Journal of Statistical Physics

, Volume 151, Issue 1–2, pp 355–366 | Cite as

Epidemics in Adaptive Social Networks with Temporary Link Deactivation



Disease spread in a society depends on the topology of the network of social contacts. Moreover, individuals may respond to the epidemic by adapting their contacts to reduce the risk of infection, thus changing the network structure and affecting future disease spread. We propose an adaptation mechanism where healthy individuals may choose to temporarily deactivate their contacts with sick individuals, allowing reactivation once both individuals are healthy. We develop a mean-field description of this system and find two distinct regimes: slow network dynamics, where the adaptation mechanism simply reduces the effective number of contacts per individual, and fast network dynamics, where more efficient adaptation reduces the spread of disease by targeting dangerous connections. Analysis of the bifurcation structure is supported by numerical simulations of disease spread on an adaptive network. The system displays a single parameter-dependent stable steady state and non-monotonic dependence of connectivity on link deactivation rate.


Adaptive network Dynamical network SIS model Link deactivation 



This work was supported by the Army Research Office, Air Force Office of Scientific Research, and by Award Number R01GM090204 from the National Institute of General Medical Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.


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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Ilker Tunc
    • 1
  • Maxim S. Shkarayev
    • 2
  • Leah B. Shaw
    • 1
  1. 1.Department of Applied ScienceCollege of William and MaryWilliamsburgUSA
  2. 2.Department of Physics & AstronomyIowa State UniversityAmesUSA

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