title: Risk factors for human infection with West Nile Virus in Connecticut: a multi-year analysis.
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.
  
  
  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.
authors: Yale Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA. aliu@jhsph.edu
authors: Ann
authors: A
authors: Liu
authors: Vivian
authors: V
authors: Lee
authors: Deron
authors: D
authors: Galusha
authors: Martin D
authors: MD
authors: Slade
authors: Maria
authors: M
authors: Diuk-Wasser
authors: Theodore
authors: T
authors: Andreadis
authors: Matthew
authors: M
authors: Scotch
authors: Peter M
authors: PM
authors: Rabinowitz
mesh_terms: 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