A Study of Malaria Susceptibility Mapping Using Statistical Methods with GIS

Abstract

Developments in the area of geographical information systems (GISs) can offer new ways to symbolize epidemiological data spatially. In this chapter, three statistical methods i.e. Multiple Linear Regression, Information Value (Infoval) and Heuristic Method are used to develop malaria susceptibility index (MSI) and malaria susceptibility zonation (MSZ) through GIS and remote sensing. Village-wise malaria location data were collected from each primary health centre (PHC) and then the locations were identified using GPS. Malaria influencing data layers like rainfall, temperature, population density, distance to river, distance to roads, distance to health facilities, distance to ponds, NDVI, land use. are very well described in this study though GIs and remote sensing. These layers are used to produce the malaria-susceptibility model map. Comparison of statistical methods to select optimum model for MSZ by malaria density method (Qs) is also calculated. The malaria investigation is also completed using the information value, multiple linear regressions, and heuristic models, and the analysis outcomes are tested using the malaria locations for the study area. The verification method is achieved by Area under Curve (AUC).