AI 中文总结
研究流行病动力学与网络拓扑的相互作用，开发基于主体模型，发现模型会经历流行病转变，网络可能失去无标度特性，通过异质平均场近似验证结果，凸显网络演化在流行病模型中的重要性。
AI 中文摘要
复杂网络上的流行病动力学受网络拓扑结构的强烈影响。然而，网络拓扑如何受诸如免疫力下降、疾病致死率以及人口变化等流行病属性的影响尚不清楚。为探究流行病动力学与网络拓扑之间的相互作用，我们在初始无标度网络上开发了一个基于主体的致命传染病模型，该模型通过人口和流行病引发的变化而演化。我们发现此模型在疾病最终消亡阶段和地方病阶段之间经历流行病转变。此外，流行病传播可能导致网络失去其无标度特性，从而引发从幂律到非幂律度分布的拓扑转变。我们使用基于主体模型的异质平均场近似验证了研究结果。这些结果凸显了在流行病模型中考虑网络演化的重要性，并增进了我们对共同演化动力系统的理解，其中疾病动力学和网络拓扑相互持续塑造。
英文摘要
Epidemics on complex networks have been shown to exhibit dynamics that are strongly influenced by the topology of the network. However, it remains unclear how the topology of the network is influenced by epidemic properties, such as waning immunity and disease fatality, coupled with demographic changes. To explore the interplay between epidemic dynamics and network topology, we develop an agent-based model of a fatal infectious disease with an imperfect immunity on an initially scale-free network that evolves via demographic and epidemic-induced changes. We show that this model undergoes an epidemic transition between a phase in which the disease eventually dies out and a phase in which it becomes endemic. Moreover, the network may lose its scale-free property as a result of the epidemic spreading, giving rise to a topological transition from a power-law to a non-power-law degree distribution. We validate our findings using heterogeneous mean-field approximations of the agent-based model. These results highlight the importance of accounting for network evolution in epidemic models and advance our understanding of coevolving dynamical systems, in which disease dynamics and network topology continuously shape one another.
