TY - JOUR

T1 - Interpreting predictive maps of disease

T2 - highlighting the pitfalls of distribution models in epidemiology

AU - Wardrop, Nicola A.

AU - Geary, Matthew

AU - Osborne, Patrick E.

AU - Atkinson, Peter M.

N1 - M1 - 1

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - spatial epidemiology

KW - predective modelling

KW - species distribution modelling

U2 - 10.4081/gh.2014.397

DO - 10.4081/gh.2014.397

M3 - Journal article

VL - 9

SP - 237

EP - 246

JO - Geospatial Health

JF - Geospatial Health

SN - 1827-1987

IS - 1

ER -