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Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units

Author

Listed:
  • Stef Baas

    (University of Twente)

  • Sander Dijkstra

    (University of Twente)

  • Aleida Braaksma

    (University of Twente)

  • Plom Rooij

    (Elisabeth-TweeSteden Ziekenhuis)

  • Fieke J. Snijders

    (Leiden University Medical Centre)

  • Lars Tiemessen

    (Rijnstate)

  • Richard J. Boucherie

    (University of Twente)

Abstract

This paper presents a mathematical model that provides a real-time forecast of the number of COVID-19 patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their Length of Stay (LoS) in both the ward and the ICU as well as transfer of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital’s data warehouse. The resulting algorithm is tested on data from the first COVID-19 peak in the Netherlands, showing that the forecast is very accurate. The forecast may be visualised in real-time in the hospital’s control centre and is used in several Dutch hospitals during the second COVID-19 peak.

Suggested Citation

  • Stef Baas & Sander Dijkstra & Aleida Braaksma & Plom Rooij & Fieke J. Snijders & Lars Tiemessen & Richard J. Boucherie, 2021. "Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units," Health Care Management Science, Springer, vol. 24(2), pages 402-419, June.
    • Handle: RePEc:kap:hcarem:v:24:y:2021:i:2:d:10.1007_s10729-021-09553-5
    DOI: 10.1007/s10729-021-09553-5
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    References listed on IDEAS

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    5. Litvak, Nelly & van Rijsbergen, Marleen & Boucherie, Richard J. & van Houdenhoven, Mark, 2008. "Managing the overflow of intensive care patients," European Journal of Operational Research, Elsevier, vol. 185(3), pages 998-1010, March.
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    Cited by:

    1. Costase Ndayishimiye & Christoph Sowada & Patrycja Dyjach & Agnieszka Stasiak & John Middleton & Henrique Lopes & Katarzyna Dubas-Jakóbczyk, 2022. "Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning—A Scoping Review," IJERPH, MDPI, vol. 19(13), pages 1-22, July.
    2. Samantha L. Zimmerman & Alexander R. Rutherford & Alexa Waall & Monica Norena & Peter Dodek, 2023. "A queuing model for ventilator capacity management during the COVID-19 pandemic," Health Care Management Science, Springer, vol. 26(2), pages 200-216, June.
    3. 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.
    4. Alec Morton & Ebru Bish & Itamar Megiddo & Weifen Zhuang & Roberto Aringhieri & Sally Brailsford & Sarang Deo & Na Geng & Julie Higle & David Hutton & Mart Janssen & Edward H Kaplan & Jianbin Li & Món, 2021. "Introduction to the special issue: Management Science in the Fight Against Covid-19," Health Care Management Science, Springer, vol. 24(2), pages 251-252, June.
    5. 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).
    6. Bahnsen, Lewe & Wild, Frank, 2021. "Europäische Gesundheitssysteme in der COVID-19-Pandemie: Ein vergleichender Überblick," WIP-Analysen Juli 2021, WIP – Wissenschaftliches Institut der PKV.

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