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Open Access Research article

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model

Timo Smieszek1*, Michael Balmer2, Jan Hattendorf34, Kay W Axhausen2, Jakob Zinsstag34 and Roland W Scholz1

Author Affiliations

1 Institute for Environmental Decisions, Natural and Social Science Interface, ETH Zurich, Universitaetsstrasse 22, 8092 Zurich, Switzerland

2 Institute for Transport Planning and Systems, ETH Zurich, Wolfgang-Pauli-Strasse 15, 8093 Zurich, Switzerland

3 Department of Public Health and Epidemiology, Swiss Tropical and Public Health Institute, Socinstrasse 57, 4002 Basel, Switzerland

4 University of Basel, P.O. Box, 4003 Basel, Switzerland

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BMC Infectious Diseases 2011, 11:115  doi:10.1186/1471-2334-11-115

Published: 9 May 2011

Abstract

Background

Simulation models of influenza spread play an important role for pandemic preparedness. However, as the world has not faced a severe pandemic for decades, except the rather mild H1N1 one in 2009, pandemic influenza models are inherently hypothetical and validation is, thus, difficult. We aim at reconstructing a recent seasonal influenza epidemic that occurred in Switzerland and deem this to be a promising validation strategy for models of influenza spread.

Methods

We present a spatially explicit, individual-based simulation model of influenza spread. The simulation model bases upon (i) simulated human travel data, (ii) data on human contact patterns and (iii) empirical knowledge on the epidemiology of influenza. For model validation we compare the simulation outcomes with empirical knowledge regarding (i) the shape of the epidemic curve, overall infection rate and reproduction number, (ii) age-dependent infection rates and time of infection, (iii) spatial patterns.

Results

The simulation model is capable of reproducing the shape of the 2003/2004 H3N2 epidemic curve of Switzerland and generates an overall infection rate (14.9 percent) and reproduction numbers (between 1.2 and 1.3), which are realistic for seasonal influenza epidemics. Age and spatial patterns observed in empirical data are also reflected by the model: Highest infection rates are in children between 5 and 14 and the disease spreads along the main transport axes from west to east.

Conclusions

We show that finding evidence for the validity of simulation models of influenza spread by challenging them with seasonal influenza outbreak data is possible and promising. Simulation models for pandemic spread gain more credibility if they are able to reproduce seasonal influenza outbreaks. For more robust modelling of seasonal influenza, serological data complementing sentinel information would be beneficial.