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Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach

The first round of vaccination against coronavirus disease 2019 (COVID-19) began in early December of 2020 in a few countries. There are several vaccines, and each has a different efficacy and mechanism of action. Several countries, for example, the United Kingdom and the USA, have been able to deve...

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Autor principal: Gonzalez-Parra, Gilberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8809086/
https://www.ncbi.nlm.nih.gov/pubmed/35118438
http://dx.doi.org/10.3390/epidemiologia2030021
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author Gonzalez-Parra, Gilberto
author_facet Gonzalez-Parra, Gilberto
author_sort Gonzalez-Parra, Gilberto
collection PubMed
description The first round of vaccination against coronavirus disease 2019 (COVID-19) began in early December of 2020 in a few countries. There are several vaccines, and each has a different efficacy and mechanism of action. Several countries, for example, the United Kingdom and the USA, have been able to develop consistent vaccination programs where a great percentage of the population has been vaccinated (May 2021). However, in other countries, a low percentage of the population has been vaccinated due to constraints related to vaccine supply and distribution capacity. Countries such as the USA and the UK have implemented different vaccination strategies, and some scholars have been debating the optimal strategy for vaccine campaigns. This problem is complex due to the great number of variables that affect the relevant outcomes. In this article, we study the impact of different vaccination regimens on main health outcomes such as deaths, hospitalizations, and the number of infected. We develop a mathematical model of COVID-19 transmission to focus on this important health policy issue. Thus, we are able to identify the optimal strategy regarding vaccination campaigns. We find that for vaccines with high efficacy (>70%) after the first dose, the optimal strategy is to delay inoculation with the second dose. On the other hand, for a low first dose vaccine efficacy, it is better to use the standard vaccination regimen of 4 weeks between doses. Thus, under the delayed second dose option, a campaign focus on generating a certain immunity in as great a number of people as fast as possible is preferable to having an almost perfect immunity in fewer people first. Therefore, based on these results, we suggest that the UK implemented a better vaccination campaign than that in the USA with regard to time between doses. The results presented here provide scientific guidelines for other countries where vaccination campaigns are just starting, or the percentage of vaccinated people is small.
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spelling pubmed-88090862022-02-02 Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach Gonzalez-Parra, Gilberto Epidemiologia (Basel) Article The first round of vaccination against coronavirus disease 2019 (COVID-19) began in early December of 2020 in a few countries. There are several vaccines, and each has a different efficacy and mechanism of action. Several countries, for example, the United Kingdom and the USA, have been able to develop consistent vaccination programs where a great percentage of the population has been vaccinated (May 2021). However, in other countries, a low percentage of the population has been vaccinated due to constraints related to vaccine supply and distribution capacity. Countries such as the USA and the UK have implemented different vaccination strategies, and some scholars have been debating the optimal strategy for vaccine campaigns. This problem is complex due to the great number of variables that affect the relevant outcomes. In this article, we study the impact of different vaccination regimens on main health outcomes such as deaths, hospitalizations, and the number of infected. We develop a mathematical model of COVID-19 transmission to focus on this important health policy issue. Thus, we are able to identify the optimal strategy regarding vaccination campaigns. We find that for vaccines with high efficacy (>70%) after the first dose, the optimal strategy is to delay inoculation with the second dose. On the other hand, for a low first dose vaccine efficacy, it is better to use the standard vaccination regimen of 4 weeks between doses. Thus, under the delayed second dose option, a campaign focus on generating a certain immunity in as great a number of people as fast as possible is preferable to having an almost perfect immunity in fewer people first. Therefore, based on these results, we suggest that the UK implemented a better vaccination campaign than that in the USA with regard to time between doses. The results presented here provide scientific guidelines for other countries where vaccination campaigns are just starting, or the percentage of vaccinated people is small. MDPI 2021-07-20 /pmc/articles/PMC8809086/ /pubmed/35118438 http://dx.doi.org/10.3390/epidemiologia2030021 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gonzalez-Parra, Gilberto
Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach
title Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach
title_full Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach
title_fullStr Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach
title_full_unstemmed Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach
title_short Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach
title_sort analysis of delayed vaccination regimens: a mathematical modeling approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8809086/
https://www.ncbi.nlm.nih.gov/pubmed/35118438
http://dx.doi.org/10.3390/epidemiologia2030021
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