IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i9p4888-d544156.html
   My bibliography  Save this article

A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data

Author

Listed:
  • Ahmad B. Hassanat

    (Community College, University of Tabuk, Tabuk 71491, Saudi Arabia
    Computer Science Department, Mutah University, Karak 61711, Jordan)

  • Sami Mnasri

    (Community College, University of Tabuk, Tabuk 71491, Saudi Arabia
    CNRS-IRIT (RMESS), University of Toulouse, 31000 Toulouse, France)

  • Mohammed A. Aseeri

    (Communication and Information Technology Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia)

  • Khaled Alhazmi

    (Communication and Information Technology Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia)

  • Omar Cheikhrouhou

    (College of CIT, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Ghada Altarawneh

    (Accounting Department, Faculty of Business Administration, University of Tabuk, Tabuk 71491, Saudi Arabia
    Accounting Department, Mutah University, Karak 61711, Jordan)

  • Malek Alrashidi

    (Community College, University of Tabuk, Tabuk 71491, Saudi Arabia)

  • Ahmad S. Tarawneh

    (Department of Algorithms and Their Applications, Eötvös Loránd University, H-1053 Budapest, Hungary)

  • Khalid S. Almohammadi

    (Community College, University of Tabuk, Tabuk 71491, Saudi Arabia)

  • Hani Almoamari

    (Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia)

