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Environmental predictors of West Nile fever risk in Europe
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, consid...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4118316/ https://www.ncbi.nlm.nih.gov/pubmed/24986363 http://dx.doi.org/10.1186/1476-072X-13-26 |
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author | Tran, Annelise Sudre, Bertrand Paz, Shlomit Rossi, Massimiliano Desbrosse, Annie Chevalier, Véronique Semenza, Jan C |
author_facet | Tran, Annelise Sudre, Bertrand Paz, Shlomit Rossi, Massimiliano Desbrosse, Annie Chevalier, Véronique Semenza, Jan C |
author_sort | Tran, Annelise |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4118316 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41183162014-08-05 Environmental predictors of West Nile fever risk in Europe Tran, Annelise Sudre, Bertrand Paz, Shlomit Rossi, Massimiliano Desbrosse, Annie Chevalier, Véronique Semenza, Jan C Int J Health Geogr Research 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. BioMed Central 2014-07-01 /pmc/articles/PMC4118316/ /pubmed/24986363 http://dx.doi.org/10.1186/1476-072X-13-26 Text en Copyright © 2014 Tran et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Tran, Annelise Sudre, Bertrand Paz, Shlomit Rossi, Massimiliano Desbrosse, Annie Chevalier, Véronique Semenza, Jan C Environmental predictors of West Nile fever risk in Europe |
title | Environmental predictors of West Nile fever risk in Europe |
title_full | Environmental predictors of West Nile fever risk in Europe |
title_fullStr | Environmental predictors of West Nile fever risk in Europe |
title_full_unstemmed | Environmental predictors of West Nile fever risk in Europe |
title_short | Environmental predictors of West Nile fever risk in Europe |
title_sort | environmental predictors of west nile fever risk in europe |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4118316/ https://www.ncbi.nlm.nih.gov/pubmed/24986363 http://dx.doi.org/10.1186/1476-072X-13-26 |
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