Influence of social contact patterns and demographic factors on influenza simulation results.
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. 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.



We 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.



When 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.



Contact 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.
GlaxoSmithKline Vaccines, Wavre, Belgium.
Ruprecht
R
Schmidt-Ott
ExploSYS GmbH, Otto-Hahn-Weg 6, 70771, Leinfelden-Echterdingen, Germany.
Markus
M
Schwehm
Department for Clinical Epidemiology and Applied Biometry, University of Tübingen, Silcherstr. 5, 72076, Tübingen, Germany. martin.eichner@uni-tuebingen.de.|Epimos GmbH, Uhlandstr. 3, 72144, Dusslingen, Germany. martin.eichner@uni-tuebingen.de.
Martin
M
Eichner
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D016358:Contact Tracing; D003710:Demography; D005060:Europe / Q000453:epidemiology; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D009980:Influenza A virus / Q000276:immunology; D009981:Influenza B virus / Q000276:immunology; D007251:Influenza, Human / Q000453:epidemiology* / Q000517:prevention & control / Q000635:transmission / Q000821:virology; D008875:Middle Aged; D015233:Models, Statistical; D012919:Social Behavior; D014611:Vaccination; D055815:Young Adult
4Flu; Influenza; Mathematical model; POLYMOD; Simulation; Vaccination