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Vaccine optimization for COVID-19: who to vaccinate first?
A vaccine, when available, will likely become our best tool to control the current 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 f...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430607/ https://www.ncbi.nlm.nih.gov/pubmed/32817963 http://dx.doi.org/10.1101/2020.08.14.20175257 |
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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 | A vaccine, when available, will likely become our best tool to control the current 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-7430607 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-74306072020-08-18 Vaccine optimization for COVID-19: who to vaccinate first? Matrajt, Laura Eaton, Julia Leung, Tiffany Brown, Elizabeth R. medRxiv Article A vaccine, when available, will likely become our best tool to control the current 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. Cold Spring Harbor Laboratory 2020-12-15 /pmc/articles/PMC7430607/ /pubmed/32817963 http://dx.doi.org/10.1101/2020.08.14.20175257 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article 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 | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430607/ https://www.ncbi.nlm.nih.gov/pubmed/32817963 http://dx.doi.org/10.1101/2020.08.14.20175257 |
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