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A Study on Predicting the Outbreak of COVID-19 in the United Arab Emirates: A Monte Carlo Simulation Approach

Author

Listed:
  • Noor Alkhateeb

    (Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

  • Farag Sallabi

    (Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

  • Saad Harous

    (Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates)

  • Mamoun Awad

    (Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

Abstract

According to the World Health Organization updates, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic between 2019 and 2022, with millions of confirmed cases and deaths worldwide. There are various approaches to predicting the suspected, infected, and recovered (SIR) cases with different factual or epidemiological models. Some of the recent approaches to predicting the COVID-19 outbreak have had positive impacts in specific nations. Results show that the SIR model is a significant tool to cast the dynamics and predictions of the COVID-19 outbreak compared to other epidemic models. In this paper, we employ the Monte Carlo simulation to predict the spread of COVID-19 in the United Arab Emirates. We study traditional SIR models in general and focus on a time-dependent SIR model, which has been proven more adaptive and robust in predicting the COVID-19 outbreak. We evaluate the time-dependent SIR model. Then, we implement a Monte Carlo model. The Monte Carlo model uses the parameters extracted from the Time-Dependent SIR Model. The Monte Carlo model exhibited a better prediction accuracy and resembles the data collected from the Ministry of Cabinet Affairs, United Arab Emirates, between April and July 2020.

Suggested Citation

  • Noor Alkhateeb & Farag Sallabi & Saad Harous & Mamoun Awad, 2022. "A Study on Predicting the Outbreak of COVID-19 in the United Arab Emirates: A Monte Carlo Simulation Approach," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4434-:d:983025
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    References listed on IDEAS

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    1. Ahmad B. Hassanat & Sami Mnasri & Mohammed A. Aseeri & Khaled Alhazmi & Omar Cheikhrouhou & Ghada Altarawneh & Malek Alrashidi & Ahmad S. Tarawneh & Khalid S. Almohammadi & Hani Almoamari, 2021. "A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data," Sustainability, MDPI, vol. 13(9), pages 1-22, April.
    2. Das, Dhiraj Kumar & Khajanchi, Subhas & Kar, T.K., 2020. "The impact of the media awareness and optimal strategy on the prevalence of tuberculosis," Applied Mathematics and Computation, Elsevier, vol. 366(C).
    3. Sibel Eker, 2020. "Validity and usefulness of COVID-19 models," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-5, December.
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