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Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia

Author

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  • Dabiah Alboaneen

    (Computer Science Department, College of Science and Humanities in Jubail, Imam Abdulrahman Bin Faisal University, Jubail P.O. Box 31961, Saudi Arabia)

  • Bernardi Pranggono

    (Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK)

  • Dhahi Alshammari

    (Computer Science and Information Department, College of Computer Science and Engineering, University of Ha’il, Hail 8145, Saudi Arabia)

  • Nourah Alqahtani

    (Computer Science Department, College of Science and Humanities in Jubail, Imam Abdulrahman Bin Faisal University, Jubail P.O. Box 31961, Saudi Arabia)

  • Raja Alyaffer

    (Computer Science Department, College of Science and Humanities in Jubail, Imam Abdulrahman Bin Faisal University, Jubail P.O. Box 31961, Saudi Arabia)

Abstract

The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. Using a real-time data from 2 March 2020 to 15 May 2020 collected from Saudi Ministry of Health, we aimed to give a local prediction of the epidemic in Saudi Arabia. We used two models: the Logistic Growth and the Susceptible-Infected-Recovered for real-time forecasting the confirmed cases of COVID-19 across Saudi Arabia. Our models predicted that the epidemics of COVID-19 will have total cases of 69,000 to 79,000 cases. The simulations also predicted that the outbreak will entering the final-phase by end of June 2020.

Suggested Citation

  • Dabiah Alboaneen & Bernardi Pranggono & Dhahi Alshammari & Nourah Alqahtani & Raja Alyaffer, 2020. "Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia," IJERPH, MDPI, vol. 17(12), pages 1-10, June.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:12:p:4568-:d:376071
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    References listed on IDEAS

    as
    1. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Mohamed Abd Elaziz, 2020. "Optimized Forecasting Method for Weekly Influenza Confirmed Cases," IJERPH, MDPI, vol. 17(10), pages 1-12, May.
    2. Xing-Yi Ge & Jia-Lu Li & Xing-Lou Yang & Aleksei A. Chmura & Guangjian Zhu & Jonathan H. Epstein & Jonna K. Mazet & Ben Hu & Wei Zhang & Cheng Peng & Yu-Ji Zhang & Chu-Ming Luo & Bing Tan & Ning Wang , 2013. "Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor," Nature, Nature, vol. 503(7477), pages 535-538, November.
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    Cited by:

    1. Abdelrahman E. E. Eltoukhy & Ibrahim Abdelfadeel Shaban & Felix T. S. Chan & Mohammad A. M. Abdel-Aal, 2020. "Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations," IJERPH, MDPI, vol. 17(19), pages 1-25, September.
    2. repec:thr:techub:10016:y:2021:i:1:p:618-630 is not listed on IDEAS
    3. Hend Alrasheed & Alhanoof Althnian & Heba Kurdi & Heila Al-Mgren & Sulaiman Alharbi, 2020. "COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis," IJERPH, MDPI, vol. 17(21), pages 1-24, October.

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