Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology
Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology
The application of spatial modelling to epidemiology has increased significantly over the past decade, delivering enhanced understanding of the environmental and climatic factors affecting disease distributions and providing spatially continuous representations of disease risk (predictive maps). These outputs provide significant information for disease control programmes, allowing spatial targeting and tailored interventions. However, several factors (e.g. sampling protocols or temporal disease spread) can influence predictive mapping outputs. This paper proposes a conceptual framework which defines several scenarios and their potential impact on resulting predictive outputs, using simulated data to provide an exemplar. It is vital that researchers recognise these scenarios and their influence on predictive models and their outputs, as a failure to do so may lead to inaccurate interpretation of predictive maps. As long as these considerations are kept in mind, predictive mapping will continue to contribute significantly to epidemiological research and disease control planning.
237-246
Wardrop, Nicola A.
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Geary, Matthew
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Osborne, Patrick E.
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Atkinson, Peter M.
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November 2014
Wardrop, Nicola A.
8f3a8171-0727-4375-bc68-10e7d616e176
Geary, Matthew
12c5d728-838e-46f2-8a8c-99ec1a659936
Osborne, Patrick E.
c4d4261d-557c-4179-a24e-cdd7a98fb2b8
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Wardrop, Nicola A., Geary, Matthew, Osborne, Patrick E. and Atkinson, Peter M.
(2014)
Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology.
Geospatial Health, 9 (1), .
(doi:10.4081/gh.2014.397).
(PMID:25545941)
Abstract
The application of spatial modelling to epidemiology has increased significantly over the past decade, delivering enhanced understanding of the environmental and climatic factors affecting disease distributions and providing spatially continuous representations of disease risk (predictive maps). These outputs provide significant information for disease control programmes, allowing spatial targeting and tailored interventions. However, several factors (e.g. sampling protocols or temporal disease spread) can influence predictive mapping outputs. This paper proposes a conceptual framework which defines several scenarios and their potential impact on resulting predictive outputs, using simulated data to provide an exemplar. It is vital that researchers recognise these scenarios and their influence on predictive models and their outputs, as a failure to do so may lead to inaccurate interpretation of predictive maps. As long as these considerations are kept in mind, predictive mapping will continue to contribute significantly to epidemiological research and disease control planning.
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e-pub ahead of print date: 1 November 2014
Published date: November 2014
Organisations:
Geography & Environment, Civil Maritime & Env. Eng & Sci Unit
Identifiers
Local EPrints ID: 382726
URI: http://eprints.soton.ac.uk/id/eprint/382726
ISSN: 1827-1987
PURE UUID: 83b1aafd-82d9-4c7f-b205-f036ddcb13a9
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Date deposited: 09 Oct 2015 15:44
Last modified: 15 Mar 2024 03:21
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Author:
Matthew Geary
Author:
Peter M. Atkinson
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