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Modeling of West Nile virus risk based on environmental parameters

Author: Abhishek K. Kala1, Chetan Tiwari2, Armin R. Mikler3, Samuel F. Atkinson​

Published: March 28, 2017

West Nile Virus (WNV) is a vector-borne disease that was first detected in the United States in 1999 (Nash et al., 2001). Within a few years the virus had spread across the North American continent (Hayes et al., 2005). WNV has had important environmental and human impacts, including a decline in numerous bird species (CDC) and increased morbidity and mortality among humans. This has also resulted in increased economic burdens due to initial acute health care needs of infected individuals and subsequent long-term costs associates with infection, estimated at approximately $56 million per year between 1999 and 2012 (Barrett, 2014). Because that study indicated how difficult predicting and planning for WNV outbreaks was, we became interested in developing a spatially explicit model using environmental factors in an attempt to improve WNV risk predictions.

Background. The 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 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.

Methods. 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 goal was to determine which approach yielded a better predictive model.

Results. LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R 2 = 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 R 2 = 0.71).

Conclusions. 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.

Figures:

Table 1: Environmental Conditions related to WNV Risk Table 2: Data Sources [https://www.semanticscholar.org/paper/A-comparison-of-least-squares-regression-and-of-on-Kala-Tiwari/24e97fc0610548ff5c7958e57fc2ec8b0766e8f8/figure/1 Figure 1: Trendline plot for global LSRmodel (model: y = 0.6591x + 10.563; r2 = 0.66), dashed line ideal 1:1 relationship. ] [https://www.semanticscholar.org/paper/A-comparison-of-least-squares-regression-and-of-on-Kala-Tiwari/24e97fc0610548ff5c7958e57fc2ec8b0766e8f8/figure/3 Figure 2 Trendline plot for local GWRmodel (model: y = 0.6911x + 10.259; r2 = 0.75), dashed line ideal 1:1 relationship. ] [https://www.semanticscholar.org/paper/A-comparison-of-least-squares-regression-and-of-on-Kala-Tiwari/24e97fc0610548ff5c7958e57fc2ec8b0766e8f8/figure/4 Figure 3 Spatial distribution of (A) standardized residuals; (B) land surface temperature coefficients; (C) road density coefficients; and (D) stream coefficients. ]

References: https://peerj.com/articles/3070/?td=wk

[https://peerj.com/articles/3070/ Kala et al. (2017), A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters. PeerJ 5:e3070; DOI 10.7717/peerj.3070]