Pemodelan Pneumonia Berat Menggunakan Regresi Zero Inflated Negative Binomial di Gorontalo

Abstract

In certain cases, the response variable has an excess zero that causes overdispersion. Therefore, to overcome overdispersion because excess zero Zero-inflated negative binomial regression can be used. The purpose of this study is to apply Zero inflated negative binomial regression to model the case of severe pneumonia in Bone Bolango and on the city of Gorontalo. Based on the result of ZINB Regression, it was found that there are 3 (three) factors that are significant that is ASI, Vitamin A, and low birth weight.

Comparing Gaussian Kernel and Quadratic Spline of Nonparametric Regression in Modeling Infectious Diseases

“…Pneumonia (P), the leading cause of death for children under five each year, is a viral, bacterial, or fungal respiratory tract infection that attacks lung tissues [48], [49]. Previous research found that low birth weight was associated with severe pneumonia and increased mortality risk for children under five [50]- [52].…”