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Hospital preparedness during epidemics using simulation: the case of COVID-19

Author

Listed:
  • Daniel Garcia-Vicuña

    (Public University of Navarre)

  • Laida Esparza

    (Hospital Compound of Navarre)

  • Fermin Mallor

    (Public University of Navarre)

Abstract

This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.

Suggested Citation

  • Daniel Garcia-Vicuña & Laida Esparza & Fermin Mallor, 2022. "Hospital preparedness during epidemics using simulation: the case of COVID-19," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 213-249, March.
    • Handle: RePEc:spr:cejnor:v:30:y:2022:i:1:d:10.1007_s10100-021-00779-w
    DOI: 10.1007/s10100-021-00779-w
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    References listed on IDEAS

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    Cited by:

    1. Duygu Üçüncü & Süreyya Akyüz & Erdal Gül, 2024. "A novel auto-pruned ensemble clustering via SOCP," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 32(3), pages 819-841, September.
    2. Michael R. Johnson & Hiten Naik & Wei Siang Chan & Jesse Greiner & Matt Michaleski & Dong Liu & Bruno Silvestre & Ian P. McCarthy, 2023. "Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions," Health Care Management Science, Springer, vol. 26(3), pages 477-500, September.
    3. Víctor Blanco & Ricardo Gázquez & Marina Leal, 2023. "Mathematical optimization models for reallocating and sharing health equipment in pandemic situations," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 355-390, July.

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