Spatial Visualization of Cluster-Specific COVID-19 Transmission Network in South Korea During the Early Epidemic Phase

  1. James Yeongjun Park

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Abstract

Background

Coronavirus disease 2019 (COVID-19) has been rapidly spreading throughout China and other countries including South Korea. As of March 12, 2020, a total number of 7,869 cases and 66 deaths had been documented in South Korea. Although the first confirmed case in South Korea was identified on January 20, 2020, the number of confirmed cases showed a rapid growth on February 19, 2020 with a total number of 1,261 cases with 12 deaths based on the Korea Centers for Disease Control and Prevention (KCDC).

Method

Using the data of confirmed cases of COVID-19 in South Korea that are publicly available from the KCDC, this paper aims to create spatial visualizations of COVID-19 transmission between January 20, 2020 and February 19, 2020.

Results

Using spatial visualization, this paper identified two early transmission clusters in South Korea (Daegu cluster and capital area cluster). Using a degree-weighted centrality measure, this paper proposes potential super-spreaders of the virus in the visualized clusters.

Conclusion

Compared to various epidemiological measures such as the basic reproduction number, spatial visualizations of the cluster-specific transmission networks and the proposed centrality measure may be more useful to characterize super-spreaders and the spread of the virus especially in the early epidemic phase.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.18.20038638: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All the analysis and visualizations are performed using the ggplot2 software in R as well as Cytoscape.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.03.18.20038638v2 on medRxiv
  3. Version published to 10.1101/2020.03.18.20038638v1 on medRxiv