Nie Yanyi, Zhong Ming, Li Runchao, Zhao Dandan, Peng Hao, Zhong Xiaoni, Lin Tao, Wang Wei
School of Public Health, Chongqing Medical University, Chongqing 400016, China.
College of Computer Science, Sichuan University, Chengdu 610065, China.
Chaos. 2023 Jun 1;33(6). doi: 10.1063/5.0149384.
The higher-order interactions emerging in the network topology affect the effectiveness of digital contact tracing (DCT). In this paper, we propose a mathematical model in which we use the hypergraph to describe the gathering events. In our model, the role of DCT is modeled as individuals carrying the app. When the individuals in the hyperedge all carry the app, epidemics cannot spread through this hyperedge. We develop a generalized percolation theory to investigate the epidemic outbreak size and threshold. We find that DCT can effectively suppress the epidemic spreading, i.e., decreasing the outbreak size and enlarging the threshold. DCT limits the spread of the epidemic to larger cardinality of hyperedges. On real-world networks, the inhibitory effect of DCT on the spread of epidemics is evident when the spread of epidemics is small.
网络拓扑中出现的高阶相互作用会影响数字接触追踪（DCT）的有效性。在本文中，我们提出了一个数学模型，其中我们使用超图来描述聚集事件。在我们的模型中，DCT的作用被建模为携带应用程序的个体。当超边中的个体都携带该应用程序时，流行病就无法通过这条超边传播。我们发展了一种广义渗流理论来研究疫情爆发规模和阈值。我们发现DCT可以有效地抑制疫情传播，即减小爆发规模并提高阈值。DCT将疫情传播限制在更大基数的超边中。在现实世界的网络中，当疫情传播较小时，DCT对疫情传播的抑制作用是明显的。
