Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies.


  • Review of R graphics relevant to spatial health data
  • Overview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data
  • Bayesian Computation and goodness-of-fit
  • Review of basic Bayesian disease mapping models
  • Spatio-temporal modeling with MCMC and INLA
  • Special topics include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling
  • Software for fitting models based on BRugs, Nimble, CARBayes and INLA
  • Provides code relevant to fitting all examples throughout the book at a supplementary website

The book fills a void in the literature and available software, providing a crucial link for students and professionals alike to engage in the analysis of spatial and spatio-temporal health data from a Bayesian perspective using R. The book emphasizes the use of MCMC via Nimble, BRugs, and CARBAyes, but also includes INLA for comparative purposes. In addition, a wide range of packages useful in the analysis of geo-referenced spatial data are employed and code is provided. It will likely become a key reference for researchers and students from biostatistics, epidemiology, public health, and environmental science.

chapter 1|14 pages

Introduction and Datasets

chapter 2|18 pages

R Graphics and Spatial Health Data

chapter 3|8 pages

Bayesian Hierarchical Models

chapter 4|18 pages


chapter 5|8 pages

Bayesian model Goodness of Fit Criteria

chapter 6|20 pages

Bayesian Disease Mapping Models

part I|102 pages

Basic Software Approaches

chapter 887|20 pages


chapter 8|18 pages


chapter 9|12 pages


chapter 10|10 pages


chapter 12|22 pages

Spatio-Temporal Modeling with MCMC

chapter 13|8 pages

Spatio-Temporal Modeling with INLA

part II|74 pages

Some Advanced and Special topics

chapter 19014|14 pages

Multivariate Models

chapter 15|10 pages

Survival Modeling

chapter 17|16 pages

Individual Event Modeling

chapter 18|20 pages

Infectious Disease Modeling