复杂网络中基于接触的疾病传播的离散时间马尔可夫链方法
Discrete-time Markov chain approach to contact-based disease spreading in complex networks
摘要：
网络中的许多传染病过程都是通过其连通点之间的随机接触传播的。在物理学文献中，有两种极限情况被广泛分析，即所谓的接触过程（CP），即传染病以一定的速度从一个感染的顶点扩展到一个邻居，以及反应过程（RP），其中感染的个体有效地接触所有邻居以扩大流行病。然而，一个更现实的情况是从这两种情况之间的插值，考虑到一定数量的随机接触每单位时间。在这里，我们提出了一个离散时间制定的问题的接触为基础的传染病传播。我们解决了一个家庭的模型，参数化的随机接触试验每单位时间的数量，范围从CP到RP。与常见的异质平均场方法相比，我们专注于单个节点的感染概率。利用这个公式，我们可以构造不同感染模型的整个相图，并确定它们的临界性质。
英文摘要：
Many epidemic processes in networks spread by stochastic contacts among their connected vertices. There are two limiting cases widely analyzed in the physics literature, the so-called contact process (CP) where the contagion is expanded at a certain rate from an infected vertex to one neighbor at a time, and the reactive process (RP) in which an infected individual effectively contacts all its neighbors to expand the epidemics. However, a more realistic scenario is obtained from the interpolation between these two cases, considering a certain number of stochastic contacts per unit time. Here we propose a discrete-time formulation of the problem of contact-based epidemic spreading. We resolve a family of models, parameterized by the number of stochastic contact trials per unit time, that range from the CP to the RP. In contrast to the common heterogeneous mean-field approach, we focus on the probability of infection of individual nodes. Using this formulation, we can construct the whole phase diagram of the different infection models and determine their critical properties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
