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| Verfasst von: | Kociurzynski, Raisa [VerfasserIn]  |
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| | D’Ambrosio, Angelo [VerfasserIn] |
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| | Papathanassopoulos, Alexis [VerfasserIn] |
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| | Bürkin, Fabian [VerfasserIn] |
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| | Hertweck, Stephan [VerfasserIn] |
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| | Eichel, Vanessa [VerfasserIn] |
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| | Heininger, Alexandra [VerfasserIn] |
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| | Liese, Jan [VerfasserIn] |
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| | Mutters, Nico T. [VerfasserIn] |
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| | Peter, Silke [VerfasserIn] |
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| | Wismath, Nina [VerfasserIn] |
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| | Wolf, Sophia [VerfasserIn] |
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| | Grundmann, Hajo [VerfasserIn] |
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| | Donker, Tjibbe [VerfasserIn] |
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| Titel: | Forecasting local hospital bed demand for COVID-19 using on-request simulations |
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| Verf.angabe: | Raisa Kociurzynski, Angelo D’Ambrosio, Alexis Papathanassopoulos, Fabian Bürkin, Stephan Hertweck, Vanessa M. Eichel, Alexandra Heininger, Jan Liese, Nico T. Mutters, Silke Peter, Nina Wismath, Sophia Wolf, Hajo Grundmann & Tjibbe Donker |
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| E-Jahr: | 2023 |
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| Jahr: | 03 December 2023 |
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| Umfang: | 15 S. |
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| Fussnoten: | Gesehen am 11.04.2024 |
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| Titel Quelle: | Enthalten in: Scientific reports |
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| Ort Quelle: | [London] : Springer Nature, 2011 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 13(2023) vom: Dez., Seite 1-15 |
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| ISSN Quelle: | 2045-2322 |
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| Abstract: | Accurate forecasting of hospital bed demand is crucial during infectious disease epidemics to avoid overwhelming healthcare facilities. To address this, we developed an intuitive online tool for individual hospitals to forecast COVID-19 bed demand. The tool utilizes local data, including incidence, vaccination, and bed occupancy data, at customizable geographical resolutions. Users can specify their hospital’s catchment area and adjust the initial number of COVID-19 occupied beds. We assessed the model’s performance by forecasting ICU bed occupancy for several university hospitals and regions in Germany. The model achieves optimal results when the selected catchment area aligns with the hospital’s local catchment. While expanding the catchment area reduces accuracy, it improves precision. However, forecasting performance diminishes during epidemic turning points. Incorporating variants of concern slightly decreases precision around turning points but does not significantly impact overall bed occupancy results. Our study highlights the significance of using local data for epidemic forecasts. Forecasts based on the hospital’s specific catchment area outperform those relying on national or state-level data, striking a better balance between accuracy and precision. These hospital-specific bed demand forecasts offer valuable insights for hospital planning, such as adjusting elective surgeries to create additional bed capacity promptly. |
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| DOI: | doi:10.1038/s41598-023-48601-8 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.1038/s41598-023-48601-8 |
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| | kostenfrei: Volltext: https://www.nature.com/articles/s41598-023-48601-8 |
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| | DOI: https://doi.org/10.1038/s41598-023-48601-8 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Computational models |
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| | Computational platforms and environments |
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| | Computer modelling |
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| | Epidemiology |
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| | Infectious diseases |
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| | Population dynamics |
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| | Statistical methods |
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| | Stochastic modelling |
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| | Time series |
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| | Vaccines |
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| | Viral infection |
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| K10plus-PPN: | 1885644078 |
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| Verknüpfungen: | → Zeitschrift |
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Forecasting local hospital bed demand for COVID-19 using on-request simulations / Kociurzynski, Raisa [VerfasserIn]; 03 December 2023 (Online-Ressource)
