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将成对模型推广到网络上的非马尔可夫流行病。

Generalization of Pairwise Models to non-Markovian Epidemics on Networks.

机构信息

Department of Mathematics, School of Mathematical and Physical Sciences, University of Sussex, Falmer, Brighton BN1 9QH, United Kingdom.

Bolyai Institute, University of Szeged, Aradi vértanúk tere 1, Szeged 6720, Hungary.

出版信息

Phys Rev Lett. 2015 Aug 14;115(7):078701. doi: 10.1103/PhysRevLett.115.078701. Epub 2015 Aug 13.

Abstract

In this Letter, a generalization of pairwise models to non-Markovian epidemics on networks is presented. For the case of infectious periods of fixed length, the resulting pairwise model is a system of delay differential equations, which shows excellent agreement with results based on stochastic simulations. Furthermore, we analytically compute a new R_{0}-like threshold quantity and an analytical relation between this and the final epidemic size. Additionally, we show that the pairwise model and the analytic results can be generalized to an arbitrary distribution of the infectious times, using integro-differential equations, and this leads to a general expression for the final epidemic size. By showing the rigorous link between non-Markovian dynamics and pairwise delay differential equations, we provide the framework for a more systematic understanding of non-Markovian dynamics.

摘要

在这封信件中,我们提出了一种将成对模型推广到网络上非马尔可夫流行病的方法。对于固定传染期的情况,所得的成对模型是一个时滞微分方程组,该模型与基于随机模拟的结果吻合得非常好。此外,我们还分析计算了一个新的类似于 R_{0}的阈值量,并分析了该阈值量与最终流行病规模之间的关系。此外,我们还表明,使用积分微分方程,成对模型和解析结果可以推广到任意传染时间分布,从而得到最终流行病规模的一般表达式。通过展示非马尔可夫动力学和成对时滞微分方程之间的严格联系,我们为更系统地理解非马尔可夫动力学提供了框架。

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