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. 2014 Jul 1:13:26.
doi: 10.1186/1476-072X-13-26.

Environmental predictors of West Nile fever risk in Europe

Annelise Tran, Bertrand Sudre, Shlomit Paz, Massimiliano Rossi, Annie Desbrosse, Véronique Chevalier, Jan C Semenza  1
Affiliations

Environmental predictors of West Nile fever risk in Europe

Annelise Tran et al. Int J Health Geogr. .

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.

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Figures

Figure 1
Figure 1
Map of West Nile fever outbreaks by year, Europe and neighboring countries between 2002 and 2011. Note: The map represents the number of WNF presence notification by year over the study period (2002–2011).
Figure 2
Figure 2
Anomalies of temperatures and MNDWI values, 2010–2013. a) Monthly anomalies from the perennial mean monthly temperature (July). b) MNDWI anomalies from the 2002–2011 average (21st MODIS period).
Figure 3
Figure 3
ROC curves of the best model of the probability of WNV infection.
Figure 4
Figure 4
Map of predicted probability of WNV infection based on environmental predictors, Europe and neighboring countries, 2012 and 2013.
Figure 5
Figure 5
Study area: location of the West Nile fever outbreaks, Europe and neighbouring countries, 2002–2011, and extent of environmental datasets. Note: the brown box represents the extent of temperature dataset; the green boxes represent the extent of the MODIS tiles.
See this image and copyright information in PMC

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