Abstract
BackgroundThe contact networks between individuals can have a profound impact on the evolution of an infectious outbreak within a network. The impact of the interaction between contact network and disease dynamics on infection spread has been investigated using both synthetic and empirically gathered micro-contact data, establishing the utility of micro-contact data for epidemiological insight. However, the infection models tied to empirical contact data were highly stylized and were not calibrated or compared against temporally coincident infection rates, or omitted critical non-network based risk factors such as age or vaccination status.MethodsIn this paper we present an agent-based simulation model firmly grounded in disease dynamics, incorporating a detailed characterization of the natural history of infection, and 13 weeks worth of micro-contact and participant health and risk factor information gathered during the 2009 H1N1 flu pandemic.ResultsWe demonstrate that the micro-contact data-based model yields results consistent with the case counts observed in the study population, derive novel metrics based on the logarithm of the time degree for evaluating individual risk based on contact dynamic properties, and present preliminary findings pertaining to the impact of internal network structures on the spread of disease at an individual level.ConclusionsThrough the analysis of detailed output of Monte Carlo ensembles of agent based simulations we were able to recreate many possible scenarios of infection transmission using an empirically grounded dynamic contact network, providing a validated and grounded simulation framework and methodology. We confirmed recent findings on the importance of contact dynamics, and extended the analysis to new measures of the relative risk of different contact dynamics. Because exponentially more time spent with others correlates to a linear increase in infection probability, we conclude that network dynamics have an important, but not dominant impact on infection transmission for H1N1 transmission in our study population.
Highlights
The contact networks between individuals can have a profound impact on the evolution of an infectious outbreak within a network
While other contact datasets are available [6,12], and others with health information have been described [13], Flunet is unique in providing longitudinal information on contact patterns, occurrence of influenza-like illness (ILI) symptomology, and vaccination status obtained during an epidemic outbreak in the region under observation
Because time degree aggregates over contact times, and because contact times are often characterized by exponential or power law distributions Contact Data Section [6,10,15], we introduce log time degree (LTD) centrality as a measure of contact density
Summary
The contact networks between individuals can have a profound impact on the evolution of an infectious outbreak within a network. The impact of the interaction between contact network and disease dynamics on infection spread has been investigated using both synthetic and empirically gathered micro-contact data, establishing the utility of micro-contact data for epidemiological insight. Data collected by contact tracing [3] and self-reporting [4] has provided some important insights into network structure for many notifiable illnesses. Even for the best models, contact data depends heavily on unreliable self-reporting data collection methodologies [5] which omit much detail and place a substantial recording burden on participants [4]. While self-reported measures can provide insight, some leading studies have noted the desirability of employing automated data-collection approaches to capture higher fidelity contact frequency and duration information [4]
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