Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China.
Chaos. 2011 Dec;21(4):043130. doi: 10.1063/1.3664403.
We study numerically how the structures of distinct networks influence the epidemic dynamics in contact process. We first find that the variability difference between homogeneous and heterogeneous networks is very narrow, although the heterogeneous structures can induce the lighter prevalence. Contrary to non-community networks, strong community structures can cause the secondary outbreak of prevalence and two peaks of variability appeared. Especially in the local community, the extraordinarily large variability in early stage of the outbreak makes the prediction of epidemic spreading hard. Importantly, the bridgeness plays a significant role in the predictability, meaning the further distance of the initial seed to the bridgeness, the less accurate the predictability is. Also, we investigate the effect of different disease reaction mechanisms on variability, and find that the different reaction mechanisms will result in the distinct variabilities at the end of epidemic spreading.
我们通过数值方法研究了不同网络结构如何影响接触过程中的传染病动力学。我们首先发现，均匀网络和异质网络之间的可变性差异非常小，尽管异质结构可以导致患病率降低。与非社区网络不同，强社区结构会导致患病率的二次爆发和出现两个变异峰值。特别是在局部社区，爆发初期异常大的变异性使得传染病传播的预测变得困难。重要的是，桥接度在可预测性中起着重要作用，这意味着初始种子与桥接度的距离越远，预测的准确性越低。此外，我们还研究了不同疾病反应机制对变异性的影响，发现不同的反应机制会导致传染病传播结束时出现明显的变异性。
