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. © 2011 Blower and Go; licensee BioMed Central Ltd.
CITATION STYLE
Blower, S., & Go, M. H. (2011, July 19). The importance of including dynamic social networks when modeling epidemics of airborne infections: Does increasing complexity increase accuracy? BMC Medicine. https://doi.org/10.1186/1741-7015-9-88
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