School of Mathematical Sciences, Peking University, Beijing 100871, China.
LMIB and School of Mathematics and Systems Science, Beihang University, Beijing 100191, China.
Phys Rev E. 2016 Mar;93(3):032308. doi: 10.1103/PhysRevE.93.032308. Epub 2016 Mar 8.
The evolution of network structure and the spreading of epidemic are common coexistent dynamical processes. In most cases, network structure is treated as either static or time-varying, supposing the whole network is observed in the same time window. In this paper, we consider the epidemics spreading on a network which has both static and time-varying structures. Meanwhile, the time-varying part and the epidemic spreading are supposed to be of the same time scale. We introduce a static and activity-driven coupling (SADC) network model to characterize the coupling between the static ("strong") structure and the dynamic ("weak") structure. Epidemic thresholds of the SIS and SIR models are studied using the SADC model both analytically and numerically under various coupling strategies, where the strong structure is of homogeneous or heterogeneous degree distribution. Theoretical thresholds obtained from the SADC model can both recover and generalize the classical results in static and time-varying networks. It is demonstrated that a weak structure might make the epidemic threshold low in homogeneous networks but high in heterogeneous cases. Furthermore, we show that the weak structure has a substantive effect on the outbreak of the epidemics. This result might be useful in designing some efficient control strategies for epidemics spreading in networks.
网络结构的演化和传染病的传播是共同存在的动态过程。在大多数情况下,网络结构被视为静态或时变的,假设在同一时间窗口观察整个网络。在本文中,我们考虑在具有静态和时变结构的网络上传播的传染病。同时,时变部分和传染病传播被假设具有相同的时间尺度。我们引入了一种静态和活动驱动的耦合(SADC)网络模型,以描述静态(“强”)结构和动态(“弱”)结构之间的耦合。使用 SADC 模型,我们分别从理论和数值上研究了 SIS 和 SIR 模型在各种耦合策略下的传染病阈值,其中强结构具有均匀或异质的度分布。从 SADC 模型获得的理论阈值可以恢复和推广静态和时变网络中的经典结果。结果表明,在均匀网络中,弱结构可能使传染病阈值降低,但在异质情况下则会升高。此外,我们还表明,弱结构对传染病的爆发有实质性的影响。这一结果可能有助于设计网络中传染病传播的有效控制策略。