Social Network Analysis of COVID-19 Transmission in Karnataka, India

  1. S. Saraswathi
  2. A. Mukhopadhyay
  3. H. Shah
  4. T.S. Ranganath

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Abstract

We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and assess the potential of SNA as a tool for outbreak monitoring and control. We analyzed contact tracing data of 1147 Covid-19 positive cases (mean age 34.91 years, 61.99% aged 11–40, 742 males), anonymized and made public by the government. We used software tools Cytoscape and Gephi to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links, determined by contact tracing, from source to target patients). Outdegree was 1–47 for 199 (17.35%) nodes, and betweenness 0.5–87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) “super-spreaders” (outdegree≥5) caused 60% of the transmissions. Real-time social network visualization can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritizing these areas and individuals for rigorous containment could help minimize resource outlay and potentially achieve a significant reduction in COVID-19 transmission.

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  1. Version published to 10.1101/2020.08.11.20172734v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.08.11.20172734: (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
    We collected anonymized contact tracing data from daily government bulletins, and tabulated and summarized relevant demographic details such as age, district of residence, and history of travel, using Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    We created nodes and links tables in Excel, with each node representing a patient, and each link (edge), a confirmed contact between a source and a target patient.
    Excel
    suggested: None
    We imported this dataset into Gephi version 0.9.2 and applied the following sequence of layout algorithms: YiFan Hu Proportional, Fruchterman Reingold, and ‘No Overlap,’ to achieve a visual representation in which the more connected nodes are placed centrally, and ones with lower connectivity are placed towards the periphery of the network[8].
    Gephi
    suggested: (Gephi, RRID:SCR_004293)
    Layout algorithms provided in Cytoscape were applied in the following sequence: Compound Spring Embedder (CoSE) and yFiles Remove Overlap, followed by a few manual adjustments to improve clarity.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    Network attributes generated by Gephi were analyzed using MS Excel to explain relevant aspects of the network and its components.
    MS Excel
    suggested: None

    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: We detected the following sentences addressing limitations in the study:
    Limitations: Our SNA findings may not universally reflect field realities. Some findings such as eccentricity and mean path length are theoretical constructs computed by software algorithms, but in practice, these metrics remain indeterminate as our network had very few inter-district connections and many isolated nodes and components. Our dataset included many patients with contact tracing still under investigation at the time of analysis. We were not able to analyze the role of type and duration of contact, as the data for these were not available for many patients.

    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.

  3. Version published to 10.1101/2020.08.11.20172734v1 on medRxiv
  4. Version published to 10.1017/s095026882000223x