Description
Since the publication of the second 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, Third Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications.
In addition to the new material, the book also 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.
The target audience for this text is public health specialists, epidemiologists, and biostatisticians who need to work with geo-referenced health data.
Table of Contents
Introduction
Bayesian Inference and Modeling
Computational Issues
Residuals and Goodness-of-Fit
Disease Map Reconstruction and Relative Risk Estimation
Disease Cluster Detection
Regression and Ecological Analysis
Putative Hazard Modeling
Multiple Scale Analysis
Multivariate Disease Analysis
Spatial Survival and Longitudinal Analysis
Spatio-temporal Disease Mapping
Disease Map Surveillance
Infectious disease Modeling
Computational Software Issues
Basic R and Win/OpenBUGS
Selected WinBUGS Code
R Code for Thematic Mapping
Appendices
