Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units

Stef Baas; Sander Dijkstra; Aleida Braaksma; Plom van Rooij; Fieke J. Snijders; Lars Tiemessen; Richard Boucherie

doi:10.1007/s10729-021-09553-5

Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units

Stef Baas, Sander Dijkstra, Aleida Braaksma^*, Plom van Rooij, Fieke J. Snijders, Lars Tiemessen, Richard Boucherie

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

This paper presents a mathematical model that provides a real-time forecast of the number of COVID-19 patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their Length of Stay (LoS) in both the ward and the ICU as well as transfer of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital’s data warehouse. The resulting algorithm is tested on data from the first COVID-19 peak in the Netherlands, showing that the forecast is very accurate. The forecast may be visualised in real-time in the hospital’s control centre and is used in several Dutch hospitals during the second COVID-19 peak.

Original language	English
Pages (from-to)	402-419
Number of pages	18
Journal	Health care management science
Volume	24
Issue number	2
Early online date	25 Mar 2021
DOIs	https://doi.org/10.1007/s10729-021-09553-5
Publication status	Published - Jun 2021

Keywords

UT-Hybrid-D
Forecast
Bed Occupancy
Network of infinite server queues
Richards' curve
Kaplan-Meier estimator
COVID-19

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10.1007/s10729-021-09553-5Licence: CC BY

ArticleFinal published version, 2.23 MBLicence: CC BY

Code for paper Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units
Baas, S. (Creator), 4TU.Centre for Research Data, 24 Jun 2024
DOI: 10.4121/82c58053-92a3-4a9b-8632-1fda61b84974, https://data.4tu.nl/datasets/82c58053-92a3-4a9b-8632-1fda61b84974 and 3 more links, https://doi.org/10.4121/82c58053-92a3-4a9b-8632-1fda61b84974, https://data.4tu.nl/datasets/82c58053-92a3-4a9b-8632-1fda61b84974/1, https://doi.org/10.4121/82c58053-92a3-4a9b-8632-1fda61b84974.v1 (show fewer)
Dataset

Cite this

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title = "Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units",

abstract = "This paper presents a mathematical model that provides a real-time forecast of the number of COVID-19 patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their Length of Stay (LoS) in both the ward and the ICU as well as transfer of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital{\textquoteright}s data warehouse. The resulting algorithm is tested on data from the first COVID-19 peak in the Netherlands, showing that the forecast is very accurate. The forecast may be visualised in real-time in the hospital{\textquoteright}s control centre and is used in several Dutch hospitals during the second COVID-19 peak.",

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author = "Stef Baas and Sander Dijkstra and Aleida Braaksma and {van Rooij}, Plom and Snijders, {Fieke J.} and Lars Tiemessen and Richard Boucherie",

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AU - Baas, Stef

AU - Dijkstra, Sander

AU - Braaksma, Aleida

AU - van Rooij, Plom

AU - Snijders, Fieke J.

AU - Tiemessen, Lars

AU - Boucherie, Richard

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Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units

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