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https://doi.org/10.1186/1476-072x-8-67
Copy DOIPublication Date: Nov 27, 2009 | |
Citations: 67 | License type: cc-by |
BackgroundThe optimal method for early prediction of human West Nile virus (WNV) infection risk remains controversial. We analyzed the predictive utility of risk factor data for human WNV over a six-year period in Connecticut.Results and DiscussionUsing only environmental variables or animal sentinel data was less predictive than a model that considered all variables. In the final parsimonious model, population density, growing degree-days, temperature, WNV positive mosquitoes, dead birds and WNV positive birds were significant predictors of human infection risk, with an ROC value of 0.75.ConclusionA real-time model using climate, land use, and animal surveillance data to predict WNV risk appears feasible. The dynamic patterns of WNV infection suggest a need to periodically refine such prediction systems.MethodsUsing multiple logistic regression, the 30-day risk of human WNV infection by town was modeled using environmental variables as well as mosquito and wild bird surveillance.
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