IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v22y2024i1p39-d1556925.html
   My bibliography  Save this article

Early COVID-19 Pandemic Preparedness: Informing Public Health Interventions and Hospital Capacity Planning Through Participatory Hybrid Simulation Modeling

Author

Listed:
  • Yuan Tian

    (Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada)

  • Jenny Basran

    (Saskatchewan Health Authority, Saskatoon, SK S7K 0M7, Canada
    College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada)

  • Wade McDonald

    (Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada)

  • Nathaniel D. Osgood

    (Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada)

Abstract

We engaged with health sector stakeholders and public health professionals within the health system through a participatory modeling approach to support policy-making in the early COVID-19 pandemic in Saskatchewan, Canada. The objective was to use simulation modeling to guide the implementation of public health measures and short-term hospital capacity planning to mitigate the disease burden from March to June 2020. We developed a hybrid simulation model combining System Dynamics (SD), discrete-event simulation (DES), and agent-based modeling (ABM). SD models the population-level transmission of COVID-19, ABM simulates individual-level disease progression and contact tracing intervention, and DES captures COVID-19-related hospital patient flow. We examined the impact of mixed mitigation strategies—physical distancing, testing, conventional and digital contact tracing—on COVID-19 transmission and hospital capacity for a worst-case scenario. Modeling results showed that enhanced contact tracing with mass testing in the early pandemic could significantly reduce transmission, mortality, and the peak census of hospital beds and intensive care beds. Using a participatory modeling approach, we not only directly informed policy-making on contact tracing interventions and hospital surge capacity planning for COVID-19 but also helped validate the effectiveness of the interventions adopted by the provincial government. We conclude with a discussion on lessons learned and the novelty of our hybrid approach.

Suggested Citation

  • Yuan Tian & Jenny Basran & Wade McDonald & Nathaniel D. Osgood, 2024. "Early COVID-19 Pandemic Preparedness: Informing Public Health Interventions and Hospital Capacity Planning Through Participatory Hybrid Simulation Modeling," IJERPH, MDPI, vol. 22(1), pages 1-23, December.
    • Handle: RePEc:gam:jijerp:v:22:y:2024:i:1:p:39-:d:1556925
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/22/1/39/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/22/1/39/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kotiadis, K. & Tako, A.A., 2018. "Facilitated post-model coding in discrete event simulation (DES): A case study in healthcare," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1120-1133.
    2. P R Harper & M A Pitt, 2004. "On the challenges of healthcare modelling and a proposed project life cycle for successful implementation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(6), pages 657-661, June.
    3. Frerichs, L. & Lich, K.H. & Dave, G. & Corbie-Smith, G., 2016. "Integrating systems science and community-based participatory research to achieve health equity," American Journal of Public Health, American Public Health Association, vol. 106(2), pages 215-222.
    4. Brailsford, Sally C. & Eldabi, Tillal & Kunc, Martin & Mustafee, Navonil & Osorio, Andres F., 2019. "Hybrid simulation modelling in operational research: A state-of-the-art review," European Journal of Operational Research, Elsevier, vol. 278(3), pages 721-737.
    5. D F Andersen & J A M Vennix & G P Richardson & E A J A Rouwette, 2007. "Group model building: problem structuring, policy simulation and decision support," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(5), pages 691-694, May.
    6. S J E Taylor & T Eldabi & G Riley & R J Paul & M Pidd, 2009. "Simulation modelling is 50! Do we need a reality check?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 69-82, May.
    7. S C Brailsford & P R Harper & B Patel & M Pitt, 2009. "An analysis of the academic literature on simulation and modelling in health care," Journal of Simulation, Taylor & Francis Journals, vol. 3(3), pages 130-140, September.
    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. Tako, Antuela A. & Kotiadis, Kathy, 2015. "PartiSim: A multi-methodology framework to support facilitated simulation modelling in healthcare," European Journal of Operational Research, Elsevier, vol. 244(2), pages 555-564.
    Full references (including those not matched with items on IDEAS)

