Assessing Ultra-Fine-Scale Factors to Improve Human West Nile Virus Disease Models in the Chicago Area

Background: Since 1999, West Nile virus (WNV) has moved rapidly across the United States, resulting in tens of thousands of human cases. Both the number of human cases and the level of mosquito infection (MIR) vary across time and space and are related to numerous abiotic and biotic forces, ranging from differences in microclimates to socio-demographic factors. Because the interactions among these multiple factors affect the locally variable risk of WNV illness, it has been especially difficult to model human disease risk across varying spatial and temporal scales. Cook and DuPage Counties, comprising the city of Chicago and surrounding suburbs, are among the areas hardest hit by WNV in the United States. Despite active mosquito control efforts, there is consistent annual WNV presence, resulting in more than 285 confirmed WNV human cases and 20 deaths in the past 5 years in Cook County alone. Methods: A previous WNV model for the greater Chicago area identified the fifty-five most high and low risk study areas in the Northwest Mosquito Abatement District (NWMAD), an enclave ¼ the size of the previous study area. In these locations, human WNV risk was stratified by strength of predictive success, as indicated by differences in studentized residuals. Within these areas, an additional two-years of field collections and data processing was added to a 10-year WNV dataset and assessed by an ultra-fine-scale multivariate logistic regression model.Results: Multivariate statistical approaches revealed that this ultra-fine-scale model resulted in fewer explanatory variables while improving upon the fit of the existing model. Beyond mosquito infection rates and climatic factors, efforts to acquire additional covariates only slightly improve model predictive performance. Conclusions: These results suggest human WNV illness in the Chicago area may be associated with fewer, but increasingly critical, key variables at finer scales. Given limited resources, this study suggests a large variation in the significance to model performance, and provides guidance in covariate selection for optimal WNV human illness modeling.