A Scalable Forecasting Framework to Predict COVID-19 Hospital Bed Occupancy-Reference-Cited by-同舟云学术

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

Published:2022-11 Issue:6 Volume:52 Page:508-523
ISSN:2644-0865
Container-title:INFORMS Journal on Applied Analytics
language:en
Short-container-title:INFORMS Journal on Applied Analytics

Author:

Heins Jakob¹²^ORCID,Schoenfelder Jan¹^ORCID,Heider Steffen¹³^ORCID,Heller Axel R.²⁴^ORCID,Brunner Jens O.¹^ORCID

Affiliation:

1. Healthcare Operations/Health Information Management, Faculty of Business and Economics, University of Augsburg, 86159 Augsburg, Germany;

2. Department of Anesthesiology and Surgical Intensive Care Medicine, University Hospital Augsburg, 86156 Augsburg, Germany;

3. Unit of Digitalization and Business Analytics, University Hospital Augsburg, 86156 Augsburg, Germany;

4. Hospital Coordination, Ambulance District Augsburg, 86156 Augsburg, Germany

Abstract

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. Our forecasts were a central part of the official weekly reports of the Bavarian State Ministry of Health and Care from May 2020 to March 2021.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Link

https://pubsonline.informs.org/doi/pdf/10.1287/inte.2021.1115

Reference26 articles.

1. Short-Term Forecasting of Emergency Inpatient Flow

2. A network flow approach to optimizing hospital bed capacity decisions

3. A Nonhomogeneous Agent-Based Simulation Approach to Modeling the Spread of Disease in a Pandemic Outbreak

4. Daily Tutor Scheduling Support at Hopeful Journeys Educational Center

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