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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

    as
    1. Matthew D. Bailey & Lucas A. Waddell, 2020. "Daily Tutor Scheduling Support at Hopeful Journeys Educational Center," Interfaces, INFORMS, vol. 50(5), pages 287-297, September.
    2. Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen, 2011. "Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals," International Journal of Forecasting, Elsevier, vol. 27(3), pages 887-901.
    3. Elif Akcali & Murray Côté & Chin Lin, 2006. "A network flow approach to optimizing hospital bed capacity decisions," Health Care Management Science, Springer, vol. 9(4), pages 391-404, November.
    4. David Scheinker & Margaret L. Brandeau, 2020. "Implementing Analytics Projects in a Hospital: Successes, Failures, and Opportunities," Interfaces, INFORMS, vol. 50(3), pages 176-189, May.
    5. Hongwei Zhao & Naveed N Merchant & Alyssa McNulty & Tiffany A Radcliff & Murray J Cote & Rebecca S B Fischer & Huiyan Sang & Marcia G Ory, 2021. "COVID-19: Short term prediction model using daily incidence data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-14, April.
    6. Christian Haket & Bo van der Rhee & Jacques de Swart, 2020. "Saving Time and Money and Reducing Carbon Dioxide Emissions by Efficiently Allocating Customers," Interfaces, INFORMS, vol. 50(3), pages 153-165, May.
    7. Jan Schoenfelder & Christian Pfefferlen, 2018. "Decision Support for the Physician Scheduling Process at a German Hospital," Service Science, INFORMS, vol. 10(3), pages 215-229, September.
    8. Stephan Onggo & Michael Pidd & Didier Soopramanien & Dave Worthington, 2010. "Simulation of Career Development in the European Commission," Interfaces, INFORMS, vol. 40(3), pages 184-195, June.
    9. Steffen Heider & Jakob Heins & John J. Kanet, 2018. "Applying Operations Research to Scheduling Work Cells in a Manufacturing Environment," Interfaces, INFORMS, vol. 48(6), pages 556-565, November.
    10. Dionne M. Aleman & Theodorus G. Wibisono & Brian Schwartz, 2011. "A Nonhomogeneous Agent-Based Simulation Approach to Modeling the Spread of Disease in a Pandemic Outbreak," Interfaces, INFORMS, vol. 41(3), pages 301-315, June.
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    Cited by:

    1. Chengliang Wang & Feifei Yang & Quan-Lin Li, 2023. "Optimal Decision of Dynamic Bed Allocation and Patient Admission with Buffer Wards during an Epidemic," Mathematics, MDPI, vol. 11(3), pages 1-23, January.

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