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This study introduces a novel network model to understand how epidemic spreading is affected by both static and dynamic network structures. The findings reveal that dynamic structures can significantly alter epidemic thresholds, impacting outbreak control strategies.

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Area of Science:

  • Complex Systems
  • Epidemiology
  • Network Science

Background:

  • Epidemic spreading and network evolution are intertwined dynamical processes.
  • Existing models often simplify network structures as purely static or time-varying.
  • A gap exists in understanding epidemic dynamics on networks with coupled static and dynamic structures.

Purpose of the Study:

  • To introduce a novel Static and Activity-Driven Coupling (SADC) network model.
  • To analyze the interplay between static ('strong') and dynamic ('weak') network structures on epidemic thresholds.
  • To investigate the influence of coupling strategies on epidemic dynamics for SIS and SIR models.

Main Methods:

  • Development of the SADC network model to represent coupled static and dynamic structures.
  • Analytical and numerical studies of epidemic thresholds for Susceptible-Infected-Susceptible (SIS) and Susceptible-Infected-Recovered (SIR) models.
  • Examination of epidemic thresholds under various coupling strategies and network degree distributions (homogeneous and heterogeneous).

Main Results:

  • The SADC model generalizes and recovers classical results for static and time-varying networks.
  • The 'weak' (dynamic) structure's impact on epidemic thresholds varies: it can lower thresholds in homogeneous networks but raise them in heterogeneous networks.
  • The 'weak' structure significantly influences epidemic outbreaks, suggesting its importance in real-world scenarios.

Conclusions:

  • The SADC model provides a comprehensive framework for studying epidemics on complex, evolving networks.
  • Dynamic network components play a crucial role in modulating epidemic spread, with context-dependent effects.
  • Understanding these coupled dynamics is essential for designing effective public health interventions and control strategies.