<div class="csl-bib-body">
<div class="csl-entry">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. <i>PLoS ONE</i>, <i>17</i>(5), Article e0265957. https://doi.org/10.1371/journal.pone.0265957</div>
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dc.identifier.issn
1932-6203
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/139737
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dc.description.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.
en
dc.language.iso
en
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dc.publisher
PUBLIC LIBRARY SCIENCE
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dc.relation.ispartof
PLoS ONE
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dc.subject
Aged
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dc.subject
Algorithms
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dc.subject
COVID-19 Vaccines
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dc.subject
Humans
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dc.subject
SARS-CoV-2
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dc.subject
Vaccination
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dc.subject
COVID-19
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dc.subject
Influenza Vaccines
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dc.subject
Influenza, Human
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dc.subject
Modelling and Simulation
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dc.title
An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply