Cover Image

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

Nicola A. Wardrop, Matthew Geary, Patrick E. Osborne, Peter M. Atkinson
  • Nicola A. Wardrop
    Geography and Environment, Faculty of Social and Human Sciences, University of Southampton, Highfield, Southampton, United Kingdom | Nicola.Wardrop@soton.ac.uk
  • 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.

Keywords

spatial epidemiology, predictive modelling, species distribution modelling

Full Text:

PDF
Submitted: 2015-07-07 10:57:24
Published: 2014-11-01 00:00:00
Search for citations in Google Scholar
Related articles: Google Scholar
Abstract views:
348

Views:
PDF
196

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM


Copyright (c) 2014 Nicola A. Wardrop, Matthew Geary, Patrick E. Osborne, Peter M. Atkinson

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
 
© PAGEPress 2008-2017     -     PAGEPress is a registered trademark property of PAGEPress srl, Italy.     -     VAT: IT02125780185