Evidence map›Paper›PMID 21085686›Full record

ArticlePloS one2010

Optimizing vaccine allocation at different points in time during an epidemic.

Laura Matrajt, Ira M Longini

Abstract read
In one paragraph

Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed.

  1. Article
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  7. Multi-period vaccine allocation model in a pandemic: A case study of COVID-19 in Australia.Transportation research. Part E, Logistics and transportation review · 2022
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  13. Vaccine optimization for COVID-19: who to vaccinate first?medRxiv : the preprint server for health sciences · 2020
    Article
  14. Article
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  17. Dose-Optimal Vaccine Allocation over Multiple Populations.Production and operations management · 2018
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  18. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Laura MatrajtDepartment of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.
Ira M Longini

Funding

Containing Bioterrorist and Emerging Infectious DiseasesU01GM070749 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI HALLORAN, M ELIZABETH, LONGINI, IRA M · 2004 to 2013
$8.3M
METHODS FOR EVALUATING VACCINE EFFICACYR01AI032042 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI HALLORAN, M ELIZABETH · 1992 to 2009
$3.0M
NIAID NIH HHS R01 AI032042NIAID NIH HHS R01-AI32042NIGMS NIH HHS U01 GM070749NIGMS NIH HHS U01-GM070749
6 · The paper itself

Abstract

backgroundPandemic 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

findingsWe 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.

conclusionsChoosing 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.

Indexed as

AdultAlgorithmsChildDeveloped CountriesDeveloping CountriesEpidemicsHumansInfluenza A Virus, H1N1 SubtypeInfluenza, HumanInfluenza VaccinesModels, BiologicalTime FactorsVaccinationInfluenza Vaccines

Identifiers

PMID21085686
PMCPMC2978681

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.