Interpreting predictive maps of disease, highlighting the pitfalls of species distribution models in epidemiology

Hdl Handle:
http://hdl.handle.net/10034/338525
Title:
Interpreting predictive maps of disease, highlighting the pitfalls of species distribution models in epidemiology
Authors:
Wardrop, Nicola A.; Geary, Matthew; Osborne, Patrick E.; Atkinson, Peter M.
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.
Affiliation:
University of Southampton ; University of Chester / University of Southampton ; University of Southampton ; University of Southampton
Citation:
Geospatial Health, 2014, 9(1), pp. 237-246
Publisher:
University of Naples
Journal:
Geospatial Health
Publication Date:
2014
URI:
http://hdl.handle.net/10034/338525
Additional Links:
http://geospatialhealth.net
Type:
Article
Language:
en
Description:
This is the authors' PDF version of an article published in Geospatial Health© 2014. The definitive version is available at http://geospatialhealth.net
ISSN:
1827-1987
EISSN:
1970-7096
Sponsors:
This work was supported by the Medical Research Council (PMA, NAW - projects G0902445 and MR/J012343/1). The funders had no role in the decision to publish or in preparation of the manuscript.
Appears in Collections:
Biological Sciences

Full metadata record

DC FieldValue Language
dc.contributor.authorWardrop, Nicola A.en
dc.contributor.authorGeary, Matthewen
dc.contributor.authorOsborne, Patrick E.en
dc.contributor.authorAtkinson, Peter M.en
dc.date.accessioned2015-01-19T12:08:57Zen
dc.date.available2015-01-19T12:08:57Zen
dc.date.issued2014en
dc.identifier.citationGeospatial Health, 2014, 9(1), pp. 237-246en
dc.identifier.issn1827-1987en
dc.identifier.urihttp://hdl.handle.net/10034/338525en
dc.descriptionThis is the authors' PDF version of an article published in Geospatial Health© 2014. The definitive version is available at http://geospatialhealth.neten
dc.description.abstractThe 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.en
dc.description.sponsorshipThis work was supported by the Medical Research Council (PMA, NAW - projects G0902445 and MR/J012343/1). The funders had no role in the decision to publish or in preparation of the manuscript.en
dc.language.isoenen
dc.publisherUniversity of Naplesen
dc.relation.urlhttp://geospatialhealth.neten
dc.subjectspatial epidemiologyen
dc.subjectpredictive modelling.en
dc.subjectspecies distribution modellingen
dc.titleInterpreting predictive maps of disease, highlighting the pitfalls of species distribution models in epidemiologyen
dc.typeArticleen
dc.identifier.eissn1970-7096en
dc.contributor.departmentUniversity of Southampton ; University of Chester / University of Southampton ; University of Southampton ; University of Southamptonen
dc.identifier.journalGeospatial Healthen
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