Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition

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  • Provides the only integrated account of Bayesian disease mapping
  • Covers the recently developed INLA package in R
  • Presents wide-ranging coverage of Bayesian hierarchical regression modeling, including variable selection, missing data, and geographically dependent/adaptive regression
  • Includes extensive WinBUGS and R resources
  • Offers datasets and the WinBUGS and R code on the author’s website


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


Bayesian Inference and Modeling
Likelihood Models
Prior Distributions
Posterior Distributions
Predictive Distributions
Bayesian Hierarchical Modeling
Hierarchical Models
Posterior Inference

Computational Issues
Posterior Sampling
Markov Chain Monte Carlo Methods
Metropolis and Metropolis-Hastings Algorithms
Perfect Sampling
Posterior and Likelihood Approximations

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

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

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


Author Bio(s)

Andrew B. Lawson is a professor of biostatistics and eminent scholar in the Division of Biostatistics and Epidemiology in the College of Medicine at the Medical University of South Carolina. He is an ASA fellow and an advisor in disease mapping and risk assessment for the World Health Organization. Dr. Lawson has published over 100 journal papers and eight books and is the founding editor of Spatial and Spatio-temporal Epidemiology. He received a PhD in spatial statistics from the University of St. Andrews. His research interests include the analysis of clustered disease maps, spatial and spatio-temporal disease surveillance, nutritional measurement error, and Bayesian latent variable and SEM modeling.

Editorial Reviews

Praise for the Previous Edition

This book provides a technical grounding in spatial models while maintaining a strong grasp on applied epidemiological problems. … A welcome effort is made to clarify concepts which might, in other texts, have been skimmed over in a rush to fit models. … From the start, the concepts are illustrated with disease mapping examples, including R and WinBUGS code. … The book has relatively few errors … I recommend the book. It taught me new ideas and clarified existing ones. I shall continue to use it and I expect it to be useful for other statisticians with an interest in spatial analysis.
Journal of the Royal Statistical Society, Series A, April 2011

The readers who would like to get a big picture of hierarchical modeling in spatial epidemiology in a quick fashion will find this book very useful. This book covers a range of topics in hierarchical modeling for spatial epidemiological data and provides a practical, comprehensive, and up-to-date overview of the use of spatial statistics in epidemiology. … useful for readers to track down the topics of interests and see the varieties of up-to-date modeling techniques in spatial epidemiology or, more generally, spatial binary or count data. The author also lists the reference following each method for further information.
—Hongfei Li, Technometrics, November 2010

Lawson begins by building a solid Bayesian background … The remaining seven chapters provide a thorough review of modeling relative risk … Lawson provides well-written reviews of many topics and many aspects of those topics are covered in his reviews. The literature cited is huge and diverse, showing the current importance of the subjects covered. One can also gain hands-on training in analysis and visual presentations … by following carefully the detailed introduction to R and WinBUGS given in the book. Many important data sets used in the book are available online…
International Statistical Review (2009), 77, 2

This book is an excellent reference for intermediate learners of Bayesian disease mapping … many of the methodologies discussed in this book are applicable not only to spatial epidemiology but also to many other fields that utilize spatial data.
—J. Law, Biometrics, June 2009

Downloads / Updates

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Cross Platform June 04, 2013 Author web site click on