Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data

Geospat Health. 2016 May 31;11(2):428. doi: 10.4081/gh.2016.428.


Disease maps are effective tools for explaining and predicting patterns of disease outcomes across geographical space, identifying areas of potentially elevated risk, and formulating and validating aetiological hypotheses for a disease. Bayesian models have become a standard approach to disease mapping in recent decades. This article aims to provide a basic understanding of the key concepts involved in Bayesian disease mapping methods for areal data. It is anticipated that this will help in interpretation of published maps, and provide a useful starting point for anyone interested in running disease mapping methods for areal data. The article provides detailed motivation and descriptions on disease mapping methods by explaining the concepts, defining the technical terms, and illustrating the utility of disease mapping for epidemiological research by demonstrating various ways of visualising model outputs using a case study. The target audience includes spatial scientists in health and other fields, policy or decision makers, health geographers, spatial analysts, public health professionals, and epidemiologists.

MeSH terms

  • Australia / epidemiology
  • Bayes Theorem*
  • Geographic Information Systems
  • Geographic Mapping*
  • Humans
  • Models, Statistical
  • Neoplasms / epidemiology
  • Public Health*
  • Spatial Analysis