在时间变化的网络上,流行病模型中的延迟驱动的阶段过渡
Wen Wang1, Guanrong Chen2, Eric W M Wong2
1School of Mathematical Sciences, Ocean University of China, Qingdao 266100, China.
Chaos (Woodbury, N.Y.)
|April 19, 2024
概括
这项研究分析了复杂的,时间变化的网络上的流行病动态,并对延迟进行分析. 我们发现,时间延迟可以触发振荡,甚至在这些系统中促进模式形成.
科学领域:
- 复杂的系统复杂的系统.
- 网络科学 网络科学
- 数学流行病学数学流行病学
背景情况:
- 网络系统经常表现出动态交互,对分析和控制构成挑战.
- 在静态网络上进行流行病建模已经很成熟,但随时间变化的相互作用需要先进的方法.
研究的目的:
- 调查复杂网络上的流行病过程,其相互作用和时间延迟随时间变化而变化.
- 分析时间延迟对系统稳定性和模式形成的影响.
- 在延迟,时间变化的流行病模型中确定阶段过渡的关键条件.
主要方法:
- 开发一个平均定理,用自主微分方程近似推迟时间变量系统.
- 分析系统演变,并使用霍普夫分叉理论确定关键时间延迟.
- 在周期性,眼和准周期性时间变化的网络上进行数值模拟.
主要成果:
- 为了分析延迟的,时间变化的系统,建立了一个平均定理.
- 确定了一个关键的时间延迟,超出此时,特有平衡变得不稳定,导致通过Hopf分叉的振荡.
- 数字模拟证实了不同时间变化的网络类型的理论发现.
结论:
- 时间延迟在时间变化的网络上流行病过程的稳定性和动态性中起着至关重要的作用.
- 时间延迟可以通过扩展网络频率范围来促进模式形成,例如图灵模式.
- 这些发现为控制和预测在动态环境中的流行病传播提供了洞察力.
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