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Modeling COVID-19 hospital admissions and occupancy in the Netherlands

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  • Bekker, René
  • uit het Broek, Michiel
  • Koole, Ger

Abstract

We describe the models we built for predicting hospital admissions and bed occupancy of COVID-19 patients in the Netherlands. These models were used to make short-term decisions about transfers of patients between regions and for long-term policy making. For forecasting admissions we developed a new technique using linear programming. To predict occupancy we fitted residual lengths of stay and used results from queueing theory. Our models increased the accuracy of and trust in the predictions and helped manage the pandemic, minimizing the impact in terms of beds and maximizing remaining capacity for other types of care.

Suggested Citation

  • Bekker, René & uit het Broek, Michiel & Koole, Ger, 2023. "Modeling COVID-19 hospital admissions and occupancy in the Netherlands," European Journal of Operational Research, Elsevier, vol. 304(1), pages 207-218.
    • Handle: RePEc:eee:ejores:v:304:y:2023:i:1:p:207-218
    DOI: 10.1016/j.ejor.2021.12.044
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Dijkstra, Sander & Baas, Stef & Braaksma, Aleida & Boucherie, Richard J., 2023. "Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy," Omega, Elsevier, vol. 116(C).

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