Geographically Weighted Logistic Regression Modeling on the Spread of Dengue Fever in Bali Province - Safitri Pratiwi, Luh Putu; I Made Pasek Pradnyana Wijaya (Statistika, 2025)
Abstract. One of the statistical methods that can be used for data analysis by taking spatial factors into account is Geographically Weighted Logistic Regression (GWLR). GWLR is a model where there are parameters that are influenced by location (Geographically varying coefficient) and parameters that are not influenced by location. Research continues to be carried out to understand the factors that influence the spread of dengue fever and to develop more effective strategies for controlling this disease as well as the best model for data on the spread of dengue fever in Bali Province based on AIC values. The variables used are the response variable (Y) which is the case of dengue fever. The variables studied are the number of dengue fever sufferers in 2022 as the response variable and the predictor variables are number of drinking water facilities (X1), population density (X2), number of doctors (X3), number of health workers, namely nurses (X4), and number of adequate sanitation facilities (X5). The GWLR model is better used to analyze dengue fever data in Bali compared to the Logistic Regression model seen from the low AIC value of 29.4481. The variable number of doctors (X3) is the only variable that significantly affects the probability of DHF occurrence in Bali Province at α = 10%. The positive coefficient for β3 indicates that an increase in the number of doctors increases the probability of DHF occurrence in the region.