Analisis Jumlah Kasus Malaria Di Wilayah Sumatera Menggunakan Geographically Weighted Zero-Inflated Poisson Regression (Gwzipr)

Abstract

A method that can be used if there is a spatial factor and if overdispersion happens in a count data is Geographically Weighted Zero-Inflated Poisson Regression (GWZIPR). This research aimed to analyze the number of malaria cases in every regency/city of Sumatra Land using the GWZIPR method and distribution mapping of factors affecting the number of malaria cases in Sumatra Land. Data involved in this research was the number of malaria cases as the response variable and the predictor variable as a percentage of households that have access to proper sanitation, a percentage of households that have access to proper water resources, and a percentage of the number of public health centers. The results were for each area which had distinctive models based on significant variables. The distribution mapping of factors affecting the number of malaria cases in every regency/city was commonly divided into three groups based on significant variables on ln and logit models. The mapping did not shape a spreading pattern or each regency/city in that group because the geographical locations were close to each other. GWZIPR method in this research was better than the ZIP Regression method because it produced the least AIC value.