ArticlePhysica A2022
Transmission characteristic and dynamic analysis of COVID-19 on contact network with Tianjin city in China.
Article in Physica A, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 10 citations in OpenAlex.
- Network epidemiological analysis of COVID-19 transmission patterns by age, occupation and residence across four waves in Cyprus.Scientific reports · 2025Article
- The Impact of Serum/Plasma Proteomics on SARS-CoV-2 Diagnosis and Prognosis.International journal of molecular sciences · 2024Review
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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