Replicator dynamics and behavior-augmented multiscale epidemic modeling

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.