Taylor Timothy J, Kiss Istvan Z
Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
J Math Biol. 2014 Jul;69(1):183-211. doi: 10.1007/s00285-013-0699-x. Epub 2013 Jun 6.
Over the years numerous models of S I S (susceptible --> infected --> susceptible) disease dynamics unfolding on networks have been proposed. Here, we discuss the links between many of these models and how they can be viewed as more general motif-based models. We illustrate how the different models can be derived from one another and, where this is not possible, discuss extensions to established models that enables this derivation. We also derive a general result for the exact differential equations for the expected number of an arbitrary motif directly from the Kolmogorov/master equations and conclude with a comparison of the performance of the different closed systems of equations on networks of varying structure.
多年来,人们提出了许多关于在网络上展开的SIS(易感→感染→易感)疾病动态模型。在这里,我们讨论这些模型中的许多模型之间的联系,以及它们如何被视为更一般的基于基序的模型。我们说明了不同模型如何相互推导,以及在无法推导的情况下,讨论对现有模型的扩展以实现这种推导。我们还直接从柯尔莫哥洛夫/主方程推导出任意基序预期数量的精确微分方程的一般结果,并最后比较不同封闭方程组在不同结构网络上的性能。