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

Abstract

BackgroundSimulation 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.MethodsWe 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.ResultsThe 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.ConclusionsWe 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.

Collecting close-contact social mixing data with contact diaries: reporting errors and biases

“…Empirical research on potentially contagious contacts, particularly the highly cited study by Mossong et al [8], has influenced the discussion on the patterns and risk factors of disease spread and has informed infectious disease modelling [e.g. 13]. In addition, various studies have shown that empirical contact data can successfully be applied in epidemiological models to replicate serological data [14][15][16].…”

Social Networks and Travel Behaviour

“…Examples for circulating artefacts are feelings (Christakis and Fowler 2009), styles and behaviours (Gladwell 2002), as well as contagious diseases (Smieszek et al 2011). At least some of these shared artefacts facilitate a joint group identity and are thus a source for social coherence (Scott 2007;Mewes 2009;Trappmann et al 2011).…”

Temporal and Spatial Monitoring and Prediction of Epidemic Outbreaks

“…(15) In this notation represents the th component of vector . Process noise in (15) is assumed to be zero-mean white Gaussian with a diagonal covariance matrix of size .…”

“…The 2003/2004 influenza season in Switzerland is a clear instance of such pattern. The first wave of the epidemic appears in the final weeks of 2003 in Geneva, and then spreads to nearby cities in the following weeks [15].…”

“…In practice, however, the different time-series under study may be influenced by common confounding factors, and so they are likely to be correlated [14]. For example an infectious agent in one region could spread to neighboring areas through various forms of transition pathways, with similar peak and duration [15].…”