Bibliographic Information: Ordorica Arango, M., Bizyaeva, A., Levin, S. A., & Leonard, N. E. (2024). Opinion-driven risk perception and reaction in SIS epidemics. arXiv preprint arXiv:2410.12993.
Research Objective: This study investigates how the interplay between human behavior, particularly risk perception and reaction to infection, affects the spread of epidemics, using a modified SIS model incorporating nonlinear opinion dynamics.
Methodology: The researchers developed the NOD-SIS model, which couples the standard SIS epidemic model with a nonlinear opinion dynamics model. This model explores how a population's opinion about infection risk, influencing their contact rates, interacts with disease transmission dynamics. The authors analyze the model's fixed points and bifurcations under different parameter regimes, representing varying levels of infectiousness, peer pressure, and individual risk aversion or seeking tendencies.
Key Findings: The study reveals that for low infectiousness and urgency, the model behaves similarly to the standard SIS model. However, with higher infectiousness, the system exhibits bistability. The initial tendency of the population towards risk aversion (e.g., social distancing) or risk-seeking behavior (increased contact) determines the final endemic state, with risk aversion leading to lower infection levels. Notably, high peer pressure towards risk aversion can even lead to complete disease eradication.
Main Conclusions: The integration of opinion dynamics into epidemic models provides a more nuanced understanding of disease spread. The research demonstrates that behavioral responses, shaped by risk perception and social influence, play a crucial role in determining epidemic outcomes. This highlights the potential importance of public health interventions aimed at promoting risk-averse behaviors.
Significance: This study underscores the limitations of traditional epidemic models that don't account for human behavior. By incorporating opinion dynamics, it offers a more realistic and insightful approach to modeling and potentially mitigating epidemic spread.
Limitations and Future Research: The study primarily focuses on a well-mixed population. Future research could explore the model's implications in more complex scenarios, such as structured populations with varying network topologies representing different contact and communication patterns. Further investigation into the interplay of network structure and opinion dynamics in epidemic spread is warranted.
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by Marcela Ordo... lúc arxiv.org 10-18-2024
https://arxiv.org/pdf/2410.12993.pdfYêu cầu sâu hơn