论文标题
在网络上统一连续，离散和混合易感感染的回收过程
Unifying continuous, discrete, and hybrid susceptible-infected-recovered processes on networks
论文作者
论文摘要
通常假定在传播过程中两个连续感染与恢复事件之间的等待时间被认为是指数分布的，这导致了马尔可夫（即无内存）连续扩散动力学。但是，这并没有考虑到与社会，运输和疾病动态相关的记忆（相关）效应和离散相互作用。我们引入了一个框架，以建模（非）马尔可夫易感感染的（SIR）随机过程的连续，离散和混合形式。我们在本文中研究的混合SIR过程将感染描述为离散的马尔可夫和恢复事件是连续时间非马克维亚过程，它们模仿了细胞周期的分布。我们的结果表明，对流行过程的有效感染率描述无法唯一捕获这种混合动力和一般非马克维亚疾病动力学的行为。提供对马尔可夫将军和非马克维亚疾病暴发的统一描述，而是表明平均透射性产生的相同相图独立于基本的活动间分布。
Waiting times between two consecutive infection and recovery events in spreading processes are often assumed to be exponentially distributed, which results in Markovian (i.e., memoryless) continuous spreading dynamics. However, this is not taking into account memory (correlation) effects and discrete interactions that have been identified as relevant in social, transportation, and disease dynamics. We introduce a framework to model continuous, discrete, and hybrid forms of (non-)Markovian susceptible-infected-recovered (SIR) stochastic processes on networks. The hybrid SIR processes that we study in this paper describe infections as discrete-time Markovian and recovery events as continuous-time non-Markovian processes, which mimic the distribution of cell cycles. Our results suggest that the effective-infection-rate description of epidemic processes fails to uniquely capture the behavior of such hybrid and also general non-Markovian disease dynamics. Providing a unifying description of general Markovian and non-Markovian disease outbreaks, we instead show that the mean transmissibility produces the same phase diagrams independent of the underlying inter-event-time distributions.
