IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i21p7744-d433459.html
   My bibliography  Save this article

COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis

Author

Listed:
  • Hend Alrasheed

    (Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia)

  • Alhanoof Althnian

    (Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia)

  • Heba Kurdi

    (Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
    Department of Mechanical Engineering, School of Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA)

  • Heila Al-Mgren

    (Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia)

  • Sulaiman Alharbi

    (Department of Botany and Microbiology, College of Sciences, King Saud University, Riyadh 11451, Saudi Arabia)

Abstract

The novel coronavirus Severe Acute Respiratory Syndrome (SARS)-Coronavirus-2 (CoV-2) has resulted in an ongoing pandemic and has affected over 200 countries around the world. Mathematical epidemic models can be used to predict the course of an epidemic and develop methods for controlling it. As social contact is a key factor in disease spreading, modeling epidemics on contact networks has been increasingly used. In this work, we propose a simulation model for the spread of Coronavirus Disease 2019 (COVID-19) in Saudi Arabia using a network-based epidemic model. We generated a contact network that captures realistic social behaviors and dynamics of individuals in Saudi Arabia. The proposed model was used to evaluate the effectiveness of the control measures employed by the Saudi government, to predict the future dynamics of the disease in Saudi Arabia according to different scenarios, and to investigate multiple vaccination strategies. Our results suggest that Saudi Arabia would have faced a nationwide peak of the outbreak on 21 April 2020 with a total of approximately 26 million infections had it not imposed strict control measures. The results also indicate that social distancing plays a crucial role in determining the future local dynamics of the epidemic. Our results also show that the closure of schools and mosques had the maximum impact on delaying the epidemic peak and slowing down the infection rate. If a vaccine does not become available and no social distancing is practiced from 10 June 2020, our predictions suggest that the epidemic will end in Saudi Arabia at the beginning of November with over 13 million infected individuals, and it may take only 15 days to end the epidemic after 70% of the population receive a vaccine.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:21:p:7744-:d:433459
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/21/7744/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/21/7744/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Chao Chen & Jamie Twycross & Jonathan M Garibaldi, 2017. "A new accuracy measure based on bounded relative error for time series forecasting," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-23, March.
    3. 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.
    4. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
    5. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    6. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    7. Rshood Al-Khraif & Asharaf Abdul Salam & Mohd Fadzil Abdul Rashid, 2020. "Family Demographic Transition in Saudi Arabia: Emerging Issues and Concerns," SAGE Open, , vol. 10(1), pages 21582440209, March.
    8. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
    9. Stephen Eubank & Hasan Guclu & V. S. Anil Kumar & Madhav V. Marathe & Aravind Srinivasan & Zoltán Toroczkai & Nan Wang, 2004. "Modelling disease outbreaks in realistic urban social networks," Nature, Nature, vol. 429(6988), pages 180-184, May.
    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. Nadia Yusuf & Lamia Saud Shesha, 2021. "Economic Role of Population Density during Pandemics—A Comparative Analysis of Saudi Arabia and China," IJERPH, MDPI, vol. 18(8), pages 1-18, April.
    2. Isra Al-Turaiki & Fahad Almutlaq & Hend Alrasheed & Norah Alballa, 2021. "Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia," IJERPH, MDPI, vol. 18(16), pages 1-19, August.
    3. Das, Saikat & Bose, Indranil & Sarkar, Uttam Kumar, 2023. "Predicting the outbreak of epidemics using a network-based approach," European Journal of Operational Research, Elsevier, vol. 309(2), pages 819-831.
    4. Mostafa Aboulnour Salem & Ali Saleh Alshebami, 2023. "Exploring the Impact of Mobile Exams on Saudi Arabian Students: Unveiling Anxiety and Behavioural Changes across Majors and Gender," Sustainability, MDPI, vol. 15(17), pages 1-18, August.

