Transmission characteristic and dynamic analysis of COVID-19 on contact network with Tianjin city in China.

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.