Evidence map›Paper›PMID 27977667›Full record

ArticlePLoS computational biology2016

Universal or Specific? A Modeling-Based Comparison of Broad-Spectrum Influenza Vaccines against Conventional, Strain-Matched Vaccines.

Rahul Subramanian, Andrea L Graham, Bryan T Grenfell, Nimalan Arinaminpathy

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

17 citing papers in PubMed, 32 citations in OpenAlex.

  1. Virosomes: Beyond Vaccines.Life (Basel, Switzerland) · 2025
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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

4 authors at 3 institutions in 2 countries.

Rahul SubramanianDepartment of Ecology and Evolution, University of Chicago, Chicago, Illinois, United States of America.
Andrea L GrahamDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.
Bryan T GrenfellDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.
Nimalan ArinaminpathyDepartment of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.
Princeton University · USImperial College London · GBUniversity of Chicago · US

Funding

Scientific and Technical Computing CoreP2CHD047879 · NICHD · PRINCETON UNIVERSITY · PI SALGANIK, MATTHEW J. · 2014 to 2018
$2.0M
Medical Research Council MR/K010174/1NICHD NIH HHS P2C HD047879
6 · The paper itself

Abstract

Despite the availability of vaccines, influenza remains a major public health challenge. A key reason is the virus capacity for immune escape: ongoing evolution allows the continual circulation of seasonal influenza, while novel influenza viruses invade the human population to cause a pandemic every few decades. Current vaccines have to be updated continually to keep up to date with this antigenic change, but emerging 'universal' vaccines-targeting more conserved components of the influenza virus-offer the potential to act across all influenza A strains and subtypes. Influenza vaccination programmes around the world are steadily increasing in their population coverage. In future, how might intensive, routine immunization with novel vaccines compare against similar mass programmes utilizing conventional vaccines? Specifically, how might novel and conventional vaccines compare, in terms of cumulative incidence and rates of antigenic evolution of seasonal influenza? What are their potential implications for the impact of pandemic emergence? Here we present a new mathematical model, capturing both transmission dynamics and antigenic evolution of influenza in a simple framework, to explore these questions. We find that, even when matched by per-dose efficacy, universal vaccines could dampen population-level transmission over several seasons to a greater extent than conventional vaccines. Moreover, by lowering opportunities for cross-protective immunity in the population, conventional vaccines could allow the increased spread of a novel pandemic strain. Conversely, universal vaccines could mitigate both seasonal and pandemic spread. However, where it is not possible to maintain annual, intensive vaccination coverage, the duration and breadth of immunity raised by universal vaccines are critical determinants of their performance relative to conventional vaccines. In future, conventional and novel vaccines are likely to play complementary roles in vaccination strategies against influenza: in this context, our results suggest important characteristics to monitor during the clinical development of emerging vaccine technologies.

Indexed as

Influenza, HumanModels, ImmunologicalModels, StatisticalPandemicsComputational BiologyHumansInfluenza A virusInfluenza VaccinesVaccinationInfluenza Vaccines

Identifiers

PMID27977667
PMCPMC5157952
OpenAlexW2566895100

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.