MODELING NOVEL COVID-19 PANDEMIC IN NIGERIA USING COUNT DATA REGRESSION MODELS

Abstract

This study aimed to model COVID-19 daily cases in Nigeria, focusing on confirmed, active, critical, recovered, and death cases using count data regression models. Three count data regression models-Poisson regression, Negative Binomial regression, and Generalized Poisson regression were applied to predict COVID-19 related deaths based on the mentioned variables. Secondary data from the Nigeria Centre for Disease Control (NCDC) between February 29, 2020, and October 19, 2020, were used. The study found that Poisson Regression could not handle over-dispersion inherent in the data. Consequently, Negative Binomial Regression and Generalized Poisson Regression were considered, with Generalized Poisson Regression identified as the best model through performance criteria such as -2 log likelihood (-2logL), Akaike information criterion (AIC), and Bayesian information criterion (BIC). The study revealed positive and significant impacts of confirmed, active, and critical cases on COVID-19 related deaths, while recovered cases had a negative effect. Recommendations included increased attention to confirmed, active, and critical cases by relevant authorities to mitigate COVID-19-related deaths in Nigeria.