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On epidemic spreading in metapopulation networks with time-varying contact patterns.

Chaos (Woodbury, N.Y.)·2023
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Implementing self-quarantine and social distancing effectively raises epidemic thresholds. Individual activity and attractiveness similarly impact disease spread, while network heterogeneity accelerates outbreaks.

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Area of Science:

  • Network Science
  • Epidemiology
  • Computational Social Science

Background:

  • Information technology revolution complicates social interactions.
  • Understanding heterogeneous behaviors in dynamic networks is crucial for disease modeling.

Purpose of the Study:

  • To explore the role of heterogeneous behaviors in time-varying networks.
  • To propose a multi-layer activity-driven network model with attractiveness.
  • To analyze the impact of individual behaviors on epidemic thresholds.

Main Methods:

  • Microscopic Markov chain approach.
  • Mean-field approach.
  • Modeling three individual behaviors: self-quarantine, social distancing, and information spreading.

Main Results:

  • Self-quarantine and social distancing effectively increase the epidemic threshold.
  • Individual activity and attractiveness have equivalent effects on the epidemic threshold.
  • Stronger heterogeneity in activated individual edge numbers leads to earlier epidemic outbreaks.

Conclusions:

  • Behavioral interventions like self-quarantine and social distancing are vital for disease control.
  • Network structure and individual attributes significantly influence epidemic dynamics.
  • Heterogeneity in network activity can accelerate disease transmission.