Geographically weighted negative bivariate binomial regression for modelling the number of dengue diseases and their mortality in East Nusa Tenggara, Indonesia

Abstract

Dengue disease usually occurs in tropical countries. In 2020, East Nusa Tenggara (NTT), Indonesia was the highest number in the mortality of dengue. For that reason, analysis of spread dengue is indispensable. One of the statistical modelling methods to spread dengue and its factors based on spatial analysis is Geographically Weighted Regression (GWR). GWR was developed to Geographically Weighted Poisson Regression (GWPR) and Geographically Weighted Negative Binomial Regression (GWNB) as handling overdispersion. GWNB has improvement analysis into Geographically Weighted Bivariate Negative Binomial regression (GWBNB) model. This paper applied the GWBNB model for the number of dengue cases and their mortality (as dependent variables) based on population density, percentage of poverty, the number of doctors, the number of health facilities, and the percentage of the area that has good sanitation (as independent variables). It resulted from the BNB model, the factors that impacted the dengue’s number and their mortality were poverty, health facilities, and the percentage of areas with good sanitation. In BNB model resulted in 364.4725 of AIC. However, the spatial testing has shown spatial contiguity of dengue’s number and their mortality. As a result of the GWBNB model, shown most of the variables were significant and it established the cluster based on its estimation. Besides, with the GWBNB model, the heterogeneity of spatial can be solved. The accuracy of GWBNB estimation was 73% for predict the number of dengue and their mortality in East Nusa Tenggara, Indonesia. The GWBNB model resulted better than the BNB model based on AIC’s value.