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

Samuel Olorunfemi Adams, Muhammad Ardo Bamanga, Samuel Olayemi Olanrewaju, Haruna Umar Yahaya and Rafiu Olayinka Akano
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Samuel Olorunfemi Adams: Department of Statistics, University of Abuja, Abuja, Nigeria
Muhammad Ardo Bamanga: Department of Mathematical Sciences, Kaduna State University, Kaduna, Nigeria
Samuel Olayemi Olanrewaju: Department of Statistics, University of Abuja, Abuja, Nigeria
Haruna Umar Yahaya: Department of Statistics, University of Abuja, Abuja, Nigeria
Rafiu Olayinka Akano: Department of Statistics, University of Abuja, Abuja, Nigeria

International Journal of Healthcare and Medical Sciences, 2020, vol. 6, issue 4, 64-73

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.

Keywords: Akaike information criterion (AIC); Bayesian information criterion (BIC); COVID -19; Negative binomial regression; Poisson regression. (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:arp:ijohms:2020:p:64-73

DOI: 10.32861/ijhms.64.64.73

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