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How simulation modelling can help reduce the impact of COVID-19

Author

Listed:
  • Christine S.M. Currie
  • John W. Fowler
  • Kathy Kotiadis
  • Thomas Monks
  • Bhakti Stephan Onggo
  • Duncan A. Robertson
  • Antuela A. Tako

Abstract

Modelling has been used extensively by all national governments and the World Health Organisation in deciding on the best strategies to pursue in mitigating the effects of COVID-19. Principally these have been epidemiological models aimed at understanding the spread of the disease and the impacts of different interventions. But a global pandemic generates a large number of problems and questions, not just those related to disease transmission, and each requires a different model to find the best solution. In this article we identify challenges resulting from the COVID-19 pandemic and discuss how simulation modelling could help to support decision-makers in making the most informed decisions. Modellers should see the article as a call to arms and decision-makers as a guide to what support is available from the simulation community.

Suggested Citation

  • Christine S.M. Currie & John W. Fowler & Kathy Kotiadis & Thomas Monks & Bhakti Stephan Onggo & Duncan A. Robertson & Antuela A. Tako, 2020. "How simulation modelling can help reduce the impact of COVID-19," Journal of Simulation, Taylor & Francis Journals, vol. 14(2), pages 83-97, April.
    • Handle: RePEc:taf:tjsmxx:v:14:y:2020:i:2:p:83-97
    DOI: 10.1080/17477778.2020.1751570
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    1. Bianco, Débora & Bueno, Adauto & Godinho Filho, Moacir & Latan, Hengky & Miller Devós Ganga, Gilberto & Frank, Alejandro G. & Chiappetta Jabbour, Charbel Jose, 2023. "The role of Industry 4.0 in developing resilience for manufacturing companies during COVID-19," International Journal of Production Economics, Elsevier, vol. 256(C).
    2. Calum MacRury & Nykyta Polituchyi & Paweł Prałat & Kinga Siuta & Przemysław Szufel, 2024. "Optimizing transport frequency in multi-layered urban transportation networks for pandemic prevention," Public Transport, Springer, vol. 16(2), pages 381-418, June.
    3. Maciel M. Queiroz & Dmitry Ivanov & Alexandre Dolgui & Samuel Fosso Wamba, 2022. "Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review," Annals of Operations Research, Springer, vol. 319(1), pages 1159-1196, December.
    4. Seyyed-Mahdi Hosseini-Motlagh & Mohammad Reza Ghatreh Samani & Behnam Karimi, 2023. "Resilient and social health service network design to reduce the effect of COVID-19 outbreak," Annals of Operations Research, Springer, vol. 328(1), pages 903-975, September.
    5. Mehdi Alizadeh & Mir Saman Pishvaee & Hamed Jahani & Mohammad Mahdi Paydar & Ahmad Makui, 2023. "Viable healthcare supply chain network design for a pandemic," Annals of Operations Research, Springer, vol. 328(1), pages 35-73, September.
    6. Akira Watanabe & Hiroyuki Matsuda, 2023. "Effectiveness of feedback control and the trade-off between death by COVID-19 and costs of countermeasures," Health Care Management Science, Springer, vol. 26(1), pages 46-61, March.
    7. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    8. Michael Allen & Amir Bhanji & Jonas Willemsen & Steven Dudfield & Stuart Logan & Thomas Monks, 2020. "A simulation modelling toolkit for organising outpatient dialysis services during the COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-13, August.
    9. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    10. Elaiw, A.M. & Alsaedi, A.J. & Hobiny, A.D. & Aly, S., 2023. "Stability of a delayed SARS-CoV-2 reactivation model with logistic growth and adaptive immune response," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    11. Gorji, Mohammad-Ali & Shetab-Boushehri, Seyyed-Nader & Akbarzadeh, Meisam, 2023. "Evaluation and improvement of the resilience of a transportation system against epidemic diseases: A system dynamics approach," Transport Policy, Elsevier, vol. 133(C), pages 27-44.
    12. Kazim Topuz & Behrooz Davazdahemami & Dursun Delen, 2024. "A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases," Annals of Operations Research, Springer, vol. 341(1), pages 673-697, October.
    13. Sparnaaij, Martijn & Yuan, Yufei & Daamen, Winnie & Duives, Dorine C., 2024. "Using pedestrian modelling to inform virus transmission mitigation policies: A novel activity scheduling model to enable virus transmission risk assessment in a restaurant environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    14. Bhavya Sharma & Murari Lal Mittal & Gunjan Soni & Bharti Ramtiyal, 2023. "An Implementation Framework for Resiliency Assessment in a Supply Chain," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 591-614, December.
    15. Bingfeng Bai & Wei Fan, 2023. "Research on strategic liner ship fleet planning with regard to hub-and-spoke network," Operations Management Research, Springer, vol. 16(1), pages 363-376, March.

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