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Epidemic dynamics on information-driven adaptive networks.

Xiu-Xiu Zhan1,2, Chuang Liu1, Gui-Quan Sun3

  • 1Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China.

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Summary
This summary is machine-generated.

This study introduces an information-driven model where individuals adapt network connections to slow disease spread. The model effectively reduces epidemic speed and prevalence by isolating infected individuals.

Keywords:
Adaptive modelBifurcation analysisEpidemic spreadingInformation diffusion

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Area of Science:

  • Complex systems
  • Epidemiology
  • Network science

Background:

  • Understanding epidemic dynamics requires studying network structure and individual behavior.
  • Simultaneous evolution of disease and information presents a complex challenge.

Purpose of the Study:

  • To propose and analyze an information-driven adaptive model for disease spread.
  • To investigate how information influences adaptive behaviors in epidemic scenarios.

Main Methods:

  • Developed an information-driven adaptive model for simultaneous disease and information evolution.
  • Employed simulation results and pairwise numerical analyses.
  • Utilized local bifurcation analysis to study dynamical behaviors.

Main Results:

  • The information-driven adaptive process significantly slows epidemic spreading speed.
  • Epidemic prevalence at the final state is substantially diminished.
  • Visualizations show disease containment within isolated fields on lattice and real-world networks.

Conclusions:

  • Information-driven adaptation is a potent strategy for mitigating epidemics.
  • Understanding information's role in human response is crucial for epidemic control.
  • The model provides insights into disease containment through adaptive network dynamics.