Abstract
BackgroundThe primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity.MethodsWe examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model.ResultsLSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R2 = 0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R2 = 0.71).ConclusionsThe spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.
Highlights
West Nile Virus (WNV) is a vector-borne disease that was first detected in the United States in 1999 (Nash et al, 2001)
least squares regression (LSR) modeling identified land surface temperature (VIF = 1.046), stream density (VIF = 1.177), and road density (VIF = 1.143) as statistically significant (p < 0.05) variables related to WNV risk: WNV risk = −75.87 + 595.60(RD) + 1.89(LST ) − 146.89(SD)
A global model can be used to examine the relationship between disease risk and potential explanatory factors which are based on the assumption that the relationship is a stationary spatial process (Miller, 2012)
Summary
West Nile Virus (WNV) is a vector-borne disease that was first detected in the United States in 1999 (Nash et al, 2001). Because least squares regression methods do not account for spatial autocorrelation and nonstationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial nonstationarity. We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR) Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies
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