Epidemic spreading in random rectangular networks.
Epidemic spreading in random rectangular networks.
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DOI：
10.1103/physreve.94.052316
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发表时间：
2016-11
期刊：
影响因子：
--
通讯作者：
Moreno Y
中科院分区：
文献类型：
--
作者：
Estrada E;Meloni S;Sheerin M;Moreno Y
使用网络理论来模拟疾病在人群中的传播，为经典流行病学模型引入了重要的现实元素。随机几何图（RGG）的使用是此类网络模型之一，可以考虑疾病传播的空间特性。在某些现实场景中（例如在分析通过植物传播的疾病时），疾病宿主所在的地块和田地的形状可能在传播动力学中发挥重要作用。在这里，我们考虑 RGG 的概括，以解释分配疾病宿主的地块或田地形状的变化。我们考虑在随机矩形图的节点上发生的疾病传播，并考虑这些网络上易感者-感染者-易感者模型或易感者-感染者-恢复模型的流行阈值的下限。使用广泛的数值模拟并基于我们的分析结果，我们得出结论：（在其他条件不变的情况下）节点分布的图或场的伸长使得网络对疾病传播更具弹性，因为流行阈值随着矩形的伸长而增加。这些结果与积累的经验证据和模拟结果一致，即同一面积和不同形状的地块或田地中植物上疾病的传播。
The use of network theory to model disease propagation on populations introduces important elements of reality to the classical epidemiological models. The use of random geometric graphs (RGGs) is one of such network models that allows for the consideration of spatial properties on disease propagation. In certain real-world scenarios—like in the analysis of a disease propagating through plants—the shape of the plots and fields where the host of the disease is located may play a fundamental role in the propagation dynamics. Here we consider a generalization of the RGG to account for the variation of the shape of the plots or fields where the hosts of a disease are allocated. We consider a disease propagation taking place on the nodes of a random rectangular graph and we consider a lower bound for the epidemic threshold of a susceptible-infected-susceptible model or a susceptible-infected-recovered model on these networks. Using extensive numerical simulations and based on our analytical results we conclude that (ceteris paribus) the elongation of the plot or field in which the nodes are distributed makes the network more resilient to the propagation of a disease due to the fact that the epidemic threshold increases with the elongation of the rectangle. These results agree with accumulated empirical evidence and simulation results about the propagation of diseases on plants in plots or fields of the same area and different shapes.