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Open Access Research article

4Flu - an individual based simulation tool to study the effects of quadrivalent vaccination on seasonal influenza in Germany

Martin Eichner12*, Markus Schwehm3, Johannes Hain4, Helmut Uphoff5, Bernd Salzberger6, Markus Knuf7 and Ruprecht Schmidt-Ott8

Author Affiliations

1 Department for Clinical Epidemiology and Applied Biometry, University of Tübingen, Silcherstr. 5, 72076 Tübingen, Germany

2 Epimos GmbH, Uhlandstr. 3, 72144 Dusslingen, Germany

3 ExploSYS GmbH, Otto-Hahn-Weg 6, 70771 Leinfelden-Echterdingen, Germany

4 GlaxoSmithKline GmbH & Co. KG, Prinzregentenplatz 9, 81675 München, Germany

5 Hessisches Landesprüfungs- und Untersuchungsamt im Gesundheitswesen, Zentrum für Gesundheitsschutz, Wolframstr. 33, 35683 Dillenburg, Germany

6 Klinik f. Innere Medizin, Universitätsklinikum Regensburg, 93042 Regensburg, Germany

7 Dr. Horst Schmidt Klinik, Klinik für Kinder und Jugendliche, Ludwig-Erhard-Str. 100, 65199 Wiesbaden, Germany

8 GlaxoSmithKline Vaccines, Wavre, Belgium

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BMC Infectious Diseases 2014, 14:365  doi:10.1186/1471-2334-14-365

Published: 3 July 2014

Abstract

Background

Influenza vaccines contain Influenza A and B antigens and are adjusted annually to match the characteristics of circulating viruses. In Germany, Influenza B viruses belonged to the B/Yamagata lineage, but since 2001, the antigenically distinct B/Victoria lineage has been co-circulating. Trivalent influenza vaccines (TIV) contain antigens of the two A subtypes A(H3N2) and A(H1N1), yet of only one B lineage, resulting in frequent vaccine mismatches. Since 2012, the WHO has been recommending vaccine strains from both B lineages, paving the way for quadrivalent influenza vaccines (QIV).

Methods

Using an individual-based simulation tool, we simulate the concomitant transmission of four influenza strains, and compare the effects of TIV and QIV on the infection incidence. Individuals are connected in a dynamically evolving age-dependent contact network based on the POLYMOD matrix; their age-distribution reproduces German demographic data and predictions. The model considers maternal protection, boosting of existing immunity, loss of immunity, and cross-immunizing events between the B lineages. Calibration to the observed annual infection incidence of 10.6% among young adults yielded a basic reproduction number of 1.575. Vaccinations are performed annually in October and November, whereby coverage depends on the vaccinees’ age, their risk status and previous vaccination status. New drift variants are introduced at random time points, leading to a sudden loss of protective immunity for part of the population and occasionally to reduced vaccine efficacy. Simulations run for 50 years, the first 30 of which are used for initialization. During the final 20 years, individuals receive TIV or QIV, using a mirrored simulation approach.

Results

Using QIV, the mean annual infection incidence can be reduced from 8,943,000 to 8,548,000, i.e. by 395,000 infections, preventing 11.2% of all Influenza B infections which still occur with TIV (95% CI: 10.7-11.8%). Using a lower B lineage cross protection than the baseline 60%, the number of Influenza B infections increases and the number additionally prevented by QIV can be 5.5 times as high.

Conclusions

Vaccination with TIV substantially reduces the Influenza incidence compared to no vaccination. Depending on the assumed degree of B lineage cross protection, QIV further reduces Influenza B incidence by 11-33%.

Keywords:
Influenza; Vaccination; Simulation; Mathematical model