpretation of Residuals in a Bayesian Setting Pseudo Bayes Factors and Marginal Predictive Likelihood Other Diagnostics Exercises THEMES Disease Map Reconstruction and Relative Risk Estimation An Introduction to Case Event and Count Likelihoods Specification of the Predictor in Case Event and Count Models Simple Case and Count Data Models with Uncorrelated Random Effects Correlated Heterogeneity Models Convolution Models Model Comparison and Goodness-of-Fit Diagnostics Alternative Risk Models Edge Effects Exercises Disease Cluster Detection Cluster Definitions Cluster Detection using Residuals Cluster Detection using Posterior Measures Cluster Models Edge Detection and Wombling Regression and Ecological Analysis Basic Regression Modeling Missing Data Non-Linear Predictors Confounding and Multi-Colinearity Geographically Dependent Regression Variable Selection Ecological Analysis: The General Case of Regression Biases and Misclassification Error Putative Hazard Modeling Case Event Data Aggregated Count Data Spatiotemporal Effects Multiple Scale Analysis Modifiable Areal Unit Problem (MAUP) Misaligned Data Problem (MIDP) Multivariate Disease Analysis Notation for Multivariate Analysis Two Diseases Multiple Diseases Spatial Survival and Longitudinal Analyses General Issues Spatial Survival Analysis Spatial Longitudinal Analysis Extensions to Repeated Events Spatiotemporal Disease Mapping Case Event Data Count Data Alternative Models Infectious Diseases Disease Map Surveillance Surveillance Concepts Temporal Surveillance Spatial and Spatiotemporal Surveillance Appendices