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A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy

Author

Listed:
  • Jakob Heins

    (Healthcare Operations/Health Information Management, Faculty of Business and Economics, University of Augsburg, 86159 Augsburg, Germany; Department of Anesthesiology and Surgical Intensive Care Medicine, University Hospital Augsburg, 86156 Augsburg, Germany)

  • Jan Schoenfelder

    (Healthcare Operations/Health Information Management, Faculty of Business and Economics, University of Augsburg, 86159 Augsburg, Germany)

  • Steffen Heider

    (Healthcare Operations/Health Information Management, Faculty of Business and Economics, University of Augsburg, 86159 Augsburg, Germany; Unit of Digitalization and Business Analytics, University Hospital Augsburg, 86156 Augsburg, Germany)

  • Axel R. Heller

    (Department of Anesthesiology and Surgical Intensive Care Medicine, University Hospital Augsburg, 86156 Augsburg, Germany; Hospital Coordination, Ambulance District Augsburg, 86156 Augsburg, Germany)

  • Jens O. Brunner

    (Healthcare Operations/Health Information Management, Faculty of Business and Economics, University of Augsburg, 86159 Augsburg, Germany)

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has led to capacity problems in many hospitals around the world. During the peak of new infections in Germany in April 2020 and October to December 2020, most hospitals had to cancel elective procedures for patients because of capacity shortages. We present a scalable forecasting framework with a Monte Carlo simulation to forecast the short-term bed occupancy of patients with confirmed and suspected COVID-19 in intensive care units and regular wards. We apply the simulation to different granularity and geographical levels. Our forecasts were a central part of the official weekly reports of the Bavarian State Ministry of Health and Care, which were sent to key decision makers in the individual ambulance districts from May 2020 to March 2021. Our evaluation shows that the forecasting framework delivers accurate forecasts despite data availability and quality issues.

Suggested Citation

  • Jakob Heins & Jan Schoenfelder & Steffen Heider & Axel R. Heller & Jens O. Brunner, 2022. "A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy," Interfaces, INFORMS, vol. 52(6), pages 508-523, November.
    • Handle: RePEc:inm:orinte:v:52:y:2022:i:6:p:508-523
    DOI: 10.1287/inte.2021.1115
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    References listed on IDEAS

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    Cited by:

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    3. Bradley S. Price & John P. Saldanha & Bernardo F. Quiroga & Sally L. Hodder, 2024. "Maintaining Healthcare Capacity in Rural America by Replenishing Personal Protective Equipment: The Case from West Virginia," Interfaces, INFORMS, vol. 54(6), pages 517-536, November.
    4. Arti Mann & Ben Cleveland & Dan Bumblauskas & Shashidhar Kaparthi, 2024. "Reducing Hospital Readmission Risk Using Predictive Analytics," Interfaces, INFORMS, vol. 54(4), pages 380-388, July.

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