    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. Wiriya Mahikul & Somkid Kripattanapong & Piya Hanvoravongchai & Aronrag Meeyai & Sopon Iamsirithaworn & Prasert Auewarakul & Wirichada Pan-ngum, 2020. "Contact Mixing Patterns and Population Movement among Migrant Workers in an Urban Setting in Thailand," IJERPH, MDPI, vol. 17(7), pages 1-11, March.
    2. Stefano Guarino & Enrico Mastrostefano & Massimo Bernaschi & Alessandro Celestini & Marco Cianfriglia & Davide Torre & Lena Rebecca Zastrow, 2021. "Inferring Urban Social Networks from Publicly Available Data," Future Internet, MDPI, vol. 13(5), pages 1-45, April.
    3. Jamie Bedson & Laura A. Skrip & Danielle Pedi & Sharon Abramowitz & Simone Carter & Mohamed F. Jalloh & Sebastian Funk & Nina Gobat & Tamara Giles-Vernick & Gerardo Chowell & João Rangel Almeida & Ran, 2021. "A review and agenda for integrated disease models including social and behavioural factors," Nature Human Behaviour, Nature, vol. 5(7), pages 834-846, July.
    4. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    5. Bisin, Alberto & Moro, Andrea, 2022. "Spatial‐SIR with network structure and behavior: Lockdown rules and the Lucas critique," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 370-388.
    6. Alberto Bisin & Andrea Moro, 2020. "Learning Epidemiology by Doing: The Empirical Implications of a Spatial-SIR Model with Behavioral Responses," NBER Working Papers 27590, National Bureau of Economic Research, Inc.
    7. Xiao, Yao & Yang, Mofeng & Zhu, Zheng & Yang, Hai & Zhang, Lei & Ghader, Sepehr, 2021. "Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach," Transport Policy, Elsevier, vol. 109(C), pages 12-23.
    8. Christopher Bronk Ramsey, 2020. "Human agency and infection rates: Implications for social distancing during epidemics," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-17, December.
    9. Per Block & Marion Hoffman & Isabel J. Raabe & Jennifer Beam Dowd & Charles Rahal & Ridhi Kashyap & Melinda C. Mills, 2020. "Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world," Nature Human Behaviour, Nature, vol. 4(6), pages 588-596, June.
    10. Brotherhood, Luiz & Kircher, Philipp & Santos, Cezar & Tertilt, Michèle, 2023. "Optimal Age-based Policies for Pandemics: An Economic Analysis of Covid-19 and Beyond," IDB Publications (Working Papers) 13295, Inter-American Development Bank.
    11. Rakowski, Franciszek & Gruziel, Magdalena & Bieniasz-Krzywiec, Łukasz & Radomski, Jan P., 2010. "Influenza epidemic spread simulation for Poland — a large scale, individual based model study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3149-3165.
    12. Bisin, Alberto & Moro, Andrea, 2022. "JUE insight: Learning epidemiology by doing: The empirical implications of a Spatial-SIR model with behavioral responses," Journal of Urban Economics, Elsevier, vol. 127(C).
    13. Janiak, Alexandre & Machado, Caio & Turén, Javier, 2021. "Covid-19 contagion, economic activity and business reopening protocols," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 264-284.
    14. Brotherhood, Luiz & Kircher, Philipp & Santos, Cezar & Tertilt, Michele, 2024. "Optimal Age-based Policies for Pandemics: An Economic Analysis of Covid-19 and Beyond," LIDAM Discussion Papers CORE 2024012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Wenting Yang & Jiantong Zhang & Ruolin Ma, 2020. "The Prediction of Infectious Diseases: A Bibliometric Analysis," IJERPH, MDPI, vol. 17(17), pages 1-19, August.
    16. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    17. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    18. Zhangbo Yang & Jiahao Zhang & Shanxing Gao & Hui Wang, 2022. "Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China," IJERPH, MDPI, vol. 19(2), pages 1-17, January.
    19. Mirjam Kretzschmar & Jacco Wallinga, 2007. "Editorial," Mathematical Population Studies, Taylor & Francis Journals, vol. 14(4), pages 203-209, November.
    20. Michele Bellingeri & Daniele Bevacqua & Francesco Scotognella & Davide Cassi, 2024. "The Critical Role of Networks to Describe Disease Spreading Dynamics in Social Systems: A Perspective," Mathematics, MDPI, vol. 12(6), pages 1-11, March.

    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:jijerp:v:17:y:2020:i:21:p:7744-:d:433459. 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.