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


CONCLUSIONS
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.


CONCLUSIONS
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.
Liu
Ann
A
Yale Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA. aliu@jhsph.edu
Lee
Vivian
V
Galusha
Deron
D
Slade
Martin D
MD
Diuk-Wasser
Maria
M
Andreadis
Theodore
T
Scotch
Matthew
M
Rabinowitz
Peter M
PM
D000383:Agriculture; D002980:Climate; D003237:Connecticut; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008962:Models, Theoretical; D011156:Population Density; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D014901:West Nile Fever / Q000209:etiology* / Q000635:transmission; D014902:West Nile virus