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

Stef Baas, Sander Dijkstra, Aleida Braaksma (), Plom Rooij, Fieke J. Snijders, Lars Tiemessen and Richard J. Boucherie
Additional contact information
Stef Baas: University of Twente
Sander Dijkstra: University of Twente
Aleida Braaksma: University of Twente
Plom Rooij: Elisabeth-TweeSteden Ziekenhuis
Fieke J. Snijders: Leiden University Medical Centre
Lars Tiemessen: Rijnstate
Richard J. Boucherie: University of Twente

Health Care Management Science, 2021, vol. 24, issue 2, No 12, 402-419

Abstract: 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.

Keywords: COVID-19; Forecast; Bed occupancy; Network of infinite server queues; Richards’ curve; Kaplan-Meier estimator (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s10729-021-09553-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:hcarem:v:24:y:2021:i:2:d:10.1007_s10729-021-09553-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10729

DOI: 10.1007/s10729-021-09553-5

Access Statistics for this article

Health Care Management Science is currently edited by Yasar Ozcan

More articles in Health Care Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().