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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2021
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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. |
format | Online Article Text |
id | pubmed-8128110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
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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