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Application of multiple linear regression model through gis and remote sensing for malaria mapping in varanasi district, india

Praveen Kumar Rai

Background: The production of malaria maps relies on modeling to predict the risk for most of the area, with actual observations of malaria prevalence usually only known at a limited number of specific locations. However, the estimation is complicated by the fact that there is often local variation of risk that cannot easily be accounted for by the known variables. An attempt has to be made for Varanasi district to evaluate status of Malaria disease and to develop a model, by which malaria prone zones were predicted by five classes of relative malaria susceptibility i.e. Very Low, Low, Moderate, High, and Very High categories. Methodology: Multiple Linear regression models were built for malaria cases reported in study area, as the dependent variable and various time based groupings of average temperature, rainfall and NDVI data as the independent variables. GIS is be used to investigate associations between such variables and the distribution of the different species responsible for malaria transmission. Accurate prediction of risk is dependent on knowledge of a number of variables i.e Land Use, NDVI, climatic factors, distance to location of existing government health centers, population, distance to ponds, streams and roads etc. that are related to malaria transmission. Climatic factors, particularly rainfall, temperature and relative humidity are known to have a strong influence on the biology of mosquitoes. To produce malaria susceptibility map in this method, the amounts of quantitative and qualitative variables based on sampling of 50×50 networks in form of a 38622×9 matrix have been transferred from GIS software (ILWIS 3.4 and ARC GIS-9.3) into statistical software (SPSS). Results: Percentage of malaria area is very much related to distance to health facilities. It is found that, 4.77% of malaria area is belonging to 0-1000 m buffer distance to health facilities and 24.10% of malaria area comes in 6000-10000 m buffer distance. As the distance to health facilities increases, malaria area is also increasing.