Evidence map›Paper›PMID 26658016›Full record

ArticleScientific reports2015

Strategies for Early Vaccination During Novel Influenza Outbreaks.

M Laskowski, Y Xiao, N Charland, S M Moghadas

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
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

4 authors.

M LaskowskiAgent-Based Modelling Laboratory, York University, Toronto, Ontario, Canada M3J 1P3.
Y XiaoDepartment of Mathematical Sciences, University of Cincinnati, Cincinnati OH, 45221 USA.
N CharlandMedicago Inc., 1020 Route de l'Église, Bureau 600, Quebec, Quebec, Canada GIV 3V9.
S M MoghadasAgent-Based Modelling Laboratory, York University, Toronto, Ontario, Canada M3J 1P3.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ongoing research and technology developments hold the promise of rapid production and large-scale deployment of strain-specific or cross-protective vaccines for novel influenza viruses. We sought to investigate the impact of early vaccination on age-specific attack rates and evaluate the outcomes of different vaccination strategies that are influenced by the level of single or two-dose vaccine-induced protections. We developed and parameterized an agent-based model for two population demographics of urban and remote areas in Canada. Our results demonstrate that there is a time period before and after the onset of epidemic, during which the outcomes of vaccination strategies may differ significantly and are highly influenced by demographic characteristics. For the urban population, attack rates were lowest for children younger than 5 years of age in all vaccination strategies. However, for the remote population, the lowest attack rates were obtained for adults older than 50 years of age in most strategies. We found that the reduction of attack rates following the start of vaccination campaigns during the epidemic depends critically on the disease transmissibility, suggesting that for a sufficiently high transmissibility, vaccine delivery after the onset of epidemic has little or no effect, regardless of the population demographics.

Indexed as

AdolescentAdultCanadaChildChild, PreschoolDisease OutbreaksFemaleHumansInfantInfant, NewbornInfluenza A virusInfluenza, HumanInfluenza VaccinesMaleMiddle AgedPandemicsInfluenza Vaccines

Identifiers

PMID26658016
PMCPMC4677284

What OpenQuestion holds

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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.