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

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.

Peer Review #3 of "Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus (v0.1)"

“…WNV was first detected in California in 2003 (Reisen, Lothrop et al 2004), and then received national attention for the high rates of the disease during the following two years (Jean, Honarmand et al 2007). Results of WNV vector-borne environmental modeling in California (Kala, Tiwari et al 2017) let to this study of combining socio-economic data with the results of the environmental model using multivariate geovisualization. This study utilized coarse-scale data (county level) of reported cases of WNV human incidence along with infected dead bird counts as the basis for estimating WNV risk.…”

“…A single environmental variable (referred to in this study as "mosquito risk") that represented the results of our earlier GWR model (Kala, Tiwari et al 2017) was derived from analysis of environmental 8 parameters (stream density, surface temperature, surface slope, cultivated land, developed land, road density, vegetation type, evapotranspiration rate).) Mosquito risk was found to be statistically significantly related to annual WNV-infected dead birds sentinel data, averaged for the 2004-2010 Kala, Tiwari et al (2017).Annual WNVinfected dead birds sentinel data has been shown to be useful for estimating human WNV risk by multiple studies (Eidson, Komar et al 2001, Eidson, Miller et al 2001, Guptill, Julian et al 2003, Mostashari, Kulldorff et al 2003, Ruiz, Tedesco et al 2004, Johnson, Eidson et al 2006, Nielsen and Reisen 2007, Patnaik, Juliusson et al 2007, Chaintoutis, Dovas et al 2014. The mosquito risk model resulted in a risk surface with a range of 0 to10.…”

“…In our earlier study (Kala, Tiwari et al 2017) geostatistical and spatial analysis techniques were used to build a spatially explicit model after exploring multiple environmental factors (i.e. factors directly or indirectly related to known mosquito determinants such as vegetation, elevation, evapotranspiration, streams, land use and temperature) that linked mosquito habitat suitability to the number of WNV-positive dead birds, which was used as a surrogate for human WNV risk.…”

“…A geographically weighted regression (GWR) was applied to a statistically significant ordinary least squares (OLS) model to improve model fit from 61% to 71%. The resulting WNV disease risk surface was created using multi-criteria decision analysis approach (detailed process can be referred to our previous paper, Kala, Tiwari et al (2017)). This modeling process was based upon four steps: 1) establishment of the environmental factors, 2) standardization of the factors, 3) establishment of relative weights for each factor, and 4) a Simple Additive Weighting (SAW) method to construct the disease risk surface.…”

The First exploratory spatial distribution analysis of tuberculosis and associated factors in Tonala, Mexico

“…Several studies have used GIS as a strategy for the jurisdictional characterization of TB patterns by evaluating disease concentration case responses and designing strategies for TB control [2,3]. Health departments are developing many diverse approaches that may influence decision makers.…”