Record link: 
http://hdl.handle.net/20.500.12708/139737
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Title: 
An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply
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Citation: 
Bicher, M., Rippinger, C., Zechmeister, M., Jahn, B., Sroczynski, G., Mühlberger, N., Santamaria-Navarro, J., Urach, C., Brunmeir, D., Siebert, U., & Popper, N. (2022). An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply. PLoS ONE, 17(5), Article e0265957. https://doi.org/10.1371/journal.pone.0265957
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Publisher DOI: 
10.1371/journal.pone.0265957
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
Bicher, Martin 
Rippinger, Claire 
Zechmeister, Melanie 
Jahn, Beate 
Sroczynski, Gaby 
Mühlberger, Nikolai 
Santamaria-Navarro, Julia 
Urach, Christoph 
Brunmeir, Dominik 
Siebert, Uwe 
Popper, Nikolas 
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Organisational Unit: 
E105-06 - Forschungsbereich Computational Statistics 
E194-04 - Forschungsbereich Data Science 
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Journal: 
PLoS ONE 
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ISSN: 
1932-6203
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Date (published): 
2-May-2022
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Number of Pages: 
12
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Publisher: 
PUBLIC LIBRARY SCIENCE
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Peer reviewed: 
Yes
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Keywords: 
Aged; Algorithms; COVID-19 Vaccines; Humans; SARS-CoV-2; Vaccination; COVID-19; Influenza Vaccines; Influenza, Human; Modelling and Simulation
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Abstract: 
The distribution of the newly developed vaccines presents a great challenge in the ongoing SARS-CoV-2 pandemic. Policy makers must decide which subgroups should be vaccinated first to minimize the negative consequences of the pandemic. These decisions must be made upfront and under uncertainty regarding the amount of vaccine doses available at a given time. The objective of the present work was to develop an iterative optimization algorithm, which provides a prioritization order of predefined subgroups. The results of this algorithm should be optimal but also robust with respect to potentially limited vaccine supply.
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Research Areas: 
Beyond TUW-research foci: 40%
Computer Science Foundations: 10%
Modeling and Simulation: 50%
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Science Branch: 
1020 - Informatik: 10%
5020 - Wirtschaftswissenschaften: 20%
1010 - Mathematik: 70%
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