传染病在Tie-Decay网络上的传播控制
Epidemic Thresholds of Infectious Diseases on Tie-Decay Networks
摘要：
在网络传染病的研究中，研究人员计算流行阈值，以帮助预测疾病最终是否会感染大部分人口。由于网络结构通常随时间变化，这从根本上影响了传播过程的动态，进而影响疾病传播的流行阈值，因此研究时间网络中的流行阈值非常重要。现有的时间网络中流行病阈值的研究大多集中在离散时间模型，但大多数现实世界中的网络系统在时间上不断演变。在我们的工作中，我们编码的连续时间依赖的网络到评估的易感-感染-易感（SIS）过程的流行病阈值通过研究一个SIS模型的领带衰减网络。我们推导了该模型的临界条件，并通过数值实验进行了验证。我们还研究了不同的因素-网络中联系强度的衰减系数，节点之间的相互作用频率，以及互动发生的底层社会网络的稀疏性-导致阈值临界值的降低或升高，从而有助于促进或阻碍疾病的传播。因此，我们展示了领带衰变网络的功能如何改变疾病传播的结果。
英文摘要：
In the study of infectious diseases on networks, researchers calculate epidemic thresholds to help forecast whether a disease will eventually infect a large fraction of a population. Because network structure typically changes in time, which fundamentally influences the dynamics of spreading processes on them and in turn affects epidemic thresholds for disease propagation, it is important to examine epidemic thresholds in temporal networks. Most existing studies of epidemic thresholds in temporal networks have focused on models in discrete time, but most real-world networked systems evolve continuously in time. In our work, we encode the continuous time-dependence of networks into the evaluation of the epidemic threshold of a susceptible--infected--susceptible (SIS) process by studying an SIS model on tie-decay networks. We derive the epidemic-threshold condition of this model, and we perform numerical experiments to verify it. We also examine how different factors---the decay coefficients of the tie strengths in a network, the frequency of interactions between nodes, and the sparsity of the underlying social network in which interactions occur---lead to decreases or increases of the critical values of the threshold and hence contribute to facilitating or impeding the spread of a disease. We thereby demonstrate how the features of tie-decay networks alter the outcome of disease spread.
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