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

Andrew B. Lawson

March 18, 2013 by Chapman and Hall/CRC
Reference - 396 Pages - 81 B/W Illustrations
ISBN 9781466504813 - CAT# K14543
Series: Chapman & Hall/CRC Interdisciplinary Statistics


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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.