title: Towards an early warning system for forecasting human west nile virus incidence.
abstract: We have identified environmental and demographic variables, available in January, that predict the relative magnitude and spatial distribution of West Nile virus (WNV) for the following summer. The yearly magnitude and spatial distribution for WNV incidence in humans in the United States (US) have varied wildly in the past decade. Mosquito control measures are expensive and having better estimates of the expected relative size of a future WNV outbreak can help in planning for the mitigation efforts and costs. West Nile virus is spread primarily between mosquitoes and birds; humans are an incidental host. Previous efforts have demonstrated a strong correlation between environmental factors and the incidence of WNV. A predictive model for human cases must include both the environmental factors for the mosquito-bird epidemic and an anthropological model for the risk of humans being bitten by a mosquito. Using weather data and demographic data available in January for every county in the US, we use logistic regression analysis to predict the probability that the county will have at least one WNV case the following summer. We validate our approach and the spatial and temporal WNV incidence in the US from 2005 to 2013. The methodology was applied to forecast the 2014 WNV incidence in late January 2014. We find the most significant predictors for a county to have a case of WNV to be the mean minimum temperature in January, the deviation of this minimum temperature from the expected minimum temperature, the total population of the county, publicly available samples of local bird populations, and if the county had a case of WNV the previous year.
authors.affiliation: Center for Computational Science, Tulane University, New Orleans, Louisiana, USA.
authors.forename: Carrie A
authors.initials: CA
authors.lastname: Manore
authors.affiliation: School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.
authors.forename: Justin
authors.initials: J
authors.lastname: Davis
authors.affiliation: Department of Pathobiological Sciences, Louisiana State University, Baton Rouge, Louisiana, USA.
authors.forename: Rebecca C
authors.initials: RC
authors.lastname: Christofferson
authors.affiliation: School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.
authors.forename: Dawn
authors.initials: D
authors.lastname: Wesson
authors.affiliation: Department of Mathematics, Tulane University, New Orleans, Louisiana, USA.
authors.forename: James M
authors.initials: JM
authors.lastname: Hyman
authors.affiliation: Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, Louisiana, USA.
authors.forename: Christopher N
authors.initials: CN
authors.lastname: Mores
keywords: West Nile virus; arbovirus; disease model; infectious disease; principal components; statistical model; statistics