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The importance of including dynamic social networks when modeling epidemics of airborne infections: does increasing complexity increase accuracy?

Sally Blower* and Myong-Hyun Go

Author Affiliations

Center for Biomedical Modeling, Semel Institute of Neuroscience & Human Behavior, David Geffen School of Medicine, University of California at Los Angeles, 10940 Wilshire Blvd, Suite 1450, Los Angeles, CA 90024, USA

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BMC Medicine 2011, 9:88  doi:10.1186/1741-7015-9-88

Published: 19 July 2011


Mathematical models are useful tools for understanding and predicting epidemics. A recent innovative modeling study by Stehle and colleagues addressed the issue of how complex models need to be to ensure accuracy. The authors collected data on face-to-face contacts during a two-day conference. They then constructed a series of dynamic social contact networks, each of which was used to model an epidemic generated by a fast-spreading airborne pathogen. Intriguingly, Stehle and colleagues found that increasing model complexity did not always increase accuracy. Specifically, the most detailed contact network and a simplified version of this network generated very similar results. These results are extremely interesting and require further exploration to determine their generalizability.

Please see related article BMC Medicine, 2011, 9:87