Estimating Direct and Indirect Protective Effect of Influenza Vaccination in the United States

Am J Epidemiol. 2017 Jul 1;186(1):92-100. doi: 10.1093/aje/kwx037.


With influenza vaccination rates in the United States recently exceeding 45% of the population, it is important to understand the impact that vaccination is having on influenza transmission. In this study, we used a Bayesian modeling approach, combined with a simple dynamical model of influenza transmission, to estimate this impact. The combined framework synthesized evidence from a range of data sources relating to influenza transmission and vaccination in the United States. We found that, for seasonal epidemics, the number of infections averted ranged from 9.6 million in the 2006-2007 season (95% credible interval (CI): 8.7, 10.9) to 37.2 million (95% CI: 34.1, 39.6) in the 2012-2013 season. Expressed in relative terms, the proportion averted ranged from 20.8% (95% CI: 16.8, 24.3) of potential infections in the 2011-2012 season to 47.5% (95% CI: 43.7, 50.8) in the 2008-2009 season. The percentage averted was only 1.04% (95% CI: 0.15, 3.2) for the 2009 H1N1 pandemic, owing to the late timing of the vaccination program in relation to the pandemic in the Northern hemisphere. In the future, further vaccination coverage, as well as improved influenza vaccines (especially those offering better protection in the elderly), could have an even stronger effect on annual influenza epidemics.

Keywords: Bayesian inference; herd immunity; influenza; mathematical modeling; vaccination.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Bayes Theorem
  • Child
  • Child, Preschool
  • Female
  • Humans
  • Infant
  • Influenza A Virus, H1N1 Subtype
  • Influenza Vaccines / administration & dosage*
  • Influenza, Human / epidemiology*
  • Influenza, Human / prevention & control*
  • Male
  • Middle Aged
  • Seasons
  • United States
  • Young Adult


  • Influenza Vaccines