Abstract
BackgroundThe demographic composition and the frequency and nature of social contacts may affect the spread of influenza virus in a population, resulting in distinct age-dependent immunity patterns. As demography and social contact rates differ strongly between European countries, this may impact infection incidence and vaccine effectiveness and thus limit the extent to which conclusions derived from observations in one country can be generalized to others. In the current study, we aimed to decipher the impact of social contact patterns and demographic factors on simulation results and, thus, to determine to what extent vaccination results can be generalized.MethodsWe simulated the transmission of four influenza strains (A(H1N1), A(H3N2), B/Victoria, B/Yamagata) in Belgium, Finland, Germany, GB, Italy, Luxembourg, Netherlands and Poland, using the simulation tool 4Flu. Individuals were connected in a dynamically evolving age-dependent contact network based on the POLYMOD study.ResultsWhen averaged over 20 years, simulation results without vaccination ranged from annually 20,984 (Germany) to 31,322 infections (Italy) per 100,000 individuals. QIV annually prevented 1758 (Poland) to 7720 infections (Germany) per 100,000. Variability of prevented cases remained high when the country-specific vaccination was replaced by unified coverage, but was reduced considerably if the same demography was used for all countries, or even more so when the same contact matrix was used.ConclusionsContact matrix and demography strongly influence the age-dependent incidence of influenza and the success of vaccination. Projecting simulation results from one country to another can, therefore, lead to erroneous results.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-016-1981-5) contains supplementary material, which is available to authorized users.
Highlights
The demographic composition and the frequency and nature of social contacts may affect the spread of influenza virus in a population, resulting in distinct age-dependent immunity patterns
We examined the influence of contact patterns and demography on the epidemiology of influenza by using the previously published tool 4Flu which simulates the simultaneous and independent transmission of four influenza strains (A(H1N1), A(H3N2), B/Victoria, B/Yamagata) in a population with demographic turnover [4, 5]
Initialization and evaluation period Each simulation ran for 40 years: during the first 20 years, the age-dependent immunity pattern of the population was initialized by applying trivalent influenza (TIV) vaccinations and allowing for independent transmission of four influenza strains A(H1N1), A(H3N2), B/Victoria and B/Yamagata in populations with demographic turnover
Summary
The demographic composition and the frequency and nature of social contacts may affect the spread of influenza virus in a population, resulting in distinct age-dependent immunity patterns. We aimed to decipher the impact of social contact patterns and demographic factors on simulation results and, to determine to what extent vaccination results can be generalized. Vaccine efficacy from previous years may not be fully applicable for subsequent years This problem has been recognized and led the European Medicine Agency (EMA) to draft guidelines for influenza vaccines (EMA/CHMP/VWP/ 457259/2014) in which they request that vaccine effectiveness (VE) for individual influenza vaccines should routinely be investigated. The epidemiology of influenza should be influenced by the demographic composition of the population and by age-dependent immunity patterns. Individuals were connected in a dynamically evolving agedependent contact network based on the contact structures which were determined in the EU POLYMOD study [1]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
