解开异质性和超边缘重叠在高阶网络爆炸性传染中的作用
Federico Malizia1, Andrés Guzmán1, Iacopo Iacopini1,2
1Northeastern University London, Network Science Institute, London E1W 1LP, United Kingdom.
Physical review letters
|November 30, 2025
概括
我们开发了基于群组的隔间建模 (GBCM) 来研究复杂网络中的传染. 顺序间的相关性是爆炸性传染的关键驱动因素,影响不同网络结构的流行病动态.
科学领域:
- 流行病学 流行病学
- 网络科学 网络科学
- 数学建模的数学建模
背景情况:
- 了解复杂网络中的传染动态至关重要.
- 高阶相互作用和网络异质性显著影响疾病传播.
- 现有的模型往往难以捕捉这些复杂的结构特征.
研究的目的:
- 引入基于组的分区建模 (GBCM) 以分析更高阶网络中的传染.
- 分析地解开不同交互顺序对流行病动态的影响.
- 研究结构异质性和顺序间相关性在疫情爆发和爆炸性传染中的作用.
主要方法:
- 开发了一种用于不可逆转传染的平均场框架 (GBCM).
- 集成的结构异质性和跨群体大小的相关性.
- 通过数值模拟验证模型.
主要成果:
- 在分析上,GBCM将每个交互顺序对流行病动态的贡献分开.
- 异质性和顺序间的相关性共同塑造了疫情爆发和爆炸动态.
- 顺序间的相关性普遍推动了不可逆转和可逆转的过程中的爆炸性传染.
结论:
- GBCM为研究复杂,高阶网络中的传染提供了强大的工具.
- 顺序间的相关性是爆炸性传染的基本机制.
- 该框架提供了对异质网络中流行病控制策略的见解.
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