A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

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1 Advanced Environmental Research Institute and Department of Biological Sciences, University of North Texas, Denton, Texas, United States
2 Advanced Environmental Research Institute and Department of Geography and the Environment, University of North Texas, Denton, Texas, United States
3 Advanced Environmental Research Institute and Department of Computer Science and Engineering, University of North Texas, Denton, Texas, United States
DOI
10.7287/peerj.preprints.2673v1
Published
Accepted
Subject Areas
Biogeography, Environmental Sciences, Epidemiology, Infectious Diseases, Public Health
Keywords
emerging infectious diseases, avian impacts, West Nile virus, spatial modeling, geographic information systems (GIS), model comparison
Copyright
© 2016 Kala et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Kala AK, Tiwari C, Mikler AR, Atkinson SF. 2016. A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters. PeerJ Preprints 4:e2673v1

Abstract

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 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.

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 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).

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.

Author Comment

This is a submission to PeerJ for review.