发表机构
Vermont Complex Systems Institute and Department of Computer Science, University of Vermont; Santa Fe Institute; Complexity Science Hub; Institute of Mathematics, Eötvös Loránd University; Network Science Institute, Northeastern University London; Department of Mathematics, Northeastern University（佛蒙特大学计算机科学与复杂系统研究所; 圣塔菲研究所; 复杂性科学中心; 罗兰大学数学研究所; 伦敦东北大学网络科学研究所; 东北大学数学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出通过快速变量消除对复杂传染病模型进行系统约化，揭示其共同骨架，并统一了连续与不连续的流行转变。核心贡献在于将复杂性归因于快速变量，产生依赖流行率的涌现复杂传染，从而简化高维系统分析。
AI 中文摘要
在生物和社会复杂系统中，流行病或传染模型通过网络相关性、自适应行为、高阶相互作用或共循环毒株来纳入复杂性。追踪这些机制导致高维数学系统难以进行解析处理。我们证明，一大类模型可以通过快速变量消除实现系统性约化。核心洞见在于，复杂性可以由比流行病快得多的平衡的快速变量所捕获，从而产生“涌现的复杂传染”，其中感染力量依赖于流行率。流行病转变遵循一个普遍方程，并且根据感染力量随流行率衰减或增强而表现为连续或不连续。我们的结果揭示，多样化的流行病模型共享一个共同骨架，使得具有特征形式的机制在宏观上可能无法区分。
英文摘要
Across biological and social complex systems, models of epidemics or contagions incorporate complexity through network correlations, adaptive behavior, higher-order interactions, or co-circulating strains. Tracking these mechanisms lead to high-dimensional mathematical systems that resist analytical treatment. We show that a broad class of models admits a systematic reduction via fast-variable elimination. The central insight is that complexity can be captured by fast variables equilibrating much faster than the epidemic, leading to ``emergent complex contagion'' where the force of infection depends on prevalence. The epidemic transition follows a universal equation and is continuous or discontinuous whether the force of infection decays or intensifies with prevalence. Our results reveal diverse epidemic models share a common skeleton such that mechanisms that share a characteristic form can be macroscopically indistinguishable.
Comments35 pages with appendices, 5 figures (3 in the main text), 1 table
