Modeling epidemic dynamics of mutant strains with evolutionary game-based vaccination behavior

2026-01-02

Chaos, Solitons & Fractals

AI Summary

This study addresses the challenge of jointly modeling viral evolution and dynamic individual vaccination behavior in epidemic dynamics. The authors propose an extended SIRS model that incorporates both ancestral and variant strains, introduces a vaccinated compartment, and integrates evolutionary game theory to capture individuals’ vaccination decisions. By employing Microscopic Markov Chain Approximation (MMCA), the framework couples disease transmission with behavioral adaptation at the population level. This approach represents the first attempt to co-model viral mutation and game-theoretic vaccination behavior, thereby revealing their co-evolutionary dynamics. Numerical simulations and sensitivity analyses demonstrate that the proposed method effectively mitigates diverse epidemic outbreaks, with vaccine cost, efficacy, and perceived risk of side effects identified as key determinants of vaccination coverage.

Technology Category

• Game Theory and Economic Paradigms: Behavioral Game Theory
• Machine Learning: Evolutionary Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Problem

• mutant strains
• vaccination behavior
• epidemic dynamics
• evolutionary game theory
• SIRS model

Innovation

• evolutionary game theory
• viral mutation
• behavior-aware modeling
• microscopic Markov chain approach
• time-varying vaccination rate