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Exact epidemic models on graphs using graph-automorphism driven lumping

机译:使用图自同构驱动的集总图的精确流行病模型

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摘要

The dynamics of disease transmission strongly depends on the properties of the population contact network. Pair-approximation models and individual-based network simulation have been used extensively to model contact networks with non-trivial properties. In this paper, using a continuous time Markov chain, we start from the exact formulation of a simple epidemic model on an arbitrary contact network and rigorously derive and prove some known results that were previously mainly justified based on some biological hypotheses. The main result of the paper is the illustration of the link between graph automorphisms and the process of lumping whereby the number of equations in a system of linear differential equations can be significantly reduced. The main advantage of lumping is that the simplified lumped system is not an approximation of the original system but rather an exact version of this. For a special class of graphs, we show how the lumped system can be obtained by using graph automorphisms. Finally, we discuss the advantages and possible applications of exact epidemic models and lumping.
机译:疾病传播的动力学在很大程度上取决于人口接触网络的性质。配对近似模型和基于个体的网络仿真已广泛用于对具有非平凡特性的接触网络进行建模。在本文中,我们使用连续时间马尔可夫链,从在任意接触网络上简单流行病模型的精确公式化出发,严格推导并证明了一些已知的结果,这些结果以前主要是根据某些生物学假设来证明的。本文的主要结果是说明了图自同构和集总过程之间的联系,从而可以显着减少线性微分方程系统中的方程数量。集总的主要优点是简化的集总系统不是原始系统的近似,而是它的精确版本。对于一类特殊的图,我们展示了如何通过使用图自同构来获得集总系统。最后,我们讨论了精确的流行病模型和集总的优点和可能的应用。

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