Evidence map›Paper›PMID 25494180›Full record

ArticlePloS one2014

The Possible Impact of Vaccination for Seasonal Influenza on Emergence of Pandemic Influenza via Reassortment.

Xu-Sheng Zhang, Richard Pebody, Daniela De Angelis, Peter J White, Andre Charlett, John W McCauley

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 29% of its field
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

6 citing papers in PubMed, 10 citations in OpenAlex.

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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

6 authors at 3 institutions in 1 country.

Xu-Sheng ZhangModelling and Economics Unit, Centre for Infectious Disease Surveillance and Control, Public Health England, London, United Kingdom; Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College School of Public Health, London, United Kingdom.
Richard PebodyRespiratory Diseases Department, Centre for Infectious Disease Surveillance and Control, Public Health England, London, United Kingdom.
Daniela De AngelisStatistics Unit, Centre for Infectious Disease Surveillance and Control, Public Health England, London, United Kingdom; Medical Research Council Biostatistics Unit, University Forvie Site, Cambridge, United Kingdom.
Peter J WhiteModelling and Economics Unit, Centre for Infectious Disease Surveillance and Control, Public Health England, London, United Kingdom; Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College School of Public Health, London, United Kingdom; NIHR Health Protection Research Unit in Modelling Methodology, Department of Infectious Disease Epidemiology, Imperial College School of Public Health, London, United Kingdom.
Andre CharlettStatistics Unit, Centre for Infectious Disease Surveillance and Control, Public Health England, London, United Kingdom.
John W McCauleyMedical Research Council National Institute for Medical Research, Mill Hill, London, United Kingdom.
Public Health England · GBMedical Research Council · GBMRC Biostatistics Unit · GB

Funding

Medical Research Council MC_U105260556Medical Research Council MR/K010174/1
6 · The paper itself

Abstract

backgroundOne pathway through which pandemic influenza strains might emerge is reassortment from coinfection of different influenza A viruses. Seasonal influenza vaccines are designed to target the circulating strains, which intuitively decreases the prevalence of coinfection and the chance of pandemic emergence due to reassortment. However, individual-based analyses on 2009 pandemic influenza show that the previous seasonal vaccination may increase the risk of pandemic A(H1N1) pdm09 infection. In view of pandemic influenza preparedness, it is essential to understand the overall effect of seasonal vaccination on pandemic emergence via reassortment. METHODS AND

findingsIn a previous study we applied a population dynamics approach to investigate the effect of infection-induced cross-immunity on reducing such a pandemic risk. Here the model was extended by incorporating vaccination for seasonal influenza to assess its potential role on the pandemic emergence via reassortment and its effect in protecting humans if a pandemic does emerge. The vaccination is assumed to protect against the target strains but only partially against other strains. We find that a universal seasonal vaccine that provides full-spectrum cross-immunity substantially reduces the opportunity of pandemic emergence. However, our results show that such effectiveness depends on the strength of infection-induced cross-immunity against any novel reassortant strain. If it is weak, the vaccine that induces cross-immunity strongly against non-target resident strains but weakly against novel reassortant strains, can further depress the pandemic emergence; if it is very strong, the same kind of vaccine increases the probability of pandemic emergence.

conclusionsTwo types of vaccines are available: inactivated and live attenuated, only live attenuated vaccines can induce heterosubtypic immunity. Current vaccines are effective in controlling circulating strains; they cannot always help restrain pandemic emergence because of the uncertainty of the oncoming reassortant strains, however. This urges the development of universal vaccines for prevention of pandemic influenza.

Indexed as

Cross ReactionsHumansInfluenza A Virus, H1N1 SubtypeInfluenza, HumanInfluenza VaccinesModels, TheoreticalPandemicsPopulation SurveillanceReassortant VirusesSeasonsInfluenza Vaccines

Identifiers

PMID25494180
PMCPMC4262424
OpenAlexW2050729405

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.