Prediction of Tuberculosis in Iraq Using A ZIPR Model

Afraa A. Hamada^1,*

¹Department of Statistics, College of Administration and Economics, University of Al-Qadisiyah, Iraq

Abstract

In this article, the ZeroInflated Poisson Regression model (ZI-PRM) was used to predict the number of tuberculosis patients by estimating the model using the maximum likelihood method and compared with Poisson regression model (PRM). The results showed that the ZIPRM best represented TB data from PRM. The PRM showed that the importance of some variables, although they were not significant as a cause of the TB data. The ZIP model indicates that there will be more TB cases in 2027 than there were in 2023. These findings point to an improvement in the nation's health status.

Keywords: ZIP Model; Tuberculosis; Estimation; Maximum Likelihood; Prediction