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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy. / Heins, Jakob; Schoenfelder, Jan; Heider, Steffen et al.
In: INFORMS Journal on Applied Analytics, Vol. 52, No. 6, 30.11.2022, p. 508-523.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Heins, J, Schoenfelder, J, Heider, S, Heller, AR & Brunner, JO 2022, 'A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy', INFORMS Journal on Applied Analytics, vol. 52, no. 6, pp. 508-523. https://doi.org/10.1287/inte.2021.1115

APA

Heins, J., Schoenfelder, J., Heider, S., Heller, A. R., & Brunner, J. O. (2022). A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy. INFORMS Journal on Applied Analytics, 52(6), 508-523. https://doi.org/10.1287/inte.2021.1115

Vancouver

Heins J, Schoenfelder J, Heider S, Heller AR, Brunner JO. A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy. INFORMS Journal on Applied Analytics. 2022 Nov 30;52(6):508-523. Epub 2022 Jan 14. doi: 10.1287/inte.2021.1115

Author

Heins, Jakob ; Schoenfelder, Jan ; Heider, Steffen et al. / A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy. In: INFORMS Journal on Applied Analytics. 2022 ; Vol. 52, No. 6. pp. 508-523.

Bibtex

@article{9327bb36c1124bdeb55c6770a4d2c15e,
title = "A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy",
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.",
author = "Jakob Heins and Jan Schoenfelder and Steffen Heider and Heller, {Axel R.} and Brunner, {Jens O.}",
year = "2022",
month = nov,
day = "30",
doi = "10.1287/inte.2021.1115",
language = "English",
volume = "52",
pages = "508--523",
journal = "INFORMS Journal on Applied Analytics",
number = "6",

}

RIS

TY - JOUR

T1 - A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy

AU - Heins, Jakob

AU - Schoenfelder, Jan

AU - Heider, Steffen

AU - Heller, Axel R.

AU - Brunner, Jens O.

PY - 2022/11/30

Y1 - 2022/11/30

N2 - 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.

AB - 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.

U2 - 10.1287/inte.2021.1115

DO - 10.1287/inte.2021.1115

M3 - Journal article

VL - 52

SP - 508

EP - 523

JO - INFORMS Journal on Applied Analytics

JF - INFORMS Journal on Applied Analytics

IS - 6

ER -