AI 中文总结
研究针对标准流行病学模型在物理移动与传播风险脱钩时失效的问题，建立由风险介导传播动力学支配的统一理论框架，将风险规避倾向嵌入传播机制，分析新冠数据时该模型表现优异，能捕捉定性接触变化，为预测疫情轨迹提供新基线。
AI 中文摘要
标准流行病学模型依赖物理流动性和政策指标，当物理移动与实际传播风险脱钩时会失效。为解决此问题，我们建立了由风险介导的传播动力学支配的统一理论框架。我们将潜在的风险规避倾向直接嵌入传播机制，而非将社会反应视为独立的现象学代理。通过韦伯 - 费希纳定律将其简洁地表述为对疾病发病率的对数缩放响应以及行为疲劳。分析多区域新冠数据时，我们的风险介导模型显著优于传统框架。我们的方法能直接捕捉如戴口罩等未观察到的定性接触变化，为预测未来疫情轨迹提供了一个稳健、与流动性无关的基线。
英文摘要
Standard epidemiological models rely on physical mobility and policy indicators, which fail when physical movement decouples from actual transmission risk. To address this, we establish a unified theoretical framework governed by risk-mediated transmission dynamics. Rather than treating societal responses as independent phenomenological proxies, we embed the underlying risk-avoidance tendency directly into the transmission mechanism. This is parsimoniously formulated via the Weber-Fechner law as a logarithmically scaled response to disease incidence, alongside behavioral fatigue. Analyzing multi-regional COVID-19 data, our risk-mediated model significantly outperforms traditional frameworks. While mobility metrics merely track movement volume, our approach directly captures unobserved qualitative contact changes, such as mask-wearing. By integrating this intrinsic behavioral principle, our framework provides a robust, mobility-independent baseline for predicting future epidemic trajectories.
