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Predicting the COVID-19 Spread Using Compartmental Model and Extreme Value Theory with Application to Egypt and Iraq

In: Trends in Biomathematics: Chaos and Control in Epidemics, Ecosystems, and Cells

Author

Listed:
  • Mahmoud A. Ibrahim

    (University of Szeged, Bolyai Institute
    Mansoura University, Department of Mathematics, Faculty of Science)

  • Amenah Al-Najafi

    (University of Szeged, Bolyai Institute)

  • Attila Dénes

    (University of Szeged, Bolyai Institute)

Abstract

In this chapter, we study and investigate the spread of coronavirus disease 2019 (COVID-19) in Iraq and Egypt by using compartmental, logistic regression, and Gaussian models. We developed a generalized SEIR model for the spread of COVID-19 considering mildly and symptomatically infected. The logistic and Gaussian models were utilized to forecast and predict the number of confirmed cases from both countries. We estimate the parameters that give the best fit to the incidence data, and the results provide severe forecasts for Iraq and Egypt. To provide a forecast of the spread of COVID-19 in Iraq, we present various simulation scenarios for the expected peak and its time by using Gaussian and logistic regression models, and a reasonable concord with officially reported cases was shown by the forecasted cases. Our sensitivity analyses of the basic reproduction number conclude that the most effective way to prevent COVID-19 cases is decreasing the transmission rate. The findings of this chapter could therefore assist Iraqi and Egyptian officials to intervene with the appropriate safety measures to handle the increase of the COVID-19 cases.

Suggested Citation

  • Mahmoud A. Ibrahim & Amenah Al-Najafi & Attila Dénes, 2021. "Predicting the COVID-19 Spread Using Compartmental Model and Extreme Value Theory with Application to Egypt and Iraq," Springer Books, in: Rubem P. Mondaini (ed.), Trends in Biomathematics: Chaos and Control in Epidemics, Ecosystems, and Cells, pages 57-68, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-73241-7_4
    DOI: 10.1007/978-3-030-73241-7_4
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