ABSTRACT

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.

part |2 pages

I Background

chapter 1|16 pages

Introduction

chapter 2|18 pages

Bayesian Inference and Modeling

chapter 3|24 pages

Computational Issues

chapter 4|18 pages

Residuals and Goodness-of-Fit

part 2|2 pages

II Themes

chapter 6|32 pages

Disease Cluster Detection

chapter 7|36 pages

Regression and Ecological Analysis

chapter 8|20 pages

Putative Hazard Modeling

chapter 9|18 pages

Multiple Scale Analysis

chapter 10|26 pages

Multivariate Disease Analysis

chapter 11|26 pages

Spatial Survival and Longitudinal Analysis

chapter 12|18 pages

Spatio-Temporal Disease Mapping

chapter 13|18 pages

Disease Map Surveillance

chapter 14|26 pages

Infectious Disease Modeling

chapter 15|32 pages

Computational Software Issues