意见形成的集体影响对基于智能体的流行病动力学
Collective impact of opinion formation on agent-based epidemic dynamics
- University of Parma（帕尔马大学）
- University of Pavia（帕维亚大学）
- University of Ferrara（费拉拉大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出耦合意见动力学与流行病模型的智能体框架，揭示行为反馈对疫情演变的定量影响，改善流行病解释。
AI中文摘要：
流行病传播与集体意见形成是紧密耦合的过程：个体对疫情严重性和防护措施价值的信念塑造行为，而不断演变的疫情本身又通过风险感知重塑公众情绪。我们提出了一个将意见动力学与房室流行病模型耦合的基于智能体的框架，其中每个智能体同时携带健康状况和一个描述对防护措施依从性的连续意见变量。意见演化由智能体之间的二元交互以及反映疫情当前状态的外生背景场驱动，而疾病传播率则明确依赖于交互智能体的意见。利用统计力学方法，我们刻画了在远离平衡和接近平衡状态下，房室质量分数和平均意见的宏观系统特征。校准结果表明，纳入行为意见动力学显著改善了对流行病演变的解释。特别是，推断出的风险感知函数与观察到的流行病负担一致演化，表明行为适应可以解释部分时间变异，否则这些变异将被吸收进随时间变化的传播参数中。我们的结果展示了将意见异质性整合进动力学流行病模型如何为行为与疾病传播之间的反馈提供定量见解。
英文摘要：
Epidemic spread and collective opinion formation are tightly coupled processes: individual beliefs about the severity of an outbreak and the value of protective measures shape behavior, while the evolving epidemic itself reshapes public sentiment through risk perception. We propose an agent-based framework that couples opinion dynamics with a compartmental epidemic model, in which each agent carries both a health status and a continuous opinion variable describing adherence to protective measures. Opinion evolution is driven by binary interactions among agents and by an exogenous background field that reflects the current state of the epidemic, while the disease transmission rate depends explicitly on the opinions of interacting agents. Using methods of statistical mechanics, we characterize the resulting macroscopic system for the compartmental mass fractions and mean opinions in both far-from-equilibrium and close-to-equilibrium regimes. The calibration shows that incorporating behavioral opinion dynamics substantially improves the interpretation of epidemic evolution. In particular, the inferred risk perception function evolves consistently with the observed epidemic burden, indicating that behavioral adaptation can explain part of the temporal variation that would otherwise be absorbed into time-dependent transmission parameters. Our results illustrate how integrating opinion heterogeneity in kinetic epidemic models offers quantitative insights into the feedback between behavior and disease spread.