Abstract

The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the pandemic evolution in two major cities in KSA, namely, Riyadh (the capital city) and Jeddah (the second-largest city). Consequently, this study estimates and predicts the number of cases infected with COVID-19 in the upcoming months. The major advantage of this model is that it is based on real data for KSA, which makes it more realistic. Furthermore, this paper examines the parameters used to understand better and more accurately predict the shape of the infection curve, particularly in KSA. The obtained results show the importance of several parameters in reducing the pandemic spread: the infection rate, the social distance, and the walking distance of individuals. Through this work, we try to raise the awareness of the public and officials about the seriousness of future pandemic waves. In addition, we analyze the current data of the infected cases in KSA using a novel Gaussian curve fitting method. The results show that the expected pandemic curve is flattening, which is recorded in real data of infection. We also propose a new method to predict the new cases. The experimental results on KSA’s updated cases reveal that the proposed method outperforms some current prediction techniques, and therefore, it is more efficient in fighting possible future pandemics.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4888-:d:544156
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/9/4888/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/9/4888/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chudik, Alexander & Mohaddes, Kamiar & Pesaran, M. Hashem & Raissi, Mehdi & Rebucci, Alessandro, 2021. "A counterfactual economic analysis of Covid-19 using a threshold augmented multi-country model," Journal of International Money and Finance, Elsevier, vol. 119(C).
    2. Robert G. Brown & Richard F. Meyer, 1961. "The Fundamental Theorem of Exponential Smoothing," Operations Research, INFORMS, vol. 9(5), pages 673-685, October.
    3. Sheryl L. Chang & Nathan Harding & Cameron Zachreson & Oliver M. Cliff & Mikhail Prokopenko, 2020. "Modelling transmission and control of the COVID-19 pandemic in Australia," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    4. Tim Althoff & Rok Sosič & Jennifer L. Hicks & Abby C. King & Scott L. Delp & Jure Leskovec, 2017. "Large-scale physical activity data reveal worldwide activity inequality," Nature, Nature, vol. 547(7663), pages 336-339, July.
    5. Ivanov, Dmitry, 2020. "Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gallo, Edoardo & Barak, Darija & Langtry, Alastair, 2023. "Social distancing in networks: A web-based interactive experiment," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 107(C).
    2. Fang, Da & Guo, Yan, 2022. "Flow of goods to the shock of COVID-19 and toll-free highway policy: Evidence from logistics data in China," Research in Transportation Economics, Elsevier, vol. 93(C).
    3. Mohamed Elhefnawy & Ahmed Ragab & Mohamed-Salah Ouali, 2023. "Polygon generation and video-to-video translation for time-series prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 261-279, January.
    4. Jeong-Hui Park & Eunhye Yoo & Youngdeok Kim & Jung-Min Lee, 2021. "What Happened Pre- and during COVID-19 in South Korea? Comparing Physical Activity, Sleep Time, and Body Weight Status," IJERPH, MDPI, vol. 18(11), pages 1-13, May.
    5. Reham Alhindawi & Yousef Abu Nahleh & Arun Kumar & Nirajan Shiwakoti, 2020. "Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model," Sustainability, MDPI, vol. 12(21), pages 1-18, November.
    6. Paul, Ananna & Shukla, Nagesh & Trianni, Andrea, 2023. "Modelling supply chain sustainability challenges in the food processing sector amid the COVID-19 outbreak," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    7. Papanagnou, Christos & Seiler, Andreas & Spanaki, Konstantina & Papadopoulos, Thanos & Bourlakis, Michael, 2022. "Data-driven digital transformation for emergency situations: The case of the UK retail sector," International Journal of Production Economics, Elsevier, vol. 250(C).
    8. Chumasande Lalendle & Leila Goedhals-Gerber & Joubert van Eeden, 2021. "A Monitoring and Evaluation Sustainability Framework for Road Freight Transporters in South Africa," Sustainability, MDPI, vol. 13(14), pages 1-22, July.
    9. Tang, Lianhua & Li, Yantong & Bai, Danyu & Liu, Tao & Coelho, Leandro C., 2022. "Bi-objective optimization for a multi-period COVID-19 vaccination planning problem," Omega, Elsevier, vol. 110(C).
    10. Zhi, Bangdong & Wang, Xiaojun & Xu, Fangming, 2022. "Managing inventory financing in a volatile market: A novel data-driven copula model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    11. Li, Haojie & Zhang, Yingheng & Zhu, Manman & Ren, Gang, 2021. "Impacts of COVID-19 on the usage of public bicycle share in London," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 140-155.
    12. Yu, Wantao & Zhao, Gen & Liu, Qi & Song, Yongtao, 2021. "Role of big data analytics capability in developing integrated hospital supply chains and operational flexibility: An organizational information processing theory perspective," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    13. Rozhkov, Maxim & Ivanov, Dmitry & Blackhurst, Jennifer & Nair, Anand, 2022. "Adapting supply chain operations in anticipation of and during the COVID-19 pandemic," Omega, Elsevier, vol. 110(C).
    14. Marić, Josip & Opazo-Basáez, Marco & Vlačić, Božidar & Dabić, Marina, 2023. "Innovation management of three-dimensional printing (3DP) technology: Disclosing insights from existing literature and determining future research streams," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    15. Chervenkova, Tanya & Ivanov, Dmitry, 2023. "Adaptation strategies for building supply chain viability: A case study analysis of the global automotive industry re-purposing during the COVID-19 pandemic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    16. Kochaniak, Katarzyna & Ulman, Paweł & Zajkowski, Robert, 2023. "Effectiveness of COVID-19 state aid for microenterprises in Poland," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 483-497.
    17. Afees A. Salisu & Idris A. Adediran & Rangan Gupta, 2021. "A Note on the COVID-19 Shock and Real GDP in Emerging Economies: A Counterfactual Analysis from the Threshold-Augmented Global Vector Autoregressive Model," Working Papers 202149, University of Pretoria, Department of Economics.
    18. Hinterlang, Natascha & Moyen, Stephane & Röhe, Oke & Stähler, Nikolai, 2023. "Gauging the effects of the German COVID-19 fiscal stimulus package," European Economic Review, Elsevier, vol. 154(C).
    19. LU, Yi & Zhao, Jianting & Wu, Xueying & Lo, Siu Ming, 2020. "Escaping to nature in pandemic: a natural experiment of COVID-19 in Asian cities," SocArXiv rq8sn, Center for Open Science.
    20. Lazebnik, Teddy & Shami, Labib & Bunimovich-Mendrazitsky, Svetlana, 2023. "Intervention policy influence on the effect of epidemiological crisis on industry-level production through input–output networks," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4888-:d:544156. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.