Research

Risk factors for human infection with West Nile Virus in Connecticut: a multi-year analysis

Ann Liu¹ , Vivian Lee¹ , Deron Galusha¹ , Martin D Slade¹ , Maria Diuk-Wasser² , Theodore Andreadis³ , Matthew Scotch⁴ and Peter M Rabinowitz¹

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

author email corresponding author email

International Journal of Health Geographics 2009, 8:67doi:10.1186/1476-072X-8-67

Published:	27 November 2009

Abstract

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.