Canadian journal of public health = Revue canadienne de sante publique · 2009
Predicting geographical human risk of West Nile virus--Saskatchewan, 2003 and 2007.
СтатьяНазвание и аннотация: Перевод в очереди
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Epp Tasha Y, Waldner Cheryl L, Berke Olaf
Аннотация
OBJECTIVES: To detail the use of a model to predict areas of low, medium, and high risk of West Nile virus (WNV) in humans in both 2003 and 2007 in the province of Saskatchewan. To identify consistent high-risk areas from year to year as well as important environmental variables within those high-risk areas.
METHODS: The number of laboratory-confirmed WNV individuals was obtained from Saskatchewan Health by rural municipality. The population at risk was obtained from Statistics Canada by rural municipality. Climate and habitat variables were incorporated into a discriminant analysis model with the production of risk maps as an end product.
RESULTS: The discriminant analysis models had testing classification accuracies of 67% in 2003 and 44% in 2007. Climate and habitat variables remained important in all models while some habitat variables were less important in 2007. Risk maps from historically trained 2007 model revealed a southwest to northeast decreasing trend of risk.
CONCLUSION: The models could be useful for indicating areas of high risk on a year-to-year basis or based on historical data. High-risk regions are characterized by less rainfall in June and July followed by higher temperatures in July and August with less vegetation and water coverage than low-risk regions.
METHODS: The number of laboratory-confirmed WNV individuals was obtained from Saskatchewan Health by rural municipality. The population at risk was obtained from Statistics Canada by rural municipality. Climate and habitat variables were incorporated into a discriminant analysis model with the production of risk maps as an end product.
RESULTS: The discriminant analysis models had testing classification accuracies of 67% in 2003 and 44% in 2007. Climate and habitat variables remained important in all models while some habitat variables were less important in 2007. Risk maps from historically trained 2007 model revealed a southwest to northeast decreasing trend of risk.
CONCLUSION: The models could be useful for indicating areas of high risk on a year-to-year basis or based on historical data. High-risk regions are characterized by less rainfall in June and July followed by higher temperatures in July and August with less vegetation and water coverage than low-risk regions.