Remote sensing based identification of environmental risk factors associated with West Nile disease in horses in Camargue, France

Leblond, Agnès; Sandoz, Alain; Lefebvre, Gaétan; Zeller, H.; Bicout, Dominique

doi:10.1016/j.prevetmed.2006.11.008

Cited by 42 publications

(35 citation statements)

References 15 publications

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“…The role of water bodies in WNV transmission is related to the availability of breeding sites for the mosquito vector populations and may be complex: on one hand, large areas of surfacewater may favor vector abundance and WNV transmission as demonstrated in other studies [26], but in drought conditions, the reduced number of water pools may lead to higher interactions between wild birds and mosquitoes [27]. Our findings support the first hypothesis, suggesting that in Europe, above average surface of water in June, favoring mosquito proliferation before the summer months, is a significant risk factor for WNV transmission.…”

Section: Resultsmentioning

confidence: 99%

Environmental predictors of West Nile fever risk in Europe

et al. 2014

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BackgroundWest 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.MethodsThe 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.ResultsThe 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.ConclusionsWNF 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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Section: Resultsmentioning

confidence: 99%

Environmental predictors of West Nile fever risk in Europe

et al. 2014

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“…In addition, the Bayesian approach could be easily adapted to spatiotemporal analysis. Such approach could be especially relevant for WNV surveillance as there are strong links between environment and WNV outbreaks, and as we expect local clusters of cases (Mostashari et al 2003, Leblond et al 2007b). Without integrating a spatiotemporal approach, the usefulness of a multivariate syndromic approach could be limited, especially for vector-borne disease surveillance, and thus, the next step would be to develop and test a spatiotemporal model.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a Multivariate Syndromic Surveillance System for West Nile Virus

Faverjon

Andersson

Decors

et al. 2016

Vector-Borne and Zoonotic Diseases

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Background: Various methods are currently used for the early detection of West Nile virus (WNV) but their outputs are not quantitative and/or do not take into account all available information. Our study aimed to test a multivariate syndromic surveillance system to evaluate if the sensitivity and the specificity of detection of WNV could be improved. Methods: Weekly time series data on nervous syndromes in horses and mortality in both horses and wild birds were used. Baselines were fitted to the three time series and used to simulate 100 years of surveillance data. WNV outbreaks were simulated and inserted into the baselines based on historical data and expert opinion. Univariate and multivariate syndromic surveillance systems were tested to gauge how well they detected the outbreaks; detection was based on an empirical Bayesian approach. The systems' performances were compared using measures of sensitivity, specificity, and area under receiver operating characteristic curve (AUC). Results: When data sources were considered separately (i.e., univariate systems), the best detection performance was obtained using the data set of nervous symptoms in horses compared to those of bird and horse mortality (AUCs equal to 0.80, 0.75, and 0.50, respectively). A multivariate outbreak detection system that used nervous symptoms in horses and bird mortality generated the best performance (AUC = 0.87). Conclusions: The proposed approach is suitable for performing multivariate syndromic surveillance of WNV outbreaks. This is particularly relevant, given that a multivariate surveillance system performed better than a univariate approach. Such a surveillance system could be especially useful in serving as an alert for the possibility of human viral infections. This approach can be also used for other diseases for which multiple sources of evidence are available.

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“…Lastly, land usage and landscape characteristics can also be provided by satellite imagery and more specifically Earth observation imagery [86,87]. These products can provide data on land use and land cover known to influence surface and recreational water quality such as agricultural lands, impervious (built) surfaces, wetlands, and forest areas [88][89][90][91].…”

Section: Quantitatively Measuring These Sources and Factors And Theirmentioning

confidence: 99%

Monitoring Recreational Waters: How to Integrate Environmental Determinants

Turgeon¹

2012

JEP

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Recreational waters are associated with a higher risk of disease for people engaged in activities that bring them into contact with these waters. The primary cause of contamination of recreational waters is fecal microorganisms, which may originate from various sources and involve several modulating factors, making it a complex public health and environmental issue. Monitoring recreational water quality should include two key components: Microbial water testing and monitoring environmental determinants associated with higher risks of contamination. Conducting both activities provides the foundation for a comprehensive assessment according to risk and the actual level of fecal pollution and thus could promote good management actions to ensure safe water quality. Nevertheless, monitoring of environmental determinants is rarely fully integrated in monitoring programs and is also harder to achieve, especially when water pollution is mainly associated with nonpoint sources. In order to achieve identification and monitoring of environmental determinants associated with fecal contamination of recreational waters, some specific steps should be followed and some questions must be answered. The objective of this review article is to present current knowledge on this topic and to suggest and discuss recommendations. Potential sources of contamination and factors able to modulate them should be identified and measured after the geographical area influencing fecal contamination of recreational water has been delineated. Statistical models have been developed to identify the relative importance of these environmental characteristics on fecal pollution of recreational waters but they do not allow for a full comprehension of the exact processes leading to this pollution, thus other methods should also be used to better understand these processes.

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Remote sensing based identification of environmental risk factors associated with West Nile disease in horses in Camargue, France

Cited by 42 publications

References 15 publications

Environmental predictors of West Nile fever risk in Europe

Environmental predictors of West Nile fever risk in Europe

Evaluation of a Multivariate Syndromic Surveillance System for West Nile Virus

Monitoring Recreational Waters: How to Integrate Environmental Determinants

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