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Open Access Highly Accessed Research

Environmental predictors of West Nile fever risk in Europe

Annelise Tran12, Bertrand Sudre3, Shlomit Paz4, Massimiliano Rossi3, Annie Desbrosse2, Véronique Chevalier1 and Jan C Semenza5*

Author Affiliations

1 CIRAD, UPR Animal et Gestion Intégrée des Risques, Montpellier, France

2 CIRAD, UMR Territoires Environnement Télédétection et Information Spatiale, Montpellier, France

3 Surveillance and Response Support, European Centre for Disease Prevention and Control, Surveillance and Response Support, Stockholm, Sweden

4 Department of Geography and Environmental Studies, University of Haifa, Mt. Carmel, Haifa, Israel

5 Head of Health Determinants Programme, Office of the Chief Scientist, European Centre for Disease Prevention and Control, Office of the Chief Scientist, Stockholm, SE-171 83, Sweden

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International Journal of Health Geographics 2014, 13:26  doi:10.1186/1476-072X-13-26

Published: 1 July 2014

Abstract

Background

West Nile virus (WNV) is a mosquito-borne pathogen of global public health importance. Transmission of WNV is determined by abiotic and biotic factors. The objective of this study was to examine environmental variables as predictors of WNV risk in Europe and neighboring countries, considering the anomalies of remotely sensed water and vegetation indices and of temperature at the locations of West Nile fever (WNF) outbreaks reported in humans between 2002 and 2013.

Methods

The status of infection by WNV in relationship to environmental and climatic risk factors was analyzed at the district level using logistic regression models. Temperature, remotely sensed Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI) anomalies, as well as population, birds’ migratory routes, and presence of wetlands were considered as explanatory variables.

Results

The anomalies of temperature in July, of MNDWI in early June, the presence of wetlands, the location under migratory routes, and the occurrence of a WNF outbreak the previous year were identified as risk factors. The best statistical model according to the Akaike Information Criterion was used to map WNF risk areas in 2012 and 2013. Model validations showed a good level of prediction: area under Receiver Operator Characteristic curve = 0.854 (95% Confidence Interval 0.850-0.856) for internal validation and 0.819 (95% Confidence Interval 0.814-0.823) (2012) and 0.853 (95% Confidence Interval 0.850-0.855) (2013) for external validations, respectively.

Conclusions

WNF incidence is increasing in Europe and WNV is expanding into new areas where it had never been observed before. Our model can be used to direct surveillance activities and public health interventions for the upcoming WNF season.

Keywords:
West nile fever; West nile virus; Environmental determinants; Epidemiology; Temperature; Surveillance; Arbovirus; Remote sensing; Risk maps