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 journalArticleAcademicpeer-review

17 Citations (Scopus)
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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 languageEnglish
Pages (from-to)402-419
Number of pages18
JournalHealth care management science
Issue number2
Early online date25 Mar 2021
Publication statusPublished - Jun 2021


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


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