Making the most of spatial information in health: A tutorial in Bayesian disease mapping for areal data
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 mapp...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/54222 |
| _version_ | 1848759315839582208 |
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| author | Kang, S. Cramb, S. White, N. Ball, Stephen Mengersen, K. |
| author_facet | Kang, S. Cramb, S. White, N. Ball, Stephen Mengersen, K. |
| author_sort | Kang, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 epi demiological 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. |
| first_indexed | 2025-11-14T09:57:56Z |
| format | Journal Article |
| id | curtin-20.500.11937-54222 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:57:56Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-542222018-01-17T08:41:46Z Making the most of spatial information in health: A tutorial in Bayesian disease mapping for areal data Kang, S. Cramb, S. White, N. Ball, Stephen Mengersen, K. 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 epi demiological 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. 2016 Journal Article http://hdl.handle.net/20.500.11937/54222 10.4081/gh.2016.428 http://creativecommons.org/licenses/by-nc/4.0/ fulltext |
| spellingShingle | Kang, S. Cramb, S. White, N. Ball, Stephen Mengersen, K. Making the most of spatial information in health: A tutorial in Bayesian disease mapping for areal data |
| title | Making the most of spatial information in health: A tutorial in Bayesian disease mapping for areal data |
| title_full | Making the most of spatial information in health: A tutorial in Bayesian disease mapping for areal data |
| title_fullStr | Making the most of spatial information in health: A tutorial in Bayesian disease mapping for areal data |
| title_full_unstemmed | Making the most of spatial information in health: A tutorial in Bayesian disease mapping for areal data |
| title_short | Making the most of spatial information in health: A tutorial in Bayesian disease mapping for areal data |
| title_sort | making the most of spatial information in health: a tutorial in bayesian disease mapping for areal data |
| url | http://hdl.handle.net/20.500.11937/54222 |