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Edge-based epidemic dynamics with multiple routes of transmission on random networks

机译:基于边缘的流行动态,随机网络上的多种传输路线

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This paper concerns the SIR dynamics with two types of spreading mechanism, i.e., local spreading through network contacts and global spreading through casual contacts. A low-dimensional edge-based model of ordinary differential equations incorporating arbitrary heterogeneous number of individual contacts is formulated. The basic reproduction number is obtained, and on networks of Poisson type, it is the sum of the basic reproduction numbers for global and local spreading pathways; however, on networks of other type, it is a nonlinear function of the basic reproduction numbers for global and local spreading pathways. To measure the control efforts imposed on one specific transmission pathway, type reproduction numbers for global and local transmission pathways are calculated, respectively. Equations of the final epidemic size are analytically derived. Finally, the numerical solutions to our model are compared with the ensemble averages of the stochastic simulations. Simulations have shown that casual contacts in the population may trigger large stochastic fluctuations, which may cause huge variances around their mean; thus, in this scenario the ensemble mean is not a good representation of the behavior of the stochastic epidemic process. However, increasing the local infection rate or the connectedness of networks yields better predictions. The results presented provide insights in setting a framework for the analysis and containment of multiple routes of epidemic transmission in reality.
机译:本文涉及具有两种类型的传播机制的SIR动态,即通过网络联系人和全球传播通过休闲联系方式。制定了一种基于低维边缘的普通微分方程模型,其包含任意异质数量的单独触点。获得基本的再现号码,并且在泊松类型网络上,是全球和局部扩散途径的基本再现数量的总和;然而,在其他类型的网络上,它是全局和局部扩展途径的基本再现号的非线性函数。为了测量对一个特定传输路径上施加的控制力,分别计算了全局和本地传输路径的类型再现号。分析衍生最终疫情的方程。最后,将模型的数值解决方案与随机仿真的集合平均值进行了比较。模拟表明,人口中的休闲联系可能会引发大型随机波动,这可能会导致其平均值的巨大差异;因此,在这种情况下,集合均值不是随机流行过程行为的良好代表。然而,增加局部感染率或网络的关联性产生更好的预测。结果提供了在现实中设定分析和遏制多种流行传输路线的框架的见解。

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