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Controlling contagion processes in activity driven networks.

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Area of Science:

  • Complex systems
  • Network science
  • Epidemiology

Background:

  • Traditional contagion control models often assume static networks.
  • Real-world networks are dynamic, evolving with contagion processes.
  • Activity-driven networks represent a key class of time-varying systems.

Purpose of the Study:

  • To develop an analytical framework for contagion control on activity-driven networks.
  • To derive the critical immunization threshold for these dynamic systems.
  • To assess the efficacy of different control strategies in coevolving networks.

Main Methods:

  • Developed a block variable mean-field approach.
  • Derived equations for the coevolution of contagion and network dynamics.
  • Validated the framework using numerical simulations and a mobile call data set.

Main Results:

  • The framework successfully models contagion and network dynamics coevolution.
  • Derived the critical immunization threshold for activity-driven networks.
  • Evaluated and compared the effectiveness of three distinct control strategies.

Conclusions:

  • The proposed analytical framework is effective for studying contagion control on dynamic networks.
  • The findings provide insights into optimizing control strategies for real-world evolving systems.
  • Validation with real-world data confirms the model's applicability and accuracy.