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Open Access Research article

Leveraging H1N1 infection transmission modeling with proximity sensor microdata

Mohammad Hashemian1, Kevin Stanley1* and Nathaniel Osgood12

Author Affiliations

1 Department of Computer Science, University of Saskatchewan, Saskatoon, Canada

2 Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, Canada

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BMC Medical Informatics and Decision Making 2012, 12:35  doi:10.1186/1472-6947-12-35

Published: 2 May 2012

Abstract

Background

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. 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.

Methods

In 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.

Results

We 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.

Conclusions

Through 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.