具有阈值政策的简易流行病模型

IF 2.8 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY Physica A: Statistical Mechanics and its Applications Pub Date : 2024-09-02 DOI:10.1016/j.physa.2024.130077
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引用次数: 0

摘要

我们建立了一个考虑阈值策略的简约空易感性感染(ESI)模型来描述基于网络的流行病传播,其中底层传播结构从边缘扩展到了高阶结构。为了解决显式网络中的流行病演化问题,我们提出了关于每个站点的淬火平均场概率演化,它由基于网络拓扑结构的非平滑微分方程组成。值得注意的是,在非光滑结构和高阶结构的共同作用下,在实证社会网络中观察到了三稳态,这与均值场系统分析中三个稳定均衡共存是一致的。此外,我们还发现在实证社会网络中存在一种滑动模式,均值场概率方程的理论分析也表明了这一点。最后,我们将该系统分为无阈值政策的自由子系统和有阈值政策的控制子系统。这两个子系统都存在稳定的无病均衡和稳定的地方病均衡,同时系统中还存在稳定的无病均衡和稳定的伪均衡,因此在不同临界水平的政策下,系统存在三种双稳态。
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Simplicial epidemic model with a threshold policy

We establish a simplicial empty-susceptible–infected (ESI) model with consideration of threshold policy to depict the network-based epidemic transmission, where the underlying propagation structures are expanded from edges to higher-order structures. To address the epidemic evolution in an explicit network, we formulate the quenched mean-field probability evolution about each site, which is composed of non-smooth differential equations based on network topology. Remarkably, under the combined action of non-smooth and high-order structures, a tristable state is observed in empirical social networks, which is consistent with the coexistence of three stable equilibria by analysis of the mean-field system. Moreover, we find that a sliding mode exists in empirical social networks, which is also indicated by the theoretical analysis of the mean-field probability equations. Finally, the system is divided into the free subsystem without the threshold policy and control subsystem with the threshold policy. Both subsystems admit a stable disease-free equilibrium and a stable endemic equilibrium, as well as coexistence of a stable disease-free equilibrium and a stable pseudo equilibrium in the system, thereby admitting three types of the bistable state under the policy with different critical levels.

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来源期刊
CiteScore
7.20
自引率
9.10%
发文量
852
审稿时长
6.6 months
期刊介绍: Physica A: Statistical Mechanics and its Applications Recognized by the European Physical Society Physica A publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents. Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.
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