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Related Experiment Video

Updated: Jun 4, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Mitigating epidemic spread in complex networks based on deep reinforcement learning.

Jie Yang1, Wenshuang Liu1, Xi Zhang1

  • 1School of Automation, Beijing Institute of Technology, Beijing 100081, China.

Chaos (Woodbury, N.Y.)
|December 19, 2024
PubMed
Summary
This summary is machine-generated.

This study uses deep reinforcement learning (DRL) to identify optimal quarantine targets in complex networks, balancing epidemic control with economic costs. The DRL strategy effectively mitigates contagion spread, with diminishing returns beyond a critical quarantine scale.

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Area of Science:

  • Network Science
  • Epidemiology
  • Computational Science

Background:

  • Complex networks are vulnerable to contagious cascades, necessitating efficient epidemic mitigation.
  • Physical quarantine is effective but can incur significant economic costs if not strategically implemented.

Purpose of the Study:

  • To develop an innovative, cost-effective strategy for selecting quarantine targets in complex networks.
  • To minimize both epidemic spread and quarantine-related economic repercussions.

Main Methods:

  • Modeling epidemic spread using Markov chains with stochastic transitions and node quarantines.
  • Employing deep reinforcement learning (DRL), specifically the proximal policy optimization algorithm, to train a quarantine strategy.
  • Conducting simulations on synthetic and real-world network datasets.

Main Results:

  • The DRL-based quarantine strategy effectively controls epidemic spread in complex networks.
  • A non-linear relationship was observed between the daily maximum quarantine scale and mitigation effect, showing diminishing returns after a critical threshold.
  • The strategy successfully balances infection rate reduction with quarantine costs.

Conclusions:

  • Deep reinforcement learning offers a powerful approach for optimizing epidemic mitigation strategies in complex networks.
  • Understanding the non-linear impact of quarantine scale is vital for efficient resource allocation and policy-making.
  • The proposed method provides a framework for economically viable epidemic response planning.