Determinants of COVID-19 incidence and mortality: A cross-country analysis

  1. Gursimer Jeet
  2. Atul Sharma
  3. Aarti Goyal
  4. Shankar Prinja
  5. Arvind Pandey
  6. Sandip Mandal
  7. Mendu Vishnuvardhana Rao
  8. Sumit Aggarwal
  9. Narendra K Arora

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Abstract

The authors have withdrawn this manuscript because the source data was last updated at the end of March, and hence the authors believe that the results derived and conclusions drawn in the manuscript may not hold true now. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

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  1. Version published to 10.1101/2020.06.11.20128561v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.06.11.20128561: (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

    No key resources detected.

    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.

  3. Version published to 10.1101/2020.06.11.20128561v1 on medRxiv