Research
Risk factors for human infection with West Nile Virus in Connecticut: a multi-year analysis
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* Corresponding author: Matthew Scotch matthew.scotch@yale.edu
1 Yale Occupational and Environmental Medicine Program, Yale University School of Medicine, New Haven, CT, USA
2 Yale School of Public Health, New Haven, CT, USA
3 Connecticut Agricultural Experiment Station, New Haven, CT, USA
4 Yale Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA
International Journal of Health Geographics 2009, 8:67 doi:10.1186/1476-072X-8-67
Published: 27 November 2009Abstract
Background
The 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 Discussion
Using 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.
Conclusion
A 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.
Methods
Using 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.