A bibliometric and co-occurrence analysis of COVID-19–related literature published between December 2019 and June 2020

  1. Md. Sayeed Al-Zaman

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Abstract

Purpose: The main purposes of this study were to analyze the document types and languages of published papers on coronavirus disease 2019 (COVID-19), along with the top authors, publications, countries, institutions, and disciplines, and to analyze the co-occurrence of keywords and bibliographic coupling of countries and sources of the most-cited COVID-19 literature.Methods: This study analyzed 16,384 COVID-19 studies published between December 2019 and June 2020. The data were extracted from the Web of Science database using four keywords: “COVID-19,” “coronavirus,” “2019-nCoV,” and “SARS-CoV-2.” The top 500 mostcited documents were analyzed for bibliographic and citation network visualization.Results: The studies were published in 19 different languages, and English (95.313%) was the most common. Of 157 research-producing countries, the United States (25.433%) was in the leading position. Wang Y (n=94) was the top author, and the <i>BMJ</i> (n=488) was the top source. The University of London (n=488) was the leading organization, and medicine-related papers (n=2,259) accounted for the highest proportion. The co-occurrence of keywords analysis identified “coronavirus,” “COVID-19,” “SARS-CoV-2,” “2019-nCoV,” and “pneumonia” as the most frequent words. The bibliographic coupling analysis of countries and sources showed the strongest collaborative links between China and the United States and between the <i>New England Journal of Medicine</i> and the <i>JAMA</i>.Conclusion: Collaboration between the United States and China was key in COVID-19 research during this period. Although BMJ was the leading title for COVID-19 articles, the co-author link between <i>New England Journal of Medicine</i> and <i>JAMA</i> was the strongest.

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  1. Version published to 10.6087/kcse.230
  2. ScreenIT

    SciScore for 10.1101/2020.07.15.20154989: (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
    It was transformed, restructured, and imported in IBM SPSS Statistics 25 for the final analysis.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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

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  3. Version published to 10.1101/2020.07.15.20154989v1 on medRxiv