Modeling COVID-19 Cases in Nigeria Using Some Selected Count Data Regression Models

Samuel Olorunfemi Adams,Samuel Olayemi Olanrewaju,Haruna Umar Yahaya,Rafiu Olayinka Akano,Muhammad Ardo Bamanga

doi:10.32861/ijhms.64.64.73

Samuel Olorunfemi Adams, Samuel Olayemi Olanrewaju + Show 3 more

Open Access

https://doi.org/10.32861/ijhms.64.64.73

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Abstract

COVID-19 is currently threatening countries in the world. Presently in Nigeria, there are about 29,286 confirmed cases, 11,828 discharged and 654 deaths as of 6th July 2020. It is against this background that this study was targeted at modeling daily cases of COVID-19’s deaths in Nigeria using count regression models like; Poisson Regression (PR), Negative Binomial Regression (NBR) and Generalized Poisson Regression (GPR) model. The study aim at fitting an appropriate count Regression model to the confirmed, active and critical cases of COVID-19 in Nigeria after 118 days. The data for the study was extracted from the daily COVID-19 cases update released by the Nigeria Centre for Disease Control (NCDC) online database from February 28th, 2020 – 6th, July 2020. The extracted data were used in the simulation of Poisson, Negative Binomial, and Generalized Poisson Regression model with a program written in STATA version 14 and fitted to the data at a 5% significance level. The best model was selected based on the values of -2logL, AIC, and BIC selection test/criteria. The results obtained from the analysis revealed that the Poisson regression could not capture over-dispersion, so other forms of Poisson Regression models such as the Negative Binomial Regression and Generalized Poisson Regression were used in the estimation. Of the three count Regression models, Generalized Poisson Regression was the best model for fitting daily cumulative confirmed, active and critical COVID-19 cases in Nigeria when overdispersion is present in the predictors because it had the least -2log-Likelihood, AIC, and BIC. It was also discovered that active and critical cases have a positive and significant effect on the number of COVID-19 related deaths in Nigeria.

Highlights

COVID-19 pandemic is the defining global health crisis of our time and the greatest challenge we have faced since World War Two
The results obtained from the analysis revealed that the Poisson regression could not capture over-dispersion, so other forms of Poisson Regression models such as the Negative Binomial Regression and Generalized Poisson Regression were used in the estimation
The cumulative COVID-19 deaths, active, critical, and confirmed cases were fitted with a family of count models such as Poisson Regression, Negative Binomial Regression, and Generalized Negative Binomial Regression).The data spanned from the 28th of February 2020 to 6th of July 2020

Summary

Introduction

COVID-19 pandemic is the defining global health crisis of our time and the greatest challenge we have faced since World War Two. Since its emergence in China late last year, the virus has spread to every continent except Antarctica. Countries are racing to slow the spread of the virus by testing and treating patients, carrying out contact tracing, limiting travel, quarantining citizens, and canceling large gatherings such as sporting events, concerts, and schools. The pandemic is moving like a wave, one that may yet crash on those least able to cope. COVID-19 is much more than a health crisis because it is stressing every one of the countries it touches, it has the potential to create devastating social, economic, and political crises that will leave deep scars [1]

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