Efficiency of Geographically Weighted Regression in Modeling Human Leptospirosis Based on Environmental Factors in Gilan Province, Iran

Abstract

It is of little debate that Leptospirosis is verified as the most important zoonosis disease in tropical and humid regions. In North of Iran, maximum reports have been dedicated to Gilan province and it is considered as an endemic problem there. Therefore, modeling or researching about different aspects of it seems indispensable. Hence, this paper investigated various models of Geographically Weighted Regression (GWR) approach and impacts of seven environmental variables on modelling leptospirosis in Gilan. Accordingly, counts of patients were considered as dependent variable during 2009-2011 at village level and environmental variables were utilized as independent variables in the modelling. In addition, performance of two Kernels (Fixed and Adaptive), two Weighting Functions (Bisquare and Gaussian) and three Bandwidth Selection Criteria (AIC (Akaike Information Criterion), CV (Cross Validation) and BIC (Bayesian information criterion)) were compared and assessed in GWR models. Results illustrated: (1) Leptospirosis and effective variables vary locally across the study area (positive and negative); (2) Adaptive kernel in comparison to Fixed kernel, Bisquare weighting function to Gaussian, and also AIC to CV and BIC (due to R 2 and Mean Square Error (MSE) validation criteria); (3) Temperature and humidity were founded as impressive factors (include higher values of coefficients); Finally, contain more reliable results consecutively. However, the provided distribution maps asserted that central villages of Gilan not only are more predisposed to leptospirosis prevalence, but also prevention programs should focus on these regions more than others.

Spatial Variation in the Relationship between Leptospirosis Incidence and Climate Determinants in Sarawak (Malaysia)

“…The Akaike Information Criterion (AICc) value, which assesses the relative quality of models given trade-offs between model fit and model complexity [15], showed that the model with temperature seasonality (TS) was more efficient (AIC: 31743.42) in explaining the geographical distribution of leptospirosis transmission risk. This result corroborates findings from other studies showing that geographically weighted regression can offer improvements and additional insights over standard non-spatial regression models for eco-epidemiological studies of leptospirosis [13,15,16,17,18].…”

“…This, in turn, has fostered the utilization of GIS analytics and geospatial statistics for environmental analyses of infectious diseases [14]. Previous studies have adopted GIS analysis tools in the study of ecological models to explore and analyse spatial variations in relationships between local environmental factors and the occurrences of leptospirosis [13,15,16,17,18].…”

Spatial Variation in the Relationship between Leptospirosis Incidence and Climate Determinants in Sarawak (Malaysia)

“…The Akaike Information Criterion (AICc) value, which assesses the relative quality of models given trade-offs between model fit and model complexity [15], showed that the model with temperature seasonality (TS) was more efficient (AIC: 31743.42) in explaining the geographical distribution of leptospirosis transmission risk. This result corroborates findings from other studies showing that geographically weighted regression can offer improvements and additional insights over standard non-spatial regression models for eco-epidemiological studies of leptospirosis 21 [13,15,16,17,18].…”

Beyond mapping: a case for geospatial analytics in humanitarian health

“…The Global Epidemic and Mobility Model, validated by empirical surveillance during the 2009 H1N1 pandemic, uses a spatial layer of population distribution coupled with a transportation network layer and a flexible manipulated model of disease transmission to predict epidemic spread that is accurate against a gold standard [37]. Various geographically weighted regression models can now tell us where to deploy leptospirosis prevention programs [38], for example, with greater spatial resolution and resource efficiency. Scan statistics, hot spot analysis and Poisson kriging (forms of cluster analysis and interpolation, respectively) can highlight incidence clusters of cardiac arrest for intervention programs ranging from bystander CPR trainings to AED placement [39, 40].…”