Evidence map›Paper›PMID 32126190›Full record

ArticleJournal of the Royal Society, Interface2020

Population implications of the deployment of novel universal vaccines against epidemic and pandemic influenza.

N Arinaminpathy, S Riley, W S Barclay, C Saad-Roy, B Grenfell

Open access · bronzeAbstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

8 citing papers in PubMed, 17 citations in OpenAlex.

  1. The evolution of post-infection mortality.Proceedings. Biological sciences · 2024
    Article
  2. Epidemiological impacts of post-infection mortality.Proceedings. Biological sciences · 2023
    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

5 authors at 2 institutions in 2 countries.

N ArinaminpathyMRC Centre for Global Infectious Disease Analysis, Faculty of Medicine, Imperial College London, London, UK.
S RileyMRC Centre for Global Infectious Disease Analysis, Faculty of Medicine, Imperial College London, London, UK.
W S BarclayDepartment of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK.
C Saad-RoyLewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
B GrenfellDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
Imperial College London · GBPrinceton University · US

Funding

Medical Research Council MC_PC_19012Medical Research Council MR/J008761/1Medical Research Council MR/R015600/1Wellcome Trust 200861/Z/16/Z
6 · The paper itself

Abstract

There is increasing interest in the development of new, 'universal' influenza vaccines (UIVs) that--unlike current vaccines--are effective against a broad range of seasonal influenza strains, as well as against novel pandemic viruses. While the existing literature discusses the potential epidemiological benefits of UIVs, it is also important to anticipate their potential unintended population consequences. Using mathematical modelling, we illustrate two such types of adverse consequences. First, by reducing the amount of infection-induced immunity in a population without fully replacing it, a seasonal UIV programme may permit larger pandemics than in the absence of vaccination. Second, the more successful a future UIV programme is in reducing transmission of seasonal influenza, the more vulnerable the population could become to the emergence of a vaccine escape variant. These risks could be mitigated by optimal deployment of any future UIV vaccine: namely, the use of a combined vaccine formulation (incorporating conventional as well as multiple universal antigenic targets) and achieving sufficient population coverage to compensate for any reductions in infection-induced immunity. In the absence of large-scale trials of UIVs, disease-dynamic models can provide helpful, early insights into their potential impact. In future, data from continuing vaccine development will be invaluable in developing robustly predictive modelling approaches.

Indexed as

Influenza, HumanInfluenza VaccinesHumansPandemicsVaccinationInfluenza Vaccinesepidemiologyinfluenzamathematical modellingvaccine

Identifiers

PMID32126190
PMCPMC7115234
OpenAlexW3010007636

What OpenQuestion holds

Textmetadata
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.