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

Jakob Heins (), Jan Schoenfelder (), Steffen Heider (), Axel R. Heller () and Jens O. Brunner ()
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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

Interfaces, 2022, vol. 52, issue 6, 508-523

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.

Keywords: bed capacity planning; COVID-19; intensive care unit; decision support system; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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