Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Matrajt, Laura, Eaton, Julia, Leung, Tiffany, Brown, Elizabeth R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2020
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
_version_ 1783571453018374144
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
work_keys_str_mv AT matrajtlaura vaccineoptimizationforcovid19whotovaccinatefirst
AT eatonjulia vaccineoptimizationforcovid19whotovaccinatefirst
AT leungtiffany vaccineoptimizationforcovid19whotovaccinatefirst
AT brownelizabethr vaccineoptimizationforcovid19whotovaccinatefirst