The online version of this article (doi:10.1186/s12879-017-2344-6) contains supplementary material, which is available to authorized users.
To reduce the burden of severe influenza, most industrialized countries target specific risk-groups with influenza vaccines, e.g. the elderly or individuals with comorbidities. Since children are the main spreaders, some countries have recently implemented childhood vaccination programs to reduce overall virus transmission and thereby influenza disease in the whole population. The introduction of childhood vaccination programs was often supported by modelling studies that predicted substantial incidence reductions. We developed a mathematical transmission model to examine the potential impact of childhood influenza vaccination in Germany, while also challenging established modelling assumptions.
We developed an age-stratified SEIR-type transmission model to reproduce the epidemic influenza seasons between 2003/04 and 2013/14. The model was built upon German population counts, contact patterns, and vaccination history and was fitted to seasonal data on influenza-attributable medically attended acute respiratory infections (I-MAARI) and strain distribution using Bayesian methods. As novelties we (i) implemented a stratified model structure enabling seasonal variability and (ii) deviated from the commonly assumed mass-action-principle by employing a phenomenological transmission rate.
According to the model, by vaccinating primarily the elderly over ten seasons 4 million (95% prediction interval: 3.84 – 4.19) I-MAARI were prevented which corresponds to an 8.6% (8.3% – 8.9%) reduction compared to a no-vaccination scenario and a number-needed-to-vaccinate (NNV) to prevent one I-MAARI of 37.1 (35.5 – 38.7). Additional vaccination of 2-10 year-old children at 40% coverage would have led to an overall I-MAARI reduction of 17.8% (17.1 – 18.7%) mostly due to indirect effects with a NNV of 20.7 (19.6 – 21.6). When employing the traditional mass-action-principle, the model predicted a more than 3-fold higher I-MAARI reduction (55.6%) due to childhood vaccination.
In Germany, the introduction of routine childhood influenza vaccination could considerably reduce I-MAARI among all age-groups and improve the NNV. However, the predicted impact is much lower compared to previous studies, which is primarily caused by our phenomenological approach to modelling influenza virus transmission.
WHO. Influenza vaccines—WHO position paper. Wkly Epidemiol Rec. 2005;33:279–87.
ECDC. ECDC Vaccination schedule. [cited 2016; Available from: http://vaccine-schedule.ecdc.europa.eu/Pages/Scheduler.aspx.
Jefferson, T., et al., Vaccines for preventing influenza in healthy adults, in Cochrane Database Syst Rev. 2010.
Grohskopf LA, et al. Prevention and Control of Influenza with Vaccines: Recommendations of the Advisory Committee on Immunization Practices. Morb Mortal Wkly Rep. 2015;64(30):818–25. CrossRef
Pebody, R.G., et al., Uptake and impact of vaccinating school age children against influenza during a season with circulation of drifted influenza A and B strains, England, 2014/15. Euro Surveill, 2015. 20(39).
Spier R, et al. 3rd Vaccine Global Congress, Singapore 2009. Assessing Herd Immunity in the Elderly Following the Vaccination of School Children with Live Attenuated Trivalent Influenza Vaccine (LAIV): A County-Level Analysis. Proc Vaccinol. 2010;2(1):92–100. CrossRef
Flannery B. LAIV vs IIV effectiveness: Summary of evidence since 2009. In: Presented at: Centers for Disease Control and Prevention Advisory Committee on Immunization Practices (ACIP) Meeting; 2016 Jun 22–23; Atlanta, GA; 2016.
Centers for Disease Control and Prevention. ACIP votes down use of LAIV for 2016-2017 flu season. 2016.
Rose MA, et al. The epidemiological impact of childhood influenza vaccination using live-attenuated influenza vaccine (LAIV) in Germany: predictions of a simulation study. BMC Infect Dis. 2014;14:40-40.
an der Heiden M, et al. Estimates of Excess Medically Attended Acute Respiratory Infections in Periods of Seasonal and Pandemic Influenza in Germany from 2001/02 to 2010/11. PLoS One. 2013;8(7):e64593. CrossRef
Buda S, et al. Bericht zur in Deutschland Saison 2013/14. Berlin: Robert Koch-Institut; 2014.
Buchholz, U., A. Grüber, and B. Schweiger. Abschlussbericht der Influenzasaison. Robert Koch-Institut
STIKO. Statement of the German Standing Committee on Vaccination at the RKI: Recommendations of the Standing Committee on Vaccination (STIKO) at the Robert Koch Institute. Epidemiol Bull. 2015;34:327–62.
Jefferson, T., et al., Vaccines for preventing influenza in healthy adults. Cochrane Database Syst Rev, 2010 (7).
Jefferson, T., et al., Vaccines for preventing influenza in healthy children. Cochrane Database Syst Rev, 2008(2).
Kissling, E., et al., Influenza vaccine effectiveness estimates in Europe in a season with three influenza type/subtypes circulating: the I-MOVE multicentre case-control study, influenza season 2012/13. Euro Surveill. 2014, 19(6).
Kissling, E., et al., "I-MOVE" towards monitoring seasonal and pandemic influenza vaccine effectiveness: lessons learnt from a pilot multi-centric case-control study in Europe, 2008-9. Euro Surveill, 2009. 14(44).
Kissling, E., et al., Low and decreasing vaccine effectiveness against influenza A(H3) in 2011/12 among vaccination target groups in Europe: results from the I-MOVE multicentre case-control study. Euro Surveill. 18(5).
Tricco AC, et al. Comparing influenza vaccine efficacy against mismatched and matched strains: a systematic review and meta-analysis. BMC Med. 2013;11(1):1–19. CrossRef
Eichner M, et al. 4Flu - an individual based simulation tool to study the effects of quadrivalent vaccination on seasonal influenza in Germany. BMC Infect Dis. 2014;14:365-365. CrossRef
Gelman A, Hill J. Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press; 2006.
Finkenstädt BF, Grenfell BT. Time series modelling of childhood diseases: a dynamical systems approach. J R Stat Soc: Ser C: Appl Stat. 2000;49(2):187–205. CrossRef
Chowell G, et al. Characterizing the reproduction number of epidemics with early subexponential growth dynamics. J R Soc Interface. 2016;13
Kong, L., et al., Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model. Int J Environ Res Public Health, 2016. 13(3).
Goeyvaerts N, et al. Estimating Infectious Disease Parameters from Data on Social Contacts and Serological Status. J Royal Stat Soc Ser C Appl Stat. 2010;59(2):255–77. CrossRef
Eames K, et al. The impact of illness and the impact of school closure on social contact patterns. Health Technol Assess. 2010:14(34).
Kass RE, Raftery AE. Bayes Factors. J Am Stat Assoc. 1995;90(430):773–95. CrossRef
Hardelid, P., et al., Assessment of baseline age-specific antibody prevalence and incidence of infection to novel influenza A H1N1 2009. Health Technol Assess, 2011. 14(55).
Bayer, C., et al., Internet-based syndromic monitoring of acute respiratory illness in the general population of Germany, weeks 35/2011 to 34/2012. Euro Surveill., 2014 19(4).
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