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Comparison of Active COVID-19 Cases per Population Using Time-Series Models

Author

Listed:
  • 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)

  • Nureni Olawale Adeboye

    (Federal Polytechnic, Ilaro, Nigeria)

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.

Suggested Citation

  • Sakinat Oluwabukonla Folorunso & Joseph Bamidele Awotunde & Oluwatobi Oluwaseyi Banjo & Ezekiel Adebayo Ogundepo & Nureni Olawale Adeboye, 2021. "Comparison of Active COVID-19 Cases per Population Using Time-Series Models," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 13(2), pages 1-21, July.
    • Handle: RePEc:igg:jehmc0:v:13:y:2021:i:2:p:1-21
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