发表机构
The University of Sydney; Centre for Complex Systems, The University of Sydney; Sydney Infectious Diseases Institute, The University of Sydney（悉尼大学; 悉尼大学复杂系统中心; 悉尼大学悉尼传染病研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出带反馈的复杂传染模型，耦合危机动态与行为传播，并在澳大利亚COVID-19数据上验证，以更少参数达到相当性能，且解析可处理、可推广至多种危机。
AI 中文摘要
危机与人群反应可以形成耦合的动力系统，其中危机状况塑造保护性行为，而集体反应又改变危机的轨迹。现有模型很少同时捕捉不断演变的危机状况以及竞争性行为依赖强化的传播。我们提出了一种带反馈的复杂传染（CCF）模型，该模型通过双向反馈将竞争性复杂传染与危机动态耦合。智能体根据社会强化和采纳复杂性在竞争状态之间随机切换，这些因素受危机状况和信息宣传活动的影响，而人群层面的行为变化又反馈到危机中。我们针对充分混合的人群制定并分析了该模型，刻画了其均衡点及相应的稳定性条件。作为模型验证的案例研究，我们将CCF模型与一个基于普查校准的澳大利亚COVID-19传播的智能体模型相结合，以表示社交距离的采纳与终止。我们将CCF模型与现有的舆论动态模型进行比较，发现尽管使用的参数更少，但在再现反复出现的感染波方面达到了相当的性能。由此产生的框架简洁、解析可处理，并能适应不同类型的危机。
英文摘要
Crises and population responses can form coupled dynamical systems, with crisis conditions shaping protective behaviours and collective responses altering the crisis trajectory. Existing models rarely capture both evolving crisis conditions and the reinforcement-dependent spread of competing behaviours. We propose a complex contagion with feedback (CCF) model that couples competing complex contagion with crisis dynamics through bidirectional feedback. Agents stochastically switch between competing states according to social reinforcement and adoption complexity, which is influenced by crisis conditions and information campaigns, while population-level behavioural change feeds back into the crisis. We formulate and analyse the model for a well-mixed population, characterising its equilibria and associated stability conditions. As a case study for model validation, we integrate CCF model with a census-calibrated agent-based model of COVID-19 transmission in Australia to represent social distancing adoption and discontinuation. We compare the CCF model with an existing opinion dynamics model and find that, despite using fewer parameters, it achieves comparable performance in reproducing recurrent infection waves. The resulting framework is parsimonious, analytically tractable, and can be adapted to different types of crises.
