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The Accuracy of Mean-Field Approximation for Susceptible-Infected-Susceptible Epidemic Spreading with Heterogeneous Infection Rates

机译:易感感染易感流行病与异质感染率蔓延的平均场近似的准确性

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The epidemic spreading over a network has been studied for years by applying the mean-field approach in both homogeneous case, where each node may get infected by an infected neighbor with the same rate, and heterogeneous case, where the infection rates between different pairs of nodes are also different. Researchers have discussed whether the mean-field approaches could accurately describe the epidemic spreading for the homogeneous cases but not for the heterogeneous cases. In this paper, we explore if and under what conditions the mean-field approach could perform well when the infection rates are heterogeneous. In particular, we employ the Susceptible-Infected-Susceptible (SIS) model and compare the average fraction of infected nodes in the metastable state, where the fraction of infected nodes remains stable for a long time, obtained by the continuous-time simulation and the mean-field approximation. We concentrate on an individual-based mean-field approximation called the N-intertwined Mean Field Approximation (NIMFA), which is an advanced approach considered the underlying network topology. Moreover, for the heterogeneity of the infection rates, we consider not only the independent and identically distributed (i.i.d.) infection rate but also the infection rate correlated with the degree of the two end nodes. We conclude that NIMFA is generally more accurate when the prevalence of the epidemic is higher. Given the same effective infection rate, NIMFA is less accurate when the variance of the i.i.d. infection rate or the correlation between the infection rate and the nodal degree leads to a lower prevalence. Moreover, given the same actual prevalence, NIMFA performs better in the cases: 1) when the variance of the i.i.d. infection rates is smaller (while the average is unchanged); 2) when the correlation between the infection rate and the nodal degree is positive. Our work suggests the conditions when the mean-field approach, in particular NIMFA, is more accurate in the approximation of the SIS epidemic with heterogeneous infection rates.
机译:该疾病传播在网络上已经被两个同质的情况下,每个节点可能会通过被感染的邻居同样的速度,和异构的情况下,被感染的应用平均场方法研究了多年,其中不同对之间的感染率节点也不同。研究人员已经讨论了平均场的方法是否能准确地描述疫情的同质案件,但没有为异类的情况下传播。在本文中,我们将探讨是否以及在何种条件下,当感染率是异质平均场方法可能表现良好。特别是,我们采用易感,感染易感(SIS)模型,并比较在亚稳状态,感染节点的平均分数,其中感染节点的比例仍然很长一段时间保持稳定,在连续时间模拟,得到的平均场近似。我们专注于被称为的N-交织平均场近似(NIMFA)一个基于个体的平均场近似,这被认为是潜在的网络拓扑的先进的方法。此外,对于感染率的异质性,我们不仅要考虑独立同分布(独立同分布)的感染率,但也有两个端节点的程度相关感染率。我们的结论是NIMFA通常是当疫情的发生率更高更准确。给定相同的有效感染率,NIMFA是较不准确时,独立同分布的方差感染率或感染率和节点度导致较低的发病率之间的相关性。此外,给定相同的实际流行,NIMFA执行在箱子更好:1)独立同分布的,当方差感染率是较小的(而平均不变); 2)当感染率和节点度之间的相关性为正。我们的研究表明的条件时,平均场的方法,特别是NIMFA,与异构感染率SIS疫情的逼近更准确。

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