On the Assessment of Trend and Pattern of COVID-19 Infection in Nigeria: Autoregressive Integrated Moving Average (ARIMA) Approach

COVID-19 Infection in Nigeria

  • Omobolaji Yusuf Halid Department of Statistics, Ekiti State University, Ado-Ekiti, Nigeria
  • Bayowa Teniola Babalola Department of Mathematics and Statistics, Kampala International University, Uganda
  • Joshua Sunday Department of Mathematics, The University of Jos, Plateau, Nigeria
  • Samuel Oluwaseun Adejuwon Department of Mathematical and Physical Sciences, Afe Babalola University, Ado-Ekiti-36000, Nigeria
  • Kehinde Abimbola Adigun Department of Mathematical and Physical Sciences, Afe Babalola University, Ado-Ekiti-36000, Nigeria
  • Ojo Femi Ogunboyo Department of Epidemiology and Biostatistics, University of Medical Sciences, Ondo, Nigeria
  • Temitope Olu Ogunlade Department of Mathematics, Ekiti State University, Ado-Ekiti-360001, Nigeria
  • Anthony Opeyemi Ilesanmi Department of Statistics, Ekiti State University, Ado-Ekiti, Nigeria
  • Sunday Emmanuel Fadugba Department of Mathematics, Ekiti State University, Ado-Ekiti-360001, Nigeria
Keywords: COVID-19, infection, recovery, time-series, ARIMA

Abstract

COVID-19 is a deadly infection that causes severe acute respiratory syndrome. Although not particularly spreading rapidly as before due to the introduction of vaccines and other measures, its effect still portends grave danger to human lives in Nigeria and other countries. This study aimed to model and forecast Nigeria's COVID-19 (new) trend of confirmed cases, discharged (recovery) cases and deaths and also to examine the pattern of the infection and survival rate in the face of vaccine introduction. The Box- Jenkins methodology was employed in this study to model and forecast COVID-19 confirmed cases, discharged cases and deaths. The data used for this study was secondary data of weekly confirmed cases, recoveries (discharged) and deaths extracted from the weekly publication of the Nigeria Centre for Disease Control (NCDC). The mean survival rate of COVID-19 was found to be 0.7765 and the three series were found to be stationary after differencing. Also, from an array of candidate models obtained through Autocorrelation Function (ACF) and partial autocorrelation function (PACF) plots, the best-fitted models selected based on minimum Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) were found to be ARIMA (4,1,0), (3,1,0) and (7,3,1) for newly confirmed cases, discharged cases and death, respectively. This implied that these models were adequate for forecasting future rates of infection, recovery and death as further diagnostic tests showed that the ARIMA models were the perfect fit for the three cases (since P>0.05). Finally, a 29-week out of sample forecast showed a steep downward trend in the three cases and in particular a drastic decline to zero of future COVID-19 deaths. Based on these results, it was recommended that existing vaccination strategies should be expanded to achieve near-zero new COVID-19 cases and deaths.

 

Published
2024-07-02