Datensatz Zitierlink: 
http://hdl.handle.net/20.500.12708/144308
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Titel: 
Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system
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Zitat: 
Bicher, M., Zuba, M., Rainer, L., Bachner, F., Rippinger, C., Ostermann, H., Popper, N., Thurner, S., & Klimek, P. (2022). Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system. Communications Medicine, 2(1), Article 157. https://doi.org/10.1038/s43856-022-00219-z
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Verlags-DOI: 
10.1038/s43856-022-00219-z
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Publikationstyp: 
Artikel - Forschungsartikel
de
Sprache: 
Englisch
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Autor_innen: 
Bicher, Martin 
Zuba, Martin 
Rainer, Lukas 
Bachner, Florian 
Rippinger, Claire 
Ostermann, Herwig 
Popper, Nikolas 
Thurner, Stefan 
Klimek, Peter 
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Organisations­einheit: 
E194-04 - Forschungsbereich Data Science 
E105-06 - Forschungsbereich Computational Statistics 
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Zeitschrift: 
Communications Medicine 
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Datum (veröffentlicht): 
Dez-2022
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Umfang: 
11
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Verlag: 
Springer Nature
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Peer Reviewed: 
Ja
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Keywords: 
Modelling and Simulation; COVID-19; Non-Pharmaceutical Interventions; Agent Based Modeling; Epidemiological Modelling
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Abstract: 
In response to the SARS-CoV-2 pandemic, the Austrian governmental crisis unit commissioned a forecast consortium with regularly projections of case numbers and demand for hospital beds. The goal was to assess how likely Austrian ICUs would become overburdened with COVID-19 patients in the upcoming weeks.
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Forschungs­schwerpunkte: 
Mathematical and Algorithmic Foundations: 30%
Beyond TUW-research foci: 10%
Modeling and Simulation: 60%
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Wissenschafts­zweig: 
1020 - Informatik: 30%
1010 - Mathematik: 70%
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Enthalten in den Sammlungen:
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