Pemodelan Jumlah Kasus Tuberkulosis Paru di Indonesia dengan Geographically Weighted Negative Binomial Regression

Abstrak

Geographically Weighted Negative Binomial Regression (GWNBR) model is the development of Negative Binomial Regression (NBR) model applied to spatial data. The parameter estimation of GWNBR model is performed at each observation location using spatial weighting. The purpose of this study is to determine the GWNBR model of the number of pulmonary tuberculosis cases in Indonesia in 2021 and determine the factors that influence pulmonary tuberculosis cases in Indonesia in 2021. The research data are secondary data obtained from the Indonesian Ministry of Health and Indonesian Central Agency on Statistics. Parameter estimation method is Maximum Likelihood Estimation (MLE). Spatial weighting is calculated by using the Adaptive bi-square weighting function and the optimum bandwidth is determined by using the Cross-Validation (CV). The research results showed that the exact Maximum Likelihood (ML) estimator could not be obtained analytically and the approximation of ML estimator was obtained by using the Newton-Raphson iterative method. Based on the results of the parameter testing of GWNBR model, it was concluded that the factors affecting the number of tuberculosis cases were local and varied in 34 provinces. The factor affecting locally are population density, the percentage of districts/cities implementing GERMAS, and number of hospitals.