网络流行病中的新兴时空异质性:由拓和流动性驱动的图灵不稳定性
Xinyu Wang1, Yao Fan1, Deyu Cui1
1CSSC Systems Engineering Research Institute, Beijing 100094, China.
Chaos (Woodbury, N.Y.)
|June 12, 2025
概括
网络拓和流动性通过图灵不稳定驱动流行病模式的形成. 无规模网络中的枢纽节点可以抑制本地爆发,但增加全球传播,帮助有针对性的干预.
科学领域:
- 流行病学 流行病学
- 网络科学 网络科学
- 数学生物学 数学生物学
背景情况:
- 连续媒介中的流行病建模得到了很好的研究,但对网络人口中的模式形成的理解是有限的.
- 反应-扩散系统解释了模式形成,但它们对超人口网络的应用需要进一步研究.
研究的目的:
- 利用反应-扩散理论,开发一个理论框架来研究超人口网络中的流行病动态.
- 通过图灵不稳定性,研究网络拓和流动性如何影响时空流行病模式.
主要方法:
- 将超人口网络模型与易受感染易受感染 (SIS) 流行病动态集成.
- 线性稳定性分析以确定关键值和自身矢量定位效应.
- 数字模拟以探索感染率和网络程度分布的影响.
- 分析解决方案来量化枢纽节点在传输中的作用.
主要成果:
- 网络拓和移动性共同推动通过图灵不稳定性出现的时空异质性.
- 在无尺度网络中的自身向量定位通过破坏低度节点的稳定性来放大异质性.
- 感染率 (β) 影响流行病的大小和模式几何,而程度分布则影响层次模式.
- 枢纽节点可以抑制本地不稳定,但可以增强全球传播.
结论:
- 图灵机制对于理解网络流行病模式,桥梁网络科学和反应扩散理论至关重要.
- 该研究提供了用于识别现实世界移动网络中高风险区域的预测工具.
- 结果可以为疾病控制的有针对性的干预策略提供信息.
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