Modeling the Effect of Population Density and Some Related Factors on Covid-19 Pandemic in Nigeria: An Application of Count Data Regression

Abstract

Aim: Nigeria's population density and other factors like confirmed, admitted, and discharged cases have adversely impacted health behaviors and the management of the COVID-19 pandemic. This study aims to investigate how population, population density, confirmed, admitted, and discharged cases affect the prevalence of the COVID-19 pandemic in the 36 states of Nigeria, including the FCT. Method: The number of COVID-19-related deaths, confirmed, admitted, and discharged individuals, from June 20, 2021, to December 31, 2022, were extracted from the Nigeria Centre for Disease Control (NCDC) online database, while data set on the Nigeria population and density were collected from Nigeria’s National Population Commission (NPC) website. Three count data regression techniques; Poisson, Negative Binomial, and Generalized Poisson Regression models were employed to analyze these count data. Result: It was found that the number of admitted patients has a significant negative impact on COVID-19, whereas the number of confirmed laboratory COVID-19 cases has a significant positive effect on the number of deaths related to COVID-19. Additionally, the result showed that Nigeria's COVID-19 death rate is negatively impacted by discharged cases, population, and population density. Conclusion: It is inferred that the Generalized Poisson Regression model is the most suitable count data regression model for over-dispersion and is the best model for predicting the number of COVID-19-related deaths in Nigeria between June 20, 2021, and December 31, 2022.