Interpreting predictive maps of disease, highlighting the pitfalls of species distribution models in epidemiology
AffiliationUniversity of Southampton ; University of Chester / University of Southampton ; University of Southampton ; University of Southampton
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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.
CitationGeospatial Health, 2014, 9(1), pp. 237-246
PublisherUniversity of Naples
DescriptionThis is the authors' PDF version of an article published in Geospatial Health© 2014. The definitive version is available at http://geospatialhealth.net
SponsorsThis 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.
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