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Epidemic spreading on activity-driven networks with attractiveness.

Iacopo Pozzana1, Kaiyuan Sun2, Nicola Perra3

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This study explores epidemic spreading on dynamic networks using an activity-driven model. Heterogeneous attractiveness and correlations between activity and attractiveness significantly impact contagion, either facilitating or hampering disease spread.

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Modeling

Background:

  • Epidemic spreading is often studied on static networks, but real-world networks are dynamic.
  • Activity-driven models capture the time-varying nature of network formation and evolution.
  • Understanding how network dynamics influence contagion is crucial for public health.

Purpose of the Study:

  • To analyze Susceptible-Infected-Susceptible (SIS) epidemic spreading on a generalized activity-driven modeling framework.
  • To derive the epidemic threshold analytically for time-varying networks.
  • To investigate the impact of node activity and attractiveness distributions on contagion.

Main Methods:

  • Analytical derivation of the epidemic threshold under comparable time scales for contact evolution and contagion.
  • Large-scale numerical simulations to validate theoretical findings.
  • Exploration of heterogeneous distributions and correlations between activity and attractiveness.

Main Results:

  • The derived epidemic threshold is general and applicable to any joint distribution of activity and attractiveness.
  • Heterogeneous attractiveness distributions significantly alter contagion dynamics.
  • Positive correlations between activity and attractiveness facilitate spreading, while negative correlations hamper it.

Conclusions:

  • Node activity and attractiveness are key factors in epidemic spreading on dynamic networks.
  • The interplay between these node properties and their correlations dictates the ease or difficulty of contagion.
  • This research advances the understanding of contagion phenomena in time-varying network structures.