title: The importance of including dynamic social networks when modeling epidemics of airborne infections: does increasing complexity increase accuracy?
abstract: 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.
authors.forename: Sally
authors.initials: S
authors.lastname: Blower
authors.forename: Myong-Hyun
authors.initials: MH
authors.lastname: Go
mesh_terms: D003141:Communicable Diseases / Q000453:epidemiology* / Q000635:transmission*; D016358:Contact Tracing / Q000379:methods*; D004196:Disease Outbreaks; D006801:Humans