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Exact deterministic representation of Markovian SIR epidemics on networks with and without loops

机译:马尔可夫sIR流行病在具有和不具有环的网络上的精确确定性表示

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摘要

In a previous paper Sharkey et al. [14] proved the exactness of closures at the level of triples for Markovian SIR (susceptible-infected-removed) dynamics on tree-like networks. This resulted in a deterministic representation of the epidemic dynamics on the network that can be numerically evaluated. In this paper, we extend this modelling framework to certain classes of networks exhibiting loops. We show that closures where the loops are kept intact are exact, and lead to a simplified and numerically solvable system of ODEs (ordinary-differential-equations). The findings of the paper lead us to a generalisation of closures that are based on partitioning the network around nodes that are cut-vertices (i.e. the removal of such a node leads to the network breaking down into at least two disjointed components or subnetworks). Exploiting this structural property of the network yields some natural closures, where the evolution of a particular state can typically be exactly given in terms of the corresponding or projected states on the subnetworks and the cut-vertex. A byproduct of this analysis is an alternative probabilistic proof of the exactness of the closures for tree-like networks presented in Sharkey et al. [14]. In this paper we also elaborate on how the main result can be applied to more realistic networks, for which we write down the ODEs explicitly and compare output from these to results from simulation. Furthermore, we give a general, recipe-like method of how to apply the reduction by closures technique for arbitrary networks, and give an upper bound on the maximum number of equations needed for an exact representation.
机译:在以前的论文中,Sharkey等人。 [14]证明了在树状网络上,马尔可夫SIR(易感性感染去除)动力学的三级封闭的正确性。这样就可以确定地表示网络上的流行病动态,可以对其进行数值评估。在本文中,我们将此建模框架扩展到某些具有环路的网络类别。我们证明了保持循环完整的闭包是精确的,并导致了ODE(普通微分方程)的简化和数字可解的系统。本文的发现使我们对闭包进行了概括,该闭包基于将网络围绕切顶点的节点进行划分(即删除此类节点会导致网络分解为至少两个不相连的组件或子网)。利用网络的这种结构特性会产生一些自然的闭包,其中特定状态的演化通常可以根据子网和割顶点上的相应或投影状态准确给出。该分析的副产品是Sharkey等人提出的树状网络封闭的准确性的另一种概率证明。 [14]。在本文中,我们还详细说明了如何将主要结果应用于更现实的网络,为此,我们明确记录了ODE并将其输出与仿真结果进行比较。此外,我们给出了一种通用的,类似配方的方法,该方法如何对任意网络应用闭包减少法,并给出了精确表示所需的最大方程数上限。

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