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Comparison of Active COVID-19 Cases per Population Using Time-Series Models
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Author(s): Sakinat Oluwabukonla Folorunso (Olabisi Onabanjo University, Ago Iwoye, Nigeria), Joseph Bamidele Awotunde (University of Iliorin, Iliorin, Nigeria), Oluwatobi Oluwaseyi Banjo (Olabisi Onabanjo University, Ago Iwoye, Nigeria), Ezekiel Adebayo Ogundepo (Data Science Nigeria, Nigeria) and Nureni Olawale Adeboye (Federal Polytechnic, Ilaro, Nigeria)
Copyright: 2022
Volume: 13
Issue: 2
Pages: 21
Source title:
International Journal of E-Health and Medical Communications (IJEHMC)
Editor(s)-in-Chief: Joel J.P.C. Rodrigues (Senac Faculty of Ceará, Fortaleza-CE, Brazil; Instituto de Telecomunicações, Portugal)
DOI: 10.4018/IJEHMC.20220701.oa6
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Abstract
This research explored the precision of diverse time-series models for COVID-19 epidemic detection in all the thirty-six different states and the Federal Capital Territory (FCT) in Nigeria with the maximum count of daily cumulative of confirmed, recovered and death cases as of 4 November 2020 of COVID-19 and populace of each state. A 14-multi step ahead forecast system for active coronavirus cases was built, analyzed and compared for six (6) different deep learning-stimulated and statistical time-series models using two openly accessible datasets. The results obtained showed that based on RMSE metric, ARIMA model obtained the best values for four of the states (0.002537, 0.001969.12E-058, 5.36E-05 values for Lagos, FCT, Edo and Delta states respectively). While no method is all-encompassing for predicting daily active coronavirus cases for different states in Nigeria, ARIMA model obtains the highest-ranking prediction performance and attained a good position results in other states.
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