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Vaccine optimization for COVID-19: Who to vaccinate first?

Vaccines, when available, will likely become our best tool to control the COVID-19 pandemic. Even in the most optimistic scenarios, vaccine shortages will likely occur. Using an age-stratified mathematical model paired with optimization algorithms, we determined optimal vaccine allocation for four d...

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Detalles Bibliográficos
Autores principales: Matrajt, Laura, Eaton, Julia, Leung, Tiffany, Brown, Elizabeth R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8128110/
https://www.ncbi.nlm.nih.gov/pubmed/33536223
http://dx.doi.org/10.1126/sciadv.abf1374
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author Matrajt, Laura
Eaton, Julia
Leung, Tiffany
Brown, Elizabeth R.
author_facet Matrajt, Laura
Eaton, Julia
Leung, Tiffany
Brown, Elizabeth R.
author_sort Matrajt, Laura
collection PubMed
description Vaccines, when available, will likely become our best tool to control the COVID-19 pandemic. Even in the most optimistic scenarios, vaccine shortages will likely occur. Using an age-stratified mathematical model paired with optimization algorithms, we determined optimal vaccine allocation for four different metrics (deaths, symptomatic infections, and maximum non-ICU and ICU hospitalizations) under many scenarios. We find that a vaccine with effectiveness ≥50% would be enough to substantially mitigate the ongoing pandemic, provided that a high percentage of the population is optimally vaccinated. When minimizing deaths, we find that for low vaccine effectiveness, irrespective of vaccination coverage, it is optimal to allocate vaccine to high-risk (older) age groups first. In contrast, for higher vaccine effectiveness, there is a switch to allocate vaccine to high-transmission (younger) age groups first for high vaccination coverage. While there are other societal and ethical considerations, this work can provide an evidence-based rationale for vaccine prioritization.
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spelling pubmed-81281102021-05-24 Vaccine optimization for COVID-19: Who to vaccinate first? Matrajt, Laura Eaton, Julia Leung, Tiffany Brown, Elizabeth R. Sci Adv Research Articles Vaccines, when available, will likely become our best tool to control the COVID-19 pandemic. Even in the most optimistic scenarios, vaccine shortages will likely occur. Using an age-stratified mathematical model paired with optimization algorithms, we determined optimal vaccine allocation for four different metrics (deaths, symptomatic infections, and maximum non-ICU and ICU hospitalizations) under many scenarios. We find that a vaccine with effectiveness ≥50% would be enough to substantially mitigate the ongoing pandemic, provided that a high percentage of the population is optimally vaccinated. When minimizing deaths, we find that for low vaccine effectiveness, irrespective of vaccination coverage, it is optimal to allocate vaccine to high-risk (older) age groups first. In contrast, for higher vaccine effectiveness, there is a switch to allocate vaccine to high-transmission (younger) age groups first for high vaccination coverage. While there are other societal and ethical considerations, this work can provide an evidence-based rationale for vaccine prioritization. American Association for the Advancement of Science 2021-02-03 /pmc/articles/PMC8128110/ /pubmed/33536223 http://dx.doi.org/10.1126/sciadv.abf1374 Text en Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Matrajt, Laura
Eaton, Julia
Leung, Tiffany
Brown, Elizabeth R.
Vaccine optimization for COVID-19: Who to vaccinate first?
title Vaccine optimization for COVID-19: Who to vaccinate first?
title_full Vaccine optimization for COVID-19: Who to vaccinate first?
title_fullStr Vaccine optimization for COVID-19: Who to vaccinate first?
title_full_unstemmed Vaccine optimization for COVID-19: Who to vaccinate first?
title_short Vaccine optimization for COVID-19: Who to vaccinate first?
title_sort vaccine optimization for covid-19: who to vaccinate first?
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8128110/
https://www.ncbi.nlm.nih.gov/pubmed/33536223
http://dx.doi.org/10.1126/sciadv.abf1374
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