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

Andrew B. Lawson

Chapman and Hall/CRC
Published May 24, 2018
Reference - 464 Pages - 156 B/W Illustrations
ISBN 9781138575424 - CAT# K43675
Series: Chapman & Hall/CRC Interdisciplinary Statistics

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  • Provides the only integrated state of the art account of Bayesian disease mapping
  • Presents wide-ranging coverage of Bayesian hierarchical regression modeling, including variable selection, missing data, and geographically dependent/adaptive regression
  • Features two new chapters on infectious disease modeling, and computational software issues, focusing on the use of R for data processing and map graphics as well as modelling
  • Expanded material on disease mapping, surveillance, spatiotemporal analysis, including the use of the Leroux model, sample survey and small area estimation
  • Additional emphasis on individual level infection modelling, One Health, joint modelling, and Zoonosis
  • Covers the recently developed CARBayes, OpenBUGS, and INLA packages
  • Offers datasets and the OpenBUGS and R code on the author’s website.


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.


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