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Stability of the spreading in small-world network with predictive controller.

Z J Bao1, Q Y Jiang1, W J Yan1

  • 1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.

Physics Letters. A
|April 15, 2020
PubMed
Summary

This study introduces a predictive control strategy to limit disease spread in small-world networks. Findings show controller parameters and network rewiring probability significantly impact disease dynamics and stability.

Keywords:
OscillationPredictive controlSmall-world networkSpreadingStability

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Area of Science:

  • Network science
  • Epidemiology
  • Control theory

Background:

  • Disease propagation in networks is a critical area of study.
  • Small-world networks exhibit unique topological properties influencing spread dynamics.
  • Effective control strategies are needed to mitigate epidemics.

Purpose of the Study:

  • To apply a predictive control strategy for suppressing disease or virus propagation.
  • To investigate the stability of a small-world spreading model with a predictive controller.
  • To determine the relationship between controller parameters, rewiring probability, and model stability.

Main Methods:

  • Application of a predictive control strategy.
  • Analysis of the stability conditions for a small-world spreading model.
  • Computational simulations to observe disease dynamics under varying parameters.

Main Results:

  • A sufficient and necessary stability condition was derived, dependent on controller parameters and rewiring probability (p).
  • Decreasing rewiring probability (p) generally enhanced model stability for fixed controller parameters.
  • Suitable controller parameters effectively suppressed disease spread across various rewiring probabilities.
  • Unsuitable parameters could induce oscillations within specific rewiring probability ranges.

Conclusions:

  • Predictive control is a viable strategy for managing disease spread in small-world networks.
  • Controller parameter tuning and understanding network topology (rewiring probability) are crucial for effective epidemic suppression.
  • The study provides insights into the complex interplay between control strategies and network structure in disease dynamics.