从时间变量网络过渡到静态网络:时间尺度分离在N交织的平均场中,对易受感染易受流行病的近似估计
Robin Persoons1, Mattia Sensi2,3, Bastian Prasse4
1Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands.
Physical review. E
|April 18, 2024
概括
我们使用易受感染易受感染 (SIS) 模型分析了疾病在动态网络上的传播. 我们的研究结果揭示了网络变化如何影响流行病动态,并为融合时间提供了界限.
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:
- 像SIS这样的流行病学模型经常被简化为静态网络.
- 现实世界的网络是动态的,随着时间的推移,连接会发生变化.
- 时间尺度的分离是一个常见的假设,但它的有效性需要调查.
研究的目的:
- 将SIS进程的N交织平均场近似 (NIMFA) 扩展到时间变化的网络.
- 调查疾病传播和网络拓更新之间的时间尺度分离的影响.
- 定义和推导不同网络制度的过渡时间的界限.
主要方法:
- 扩展NIMFA模型以适应时间变化的网络结构.
- 引入过渡时间T̲(r) 和T ̄(r) 来划分网络模式 (冷,中间,火).
- 分析静态NIMFA过程的收,以获得T ̄(r) 的分析边界.
主要成果:
- 为过渡时间T ̄(r) 推导出分析上限和下限.
- 证明T ̄(r) 主要取决于基本复制数 (R0).
- 在疫情值附近观察到T ̄(r) 的大值,表明时间尺度分离的限制.
结论:
- 该研究提供了关于SIS流程在动态网络上的融合时间的关键见解.
- 这些发现凸显了对某些现实世界流行病情景的时间尺度分离假设的局限性.
- 导出边界提供了一种定量方法来评估简化模型的适用性.
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