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Optimizing Vaccine Allocation at Different Points in Time during an Epidemic

BACKGROUND: Pandemic influenza A(H1N1) 2009 began spreading around the globe in April of 2009 and vaccination started in October of 2009. In most countries, by the time vaccination started, the second wave of pandemic H1N1 2009 was already under way. With limited supplies of vaccine, we are left to...

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Autores principales: Matrajt, Laura, Longini, Ira M.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2978681/
https://www.ncbi.nlm.nih.gov/pubmed/21085686
http://dx.doi.org/10.1371/journal.pone.0013767
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author Matrajt, Laura
Longini, Ira M.
author_facet Matrajt, Laura
Longini, Ira M.
author_sort Matrajt, Laura
collection PubMed
description BACKGROUND: Pandemic influenza A(H1N1) 2009 began spreading around the globe in April of 2009 and vaccination started in October of 2009. In most countries, by the time vaccination started, the second wave of pandemic H1N1 2009 was already under way. With limited supplies of vaccine, we are left to question whether it may be a good strategy to vaccinate the high-transmission groups earlier in the epidemic, but it might be a better use of resources to protect instead the high-risk groups later in the epidemic. To answer this question, we develop a deterministic epidemic model with two age-groups (children and adults) and further subdivide each age group in low and high risk. METHODS AND FINDINGS: We compare optimal vaccination strategies started at various points in time in two different settings: a population in a developed country where children account for 24% of the population, and a population in a less developed country where children make up the majority of the population, 55%. For each of these populations, we minimize mortality or hospitalizations and we find an optimal vaccination strategy that gives the best vaccine allocation given a starting vaccination time and vaccine coverage level. We find that population structure is an important factor in determining the optimal vaccine distribution. Moreover, the optimal policy is dynamic as there is a switch in the optimal vaccination strategy at some time point just before the peak of the epidemic. For instance, with 25% vaccine coverage, it is better to protect the high-transmission groups before this point, but it is optimal to protect the most vulnerable groups afterward. CONCLUSIONS: Choosing the optimal strategy before or early in the epidemic makes an important difference in minimizing the number of influenza infections, and consequently the number of influenza deaths or hospitalizations, but the optimal strategy makes little difference after the peak.
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spelling pubmed-29786812010-11-17 Optimizing Vaccine Allocation at Different Points in Time during an Epidemic Matrajt, Laura Longini, Ira M. PLoS One Research Article BACKGROUND: Pandemic influenza A(H1N1) 2009 began spreading around the globe in April of 2009 and vaccination started in October of 2009. In most countries, by the time vaccination started, the second wave of pandemic H1N1 2009 was already under way. With limited supplies of vaccine, we are left to question whether it may be a good strategy to vaccinate the high-transmission groups earlier in the epidemic, but it might be a better use of resources to protect instead the high-risk groups later in the epidemic. To answer this question, we develop a deterministic epidemic model with two age-groups (children and adults) and further subdivide each age group in low and high risk. METHODS AND FINDINGS: We compare optimal vaccination strategies started at various points in time in two different settings: a population in a developed country where children account for 24% of the population, and a population in a less developed country where children make up the majority of the population, 55%. For each of these populations, we minimize mortality or hospitalizations and we find an optimal vaccination strategy that gives the best vaccine allocation given a starting vaccination time and vaccine coverage level. We find that population structure is an important factor in determining the optimal vaccine distribution. Moreover, the optimal policy is dynamic as there is a switch in the optimal vaccination strategy at some time point just before the peak of the epidemic. For instance, with 25% vaccine coverage, it is better to protect the high-transmission groups before this point, but it is optimal to protect the most vulnerable groups afterward. CONCLUSIONS: Choosing the optimal strategy before or early in the epidemic makes an important difference in minimizing the number of influenza infections, and consequently the number of influenza deaths or hospitalizations, but the optimal strategy makes little difference after the peak. Public Library of Science 2010-11-11 /pmc/articles/PMC2978681/ /pubmed/21085686 http://dx.doi.org/10.1371/journal.pone.0013767 Text en Matrajt, Longini. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Matrajt, Laura
Longini, Ira M.
Optimizing Vaccine Allocation at Different Points in Time during an Epidemic
title Optimizing Vaccine Allocation at Different Points in Time during an Epidemic
title_full Optimizing Vaccine Allocation at Different Points in Time during an Epidemic
title_fullStr Optimizing Vaccine Allocation at Different Points in Time during an Epidemic
title_full_unstemmed Optimizing Vaccine Allocation at Different Points in Time during an Epidemic
title_short Optimizing Vaccine Allocation at Different Points in Time during an Epidemic
title_sort optimizing vaccine allocation at different points in time during an epidemic
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2978681/
https://www.ncbi.nlm.nih.gov/pubmed/21085686
http://dx.doi.org/10.1371/journal.pone.0013767
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