title: Assessment of methods for prediction of human West Nile virus (WNV) disease from WNV-infected dead birds.
abstract: BACKGROUND
  West Nile virus (WNV) is currently the leading cause of arboviral-associated encephalitis in the U.S., and can lead to long-term neurologic sequelae. Improvements in dead bird specimen processing time, including the availability of rapid field laboratory tests, allows reassessment of the effectiveness of using WNV-positive birds in forecasting human WNV disease.
  
  
  METHODS
  Using New York State integrated WNV surveillance data from transmissions seasons in 2001-2003, this study determined which factors associated with WNV-positive dead birds are most closely associated with human disease. The study also addressed the 'delay' period between the distribution of the dead bird variable and the distribution of the human cases. In the last step, the study assessed the relative risk of contracting WNV disease for people who lived in counties with a 'signal' value of the predictor variable versus people who lived in counties with no 'signal' value of the predictor variable.
  
  
  RESULTS
  The variable based on WNV-positive dead birds [(Positive/Tested)*(Population/Area)] was identified as the optimum variable for predicting WNV human disease at a county level. The delay period between distribution of the variable and human cases was determined to be approximately two weeks. For all 3 years combined, the risk of becoming a WNV case for people who lived in 'exposed' counties (those with levels of the positive dead bird variable above the signal value) was about 2 times higher than the risk for people who lived in 'unexposed' counties, but risk varied by year.
  
  
  CONCLUSIONS
  This analysis develops a new variable based on WNV-positive dead birds, [(Positive/Tested)*(Population/Area)] to be assessed in future real-time studies for forecasting the number of human cases in a county. A delay period of approximately two weeks between increases in this variable and the human case onset was identified. Several threshold 'signal' values were assessed and found effective at indicating human case risk, although specific thresholds are likely to vary by region and surveillance system differences.
authors.affiliation: School of Public Health, University at Albany, One University Place, Rensselaer, NY 12144, USA. annaveksler@yahoo.com
authors.forename: Anna
authors.initials: A
authors.lastname: Veksler
authors.forename: Millicent
authors.initials: M
authors.lastname: Eidson
authors.forename: Igor
authors.initials: I
authors.lastname: Zurbenko