Environmental predictors of West Nile fever risk in Europe

International Journal of Health Geographics

Jan C Semenza⁴  Véronique Chevalier¹  Annie Desbrosse⁵  Massimiliano Rossi³  Shlomit Paz²  Bertrand Sudre³  Annelise Tran⁵ 

[1] CIRAD, UPR Animal et Gestion Intégrée des Risques, Montpellier, France;Department of Geography and Environmental Studies, University of Haifa, Mt. Carmel, Haifa, Israel;Surveillance and Response Support, European Centre for Disease Prevention and Control, Surveillance and Response Support, Stockholm, Sweden;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;CIRAD, UMR Territoires Environnement Télédétection et Information Spatiale, Montpellier, France

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

Others  :  1146078
DOI  :  10.1186/1476-072X-13-26

 received in 2014-02-18, accepted in 2014-04-16,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Tran et al.; licensee BioMed Central Ltd.

【 图 表 】

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