Studies of Novel Coronavirus Disease 19 (COVID-19) Pandemic: A Global Analysis of Literature

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Abstract

Novel coronavirus disease 19 (COVID-19) is a global threat to millions of lives. Enormous efforts in knowledge production have been made in the last few months, requiring a comprehensive analysis to examine the research gaps and to help guide an agenda for further studies. This study aims to explore the current research foci and their country variations regarding levels of income and COVID-19 transmission features. This textual analysis of 5780 publications extracted from the Web of Science, Medline, and Scopus databases was performed to explore the current research foci and propose further research agenda. The Latent Dirichlet allocation was used for topic modeling. Regression analysis was conducted to examine country variations in the research foci. Results indicate that publications are mainly contributed by the United States, China, and European countries. Guidelines for emergency care and surgical, viral pathogenesis, and global responses in the COVID-19 pandemic are the most common topics. There is variation in the research approaches to mitigate COVID-19 problems in countries with different income and transmission levels. Findings highlighted the need for global research collaborations among high- and low/middle-income countries in the different stages of pandemic prevention and control.

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  1. SciScore for 10.1101/2020.05.05.20092635: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Searching strategy and selection criteria: Information on COVID-19 and SARS-CoV-2-related documents published until 23 April 2020 were extracted from the Medline, Scopus, and Web of Science (WoS) databases.
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    We first descriptively analyzed the number of publications in each country and presented these data by using Microsoft Excel’s Map function.
    Microsoft Excel’s
    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:
    However, some limitations should be notedd. The restriction of the search strategy to the English language might not reflect globalized practices and the research priority of a country, such as articles in the local language. Analyses of keywords, titles, and abstracts may not fully reflect the content of articles. However, with the combination of three large datasets and various techniques of text mining, this study is useful for an overview of the research direction.

    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.