Li Mingtao, Cui Jin, Zhang Juan, Pei Xin, Sun Guiquan
School of Mathematics, Taiyuan University of Technology, Taiyuan, China.
Complex System Research Center, Shanxi University, Taiyuan, China.
Physica A. 2022 Dec 15;608:128246. doi: 10.1016/j.physa.2022.128246. Epub 2022 Oct 14.
The outbreak of 2019 novel coronavirus pneumonia (COVID-19) has had a profound impact on people's lives around the world, and the spread of COVID-19 between individuals were mainly caused by contact transmission of the social networks. In order to analyze the network transmission of COVID-19, we constructed a case contact network using available contact data of 136 early diagnosed cases in Tianjin. Based on the constructed case contact network, the structural characteristics of the network were first analyzed, and then the centrality of the nodes was analyzed to find the key nodes. In addition, since the constructed network may contain missing edges and false edges, link prediction algorithms were used to reconstruct the network. Finally, to understand the spread of COVID-19 in the network, an individual-based susceptible-latent-exposed-infected-recover (SLEIR) model is established and simulated in the network. The results showed that the disease peak scale caused by the node with the highest centrality is larger, and reducing the contact infection rate of the infected person during the incubation period has a greater impact on the peak disease scale.
2019新型冠状病毒肺炎（COVID-19）疫情对全球人民的生活产生了深远影响，COVID-19在个体之间的传播主要是由社交网络的接触传播引起的。为了分析COVID-19的网络传播情况，我们利用天津市136例早期确诊病例的可用接触数据构建了一个病例接触网络。基于构建的病例接触网络，首先分析了网络的结构特征，然后分析了节点的中心性以找出关键节点。此外，由于构建的网络可能包含缺失边和虚假边，因此使用链路预测算法对网络进行重建。最后，为了了解COVID-19在网络中的传播情况，建立了一个基于个体的易感-潜伏-暴露-感染-康复（SLEIR）模型并在网络中进行模拟。结果表明，中心性最高的节点引起的疾病峰值规模更大，降低潜伏期感染者的接触感染率对疾病峰值规模有更大影响。
