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

  • Nicola A. Wardrop | Nicola.Wardrop@soton.ac.uk Geography and Environment, Faculty of Social and Human Sciences, University of Southampton, Highfield, Southampton, United Kingdom.
  • Matthew Geary Geography and Environment, Faculty of Social and Human Sciences, University of Southampton, Highfield, Southampton; Department of Biological Sciences, University of Chester, Chester, United Kingdom.
  • Patrick E. Osborne Centre for Environmental Sciences, Faculty of Engineering and the Environment, University of Southampton, Highfield, Southampton, United Kingdom.
  • Peter M. Atkinson Geography and Environment, Faculty of Social and Human Sciences, University of Southampton, Highfield, Southampton, United Kingdom.

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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Published
2014-11-01
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Section
Original Articles
Keywords:
spatial epidemiology, predictive modelling, species distribution modelling
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How to Cite
Wardrop, N. A., Geary, M., Osborne, P. E., & Atkinson, P. M. (2014). Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology. Geospatial Health, 9(1), 237-246. https://doi.org/10.4081/gh.2014.397