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  3. Replicator Dynamics And Behavior-augmented Multiscale Epidemic Modeling.
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  3. Replicator Dynamics And Behavior-augmented Multiscale Epidemic Modeling.

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Replicator dynamics and behavior-augmented multiscale epidemic modeling.

Jinming Wan1, Qingyue Liu2, Kathryn Zacks3

  • 1School of Systems Science and Industrial Engineering, State University of New York, Binghamton, New York 13902, USA.

Chaos (Woodbury, N.Y.)
|December 15, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study models how agent behavior and infection spread interact. Synchronized behavior and infection can cause recurrent outbreaks, highlighting the need for timely communication and risk-aware policies.

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Area of Science:

  • Epidemiology
  • Computational Social Science
  • Network Science

Background:

  • Traditional epidemiological models often neglect behavioral dynamics and network structures.
  • Agent behavior significantly influences disease transmission but is complex to model.
  • Understanding behavioral adaptation is crucial for effective epidemic control.

Purpose of the Study:

  • To develop a multiscale modeling framework integrating agent behavior and disease spread.
  • To investigate the synchronization between behavioral adaptation and infection prevalence.
  • To identify key factors for stabilizing epidemic dynamics.

Main Methods:

  • Integrated stochastic integrate-and-fire dynamics, networked replicator dynamics, and a behavior-augmented SIRS model.
  • Modeled stochastic acquired viral load and adaptive protective behaviors based on perceived risks and social influence.
  • Simulated the interplay between individual behavior, network structure, and disease transmission.

    • Main Results:

    • Synchronization between behavioral adaptation and infection prevalence can lead to recurrent outbreaks.
    • Risk perception dynamics, information delays, and behavioral adaptation timing significantly impact epidemic waves.
    • The integrate-and-fire mechanism smooths epidemic transitions, while delays can amplify fluctuations.

    Conclusions:

    • Epidemic control relies on biological factors, behavioral adaptation, and synchronization.
    • Timely communication, risk-aware policies, and feedback-responsive interventions are critical for stabilization.
    • The framework provides insights for pandemic preparedness and disease management.