基于连续时间随机走路及其动态的复杂网络上的一个新的分数级异常流行病模型
Jing-Wei Yang1, Zu-Guo Yu1,2, Long Shi3
1National Center for Applied Mathematics in Hunan & Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, Hunan 411105, China.
Chaos (Woodbury, N.Y.)
|November 6, 2025
概括
这项研究引入了一种新的分数顺序易感-感染-易感 (SIS) 模型,以了解复杂网络中的流行病传播. 它揭示了个体行为和网络结构如何影响疾病传播动态.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 网络科学 网络科学
背景情况:
- 流行病建模传统上使用整数顺序微分方程.
- 复杂的网络和异常扩散显著影响疾病的传播.
- 了解个体居住时间和网络拓对于准确的建模至关重要.
研究的目的:
- 提出一个新的分数顺序易感-感染-易感 (SIS) 流行病模型.
- 使用连续时间随机步行 (CTRW) 框架将异质网络中的异常扩散纳入.
- 分析记忆效应和网络拓对流行病动态的影响.
主要方法:
- 从CTRW框架推导一个分数级反应扩散模型.
- 确定解决方案和平衡的存在和独特性.
- 对无病和特有平衡的全球和局部异位稳定性的分析.
- 对乌拉姆-海尔斯稳定性的研究.
主要成果:
- 理论分析和数值模拟证实了记忆效应对趋同率的影响.
- 当基本繁殖数 (R0) 大于1时,感染密度汇聚到1 - 1/R0.0.
- 网络节点的稳定状态感染人数显示出与节点程度的近线性关系.
- 节点的行为趋于与网络矩阵的0自值相关的自向量组件的常数倍数.
结论:
- 分数顺序动态和异常扩散对于复杂网络中现实的流行病建模至关重要.
- 网络拓,特别是节点程度,在确定感染个体的分布方面发挥着重要作用.
- 该模型提供了关于记忆效应和网络结构如何共同控制流行病传播模式的见解.
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