Geo-statistical dengue risk model using GIS techniques to identify the risk prone areas by linking rainfall and population density factors in Sri Lanka

Abstract

Frequent   dengue outbreaks is one of the main health related problems in Sri Lanka. The biggest outbreak occurred in 2014 with 47,246 dengue cases identified.  An effective analysis of the epidemic is a vital part in controlling the outbreak. There is an uncertainty in identification of the relationship of dengue outbreak and influencing factors such as rainfall and population density. Hence, a careful study of these factors is needed. Ordinary Least Square (OLS) regression was first applied to find its suitability in identification of the linear relationship. OLS analysis conducted under this study revealed OLS is not a good method to model the relationship between dengue incidence and influencing factors. Then Geographically Weighted Regression (GWR) analysis was conducted and it outran OLS in modeling the relationship. For explanatory variables rainfall and population density, OLS can only explain 33.2% of the variance of dengue incidence while GWR can explain 56.3% of the same. GWR can identify the spatially non-stationary behavior of influencing factors on dengue incidence. These analyses revealed the influence of rainfall and population density is location dependent and hence need local analysis over conventional global analysis. All 25 districts in Sri Lanka were selected as the study area of this study. Rainfall and temperature data were prepared by applying pre-processing on data obtained from GSMaP remote sensing data archive. Dengue incidence data was obtained from Epidemiology Unit of Sri Lanka. The geo statistical risk model generated can be used to identify high risk areas in Sri Lanka. The high risk area map can be used to cater dengue control programs to effectively address the dengue epidemic.