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Modelling and Forecasting of Covid-19 Using Periodical ARIMA Models

Author

Listed:
  • Amaal Elsayed Mubarak

    (Damietta University)

  • Ehab Mohamed Almetwally

    (Imam Mohammad Ibn Saud Islamic University (IMSIU)
    Delta University for Science and Technology)

Abstract

Corona virus (Covid-19) is a great danger for whole world. World health organization (WHO) considered it an epidemic. Data collection was based on the reports of World health organization for Covid-19 in Egypt. The problem of this study is to describe actual behavior of the virus using an appropriate statistical model. As WHO stated, Covid-19 behaves in the form of waves, therefore we thought that we should pay attention to seasonal and periodical models when identifying an appropriate model for this virus. The aim of this article is to introduce and study Periodical Autoregressive integrated Moving Average (PARIMA) models and compare them with the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to find optimal or approximately optimal model helps to predict the epidemiological behavior of the prevalence and so find reliable future forecasts of the number of Covid-19 injuries in Egypt. A numerical study using real data analysis is performed to establish an appropriate PARIMA model. The results supported the reliance of PAR (7) odel and its use for the purpose of forecasting. Extensive comparisons have been made between the estimated PARIMA model and some other advanced time series models. The forecasts obtained from the estimated PARIMA model were compared with the forecasts obtained from ARIMA (2, 2, 2) and SARIMA (1, 2, 1), (0, 0 ,1) models.

Suggested Citation

  • Amaal Elsayed Mubarak & Ehab Mohamed Almetwally, 2024. "Modelling and Forecasting of Covid-19 Using Periodical ARIMA Models," Annals of Data Science, Springer, vol. 11(4), pages 1483-1502, August.
  • Handle: RePEc:spr:aodasc:v:11:y:2024:i:4:d:10.1007_s40745-023-00501-4
    DOI: 10.1007/s40745-023-00501-4
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    References listed on IDEAS

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    1. Eugen Ursu & Pierre Duchesne, 2009. "On modelling and diagnostic checking of vector periodic autoregressive time series models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 70-96, January.
    2. Yonas Gebeyehu Tesfaye & Paul L. Anderson & Mark M. Meerschaert, 2011. "Asymptotic results for Fourier‐PARMA time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(2), pages 157-174, March.
    3. Mohamed Bentarzi & Marc Hallin, 1994. "On The Invertibility Of Periodic Moving‐Average Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(3), pages 263-268, May.
    4. Franses, Philip Hans & Paap, Richard, 2004. "Periodic Time Series Models," OUP Catalogue, Oxford University Press, number 9780199242030.
    5. Robert Lund & I. V. Basawa, 2000. "Recursive Prediction and Likelihood Evaluation for Periodic ARMA Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(1), pages 75-93, January.
    6. Rania A. H. Mohamed & Abdulhakim A. Al-Babtain & I. Elbatal & Ehab M. Almetwally & Hisham M. Almongy & Naeem Jan, 2022. "Classical and Bayesian Inference of Marshall-Olkin Extended Gompertz Makeham Model with Modeling of Physics Data," Journal of Mathematics, Hindawi, vol. 2022, pages 1-14, July.
    7. A. I. McLeod, 1994. "Diagnostic Checking Of Periodic Autoregression Models With Application," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(2), pages 221-233, March.
    8. Georgi N. Boshnakov & Bisher M. Iqelan, 2009. "Generation Of Time Series Models With Given Spectral Properties," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(3), pages 349-368, May.
    9. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
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