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

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 o...

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Detalles Bibliográficos
Autores principales: Baas, Stef, Dijkstra, Sander, Braaksma, Aleida, van Rooij, Plom, Snijders, Fieke J., Tiemessen, Lars, Boucherie, Richard J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7993447/
https://www.ncbi.nlm.nih.gov/pubmed/33768389
http://dx.doi.org/10.1007/s10729-021-09553-5
Descripción
Sumario: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.

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