    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. Proudlove, N.C. & Bisogno, S. & Onggo, B.S.S. & Calabrese, A. & Levialdi Ghiron, N., 2017. "Towards fully-facilitated discrete event simulation modelling: Addressing the model coding stage," European Journal of Operational Research, Elsevier, vol. 263(2), pages 583-595.
    2. Martin Comis & Catherine Cleophas & Christina Büsing, 2021. "Patients, primary care, and policy: Agent-based simulation modeling for health care decision support," Health Care Management Science, Springer, vol. 24(4), pages 799-826, December.
    3. Harper, Alison & Mustafee, Navonil & Yearworth, Mike, 2021. "Facets of trust in simulation studies," European Journal of Operational Research, Elsevier, vol. 289(1), pages 197-213.
    4. Nguyen, Le Khanh Ngan & Howick, Susan & Megiddo, Itamar, 2024. "A framework for conceptualising hybrid system dynamics and agent-based simulation models," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1153-1166.
    5. Esposito Amideo, A. & Scaparra, M.P. & Kotiadis, K., 2019. "Optimising shelter location and evacuation routing operations: The critical issues," European Journal of Operational Research, Elsevier, vol. 279(2), pages 279-295.
    6. Ricciardi, Francesca & De Bernardi, Paola & Cantino, Valter, 2020. "System dynamics modeling as a circular process: The smart commons approach to impact management," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    7. Martin Kunc, 2021. "A commentary on Lustick and Tetlock 2021," Futures & Foresight Science, John Wiley & Sons, vol. 3(2), June.
    8. Alireza Moumivand & Adel Azar & Abbas Toloie Eshlaghy, 2022. "Combined soft system methodology and agent‐based simulation for multi‐methodological modelling," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(2), pages 200-217, March.
    9. Mohsen Jahangirian & Simon J E Taylor & Terry Young & Stewart Robinson, 2017. "Key performance indicators for successful simulation projects," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 747-765, July.
    10. Jesús Isaac Vázquez-Serrano & Rodrigo E. Peimbert-García & Leopoldo Eduardo Cárdenas-Barrón, 2021. "Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review," IJERPH, MDPI, vol. 18(22), pages 1-20, November.
    11. Carter, Michael W. & Busby, Carolyn R., 2023. "How can operational research make a real difference in healthcare? Challenges of implementation," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1059-1068.
    12. Jaén, Sebastián, 2024. "The decrease of ED patient boarding by implementing a stock management policy in hospital admissions," Operations Research Perspectives, Elsevier, vol. 12(C).
    13. Ichino, Andrea & Favero, Carlo A. & Rustichini, Aldo, 2020. "Restarting the economy while saving lives under Covid-19," CEPR Discussion Papers 14664, C.E.P.R. Discussion Papers.
    14. Nina Tura & Lea Hannola & Mikko Pynnönen, 2017. "Agile Methods for Boosting the Commercialization Process of New Technology," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 14(03), pages 1-23, June.
    15. M. Hashem Pesaran & Cynthia Fan Yang, 2022. "Matching theory and evidence on Covid‐19 using a stochastic network SIR model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1204-1229, September.
    16. 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.
    17. Houštecká, Anna & Koh, Dongya & Santaeulàlia-Llopis, Raül, 2021. "Contagion at work: Occupations, industries and human contact," Journal of Public Economics, Elsevier, vol. 200(C).
    18. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    19. John M Drake & Tobias S Brett & Shiyang Chen & Bogdan I Epureanu & Matthew J Ferrari & Éric Marty & Paige B Miller & Eamon B O’Dea & Suzanne M O’Regan & Andrew W Park & Pejman Rohani, 2019. "The statistics of epidemic transitions," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-14, May.
    20. S. M. Niaz Arifin & Christoph Zimmer & Caroline Trotter & Anaïs Colombini & Fati Sidikou & F. Marc LaForce & Ted Cohen & Reza Yaesoubi, 2019. "Cost-Effectiveness of Alternative Uses of Polyvalent Meningococcal Vaccines in Niger: An Agent-Based Transmission Modeling Study," Medical Decision Making, , vol. 39(5), pages 553-567, July.

    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:22:y:2024:i:1:p:39-:d:1556925. 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.