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

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish (US)
Article number20190879
JournalJournal of the Royal Society Interface
Issue number164
StatePublished - Mar 1 2020

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biophysics
  • Biochemistry
  • Biotechnology
  • Biomedical Engineering
  • Biomaterials


  • Epidemiology
  • Influenza
  • Mathematical modelling
  • Vaccine


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