Count Data Modeling Using GAMLSS Approach and Its Application in Dengue Hemorrhagic Fever Cases in East Kalimantan Province, Indonesia

Abstract

Generalized Additive Models for Location, Scale and Shape (GAMLSS) is a robust approach used to model various types and characteristics of data. Therefore, this research aims to model the count data using the GAMLSS approach through Poisson Regression (PR), Poisson Inverse Gaussian Regression (PIGR), and Negative Binomial Regression (NBR). PIGR and NBR are the best models compared to PR based on their application to modeling the number of dengue hemorrhagic fever (DHF) cases in East Kalimantan Province, Indonesia, in 2019. Furthermore, both models produced varying results on the factors with a significant effect on DHF. Only one factor of the PIGR model, namely altitude, significantly affected these cases. Meanwhile, the NBR model produced three factors that affected the number of dengue cases: altitude, population density, and health workers.