Shkarayev Maxim S, Tunc Ilker, Shaw Leah B
Department of Physics & Astronomy, Iowa State University, Ames IA, 50011.
John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL 33156.
J Phys A Math Theor. 2014 Nov 14;47(45). doi: 10.1088/1751-8113/47/45/455006.
During an epidemic, people may adapt or alter their social contacts to avoid infection. Various adaptation mechanisms have been studied previously. Recently, a new adaptation mechanism was presented in [1], where susceptible nodes temporarily deactivate their links to infected neighbors and reactivate when their neighbors recover. Considering the same adaptation mechanism on a scale-free network, we find that the topology of the subnetwork consisting of active links is fundamentally different from the original network topology. We predict the scaling exponent of the active degree distribution and derive mean-field equations by using improved moment closure approximations based on the conditional distribution of active degree given the total degree. These mean field equations show better agreement with numerical simulation results than the standard mean field equations based on a homogeneity assumption.
在疫情期间,人们可能会调整或改变其社交接触以避免感染。此前已经研究了各种适应机制。最近,[1]中提出了一种新的适应机制,即易感节点会暂时停用与受感染邻居的连接,并在邻居康复时重新激活。考虑到在无标度网络上的相同适应机制,我们发现由活跃连接组成的子网拓扑与原始网络拓扑有根本不同。我们预测活跃度分布的标度指数,并通过基于给定总度数的活跃度条件分布使用改进的矩闭合近似来推导平均场方程。这些平均场方程与基于均匀性假设的标准平均场方程相比,与数值模拟结果的吻合度更高。