在随机网络上自我适应的传染动态
Konstantin Clauß1, Christian Kuehn1,2
1Department of Mathematics, Technical University of Munich, 85748 Garching bei München, Germany.
Chaos (Woodbury, N.Y.)
|September 11, 2023
概括
这项研究引入了一种自我适应型建模方法,用于在不断发展的网络上的流行病动态. 这项研究揭示了流行病模型中的振荡行为和与自我组织的关键性的联系.
科学领域:
- 复杂的系统复杂的系统.
- 流行病学 流行病学
- 网络科学 网络科学
背景情况:
- 自适应力学在各种科学领域普遍存在,包括社会经济学,神经科学和生物物理学.
- 适应机制往往取决于系统的历史状态,导致复杂的动态.
研究的目的:
- 在共同进化网络上开发和应用流行病动态的自我适应模型框架.
- 调查流行病模型中振荡性行为和自我组织的关键性的出现.
主要方法:
- 这项研究采用了自适应式建模方法,将断层决定性的马科夫动力学与非马科夫适应相结合.
- 该框架应用于随机网络上的易感-感染-易感 (SIS) 和易感-感染-恢复 (SIR) 流行病模型.
- 考虑了共同进化的网络动态,包括节点状态变化和边缘修改.
主要成果:
- 对于简单的基于值的锁定措施,在大型参数区域观察到振荡行为.
- 在SIS模型中,对振荡周期的分析表达式是从对式模型中推导出来的,并通过模拟验证.
- 基本的复制数在1左右波动,这表明它与自我组织的关键性有关.
结论:
- 自适应型建模框架有效地捕捉了在不断发展的网络上复杂的流行病动态.
- 减少的机制可以导致显著的振荡行为,为流行病控制策略提供了洞察力.
- 这些发现突出了流行病动态和自我组织的关键性之间的潜在联系.
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