Using Geographically Weighted Regression (GWR) to Explore Spatial Varying Relationships of Immature Mosquitoes and Human Densities with the Incidence of Dengue

Abstract

The only way for dengue to spread in the human population is through the human-mosquito-human cycle. Most research in this field discusses the dengue-mosquito or dengue-human relationships over a particular study area, but few have explored the local spatial variations of dengue-mosquito and dengue-human relationships within a study area. This study examined whether spatial heterogeneity exists in these relationships. We used Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to analyze spatial relationships and identify the geographical heterogeneities by using the information of entomology and dengue cases in the cities of Kaohsiung and Fengshan in 2002. Our findings indicate that dengue-mosquito and dengue-human relationships were significantly spatially non-stationary. This means that in some areas higher dengue incidences were associated with higher vector/host densities, but in some areas higher incidences were related to lower vector/host densities. We demonstrated that a GWR model can be used to geographically differentiate the relationships of dengue incidence with immature mosquito and human densities. This study provides more insights into spatial targeting of intervention and control programs against dengue outbreaks within the study areas.

Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed

“…Methods of transportation, particularly for goods, could bridge the gap between natural vector breeding sites in agricultural fields and municipalities outside the habitation zone. Human activities can also increase the promotion of vector breeding or host-vector contact through the establishment of gathering spaces in high vector breeding sites, such as seasonal markets, who then return infected to their urban residences where the disease is reported [39]. Additionally, the EBE had a lower explanation of variance than the other models across all years, which may be attributed to the lack of a population independent variable.…”

“…It should be noted that barren LULC frequently contained stronger results than urban, which might be due to a misclassification of urban, agriculture, or periphery/transitional landscapes [50]. The yearly assessment of this project may also fail to identify seasonal transitional landscapes, land with intermittent use, which may impact the rate of transmission by bringing people to areas with a higher mosquito breeding potential [12,39]. Seasonally transitional land could include agriculture or seasonal markets.…”

“…These methods have been shown to reduce the amount of data used and identify trends in the dataset [8,36]. Other studies have used regression techniques, including the geographically weighted regression and auto-regression, methods for virus modeling [8,21,39], and boosted regression trees [22][23][24][25]. These methods can be computationally intense and difficult to implement.…”

Situation analysis of healthcare service delivery using geographically weighted regression: (A case study of Sironko District, Eastern Uganda)

“…Matthews et al (2012) argued that GWR can potentially make significant contributions to health research, such as allowing researchers to better understand the etiology and spatial processes, offering informative results beyond global models to facilitate place-specific health policy formation, and enabling scholars to explore questions that cannot be answered with traditional (global) analytical models. GWR was preferred to Ordinary Least Squares (OLS) for regression as the latter produces a global model which doesn't accommodate spatial non-stationarity of georeferenced data, (Lin et al, 2011 andPirdavani et al, 2014). OPD2015 was used as the dependent variable for the regression analysis performed in ArcMap 10.2.2 using the GWR tool.…”

An Introduction to Spatial Analysis in Social Science Research

“…These techniques have seen growing use in a Figure 1 Spatial autocorrelation pattern diverse number of disciplines, including demography, epidemiology, political science and sociology (e.g., Kyem, 2000). Researchers have found significant spatial correlations in studies of obesity, crime rates, disease patterns, and the like (e.g., Lin and Wen, 2011). There is also ample reason to believe that these tools are useful in psychology and education.…”