Leveraging H1N1 infection transmission modeling with proximity sensor microdata

BMC Medical Informatics and Decision Making

Research Article

Mohammad Hashemian¹  Kevin Stanley¹  Nathaniel Osgood² 

[1] Department of Computer Science, University of Saskatchewan, Saskatoon, Canada;Department of Computer Science, University of Saskatchewan, Saskatoon, Canada;Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, Canada;

关键词: Degree Centrality;    Contact Network;    Contact Pattern;    Contact Duration;    Complementary Cumulative Distribution Function;   

DOI  :  10.1186/1472-6947-12-35

 received in 2011-11-16, accepted in 2012-05-02,  发布年份 2012

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Hashemian et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.