Second Wave of the COVID-19 Pandemic in Delhi, India: High Seroprevalence Not a Deterrent?

  1. Nandini Sharma
  2. Pragya Sharma
  3. Saurav Basu
  4. Ritika Bakshi
  5. Ekta Gupta
  6. Reshu Agarwal
  7. Kumar Dushyant
  8. Nutan Mundeja
  9. Zeasaly Marak
  10. Sanjay Singh
  11. Gautam Singh
  12. Ruchir Rustagi

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Abstract

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  1. Version published to 10.7759/cureus.19000
  2. ScreenIT

    SciScore for 10.1101/2021.09.09.21263331: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Ethical considerations: Written and informed consent for adults and assent and parental consent for minor participants was obtained.
    IRB: The study was approved by the Institutional Ethics Committee, Maulana
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The samples were transported and processed at the Clinical Virology Lab, Institute of Liver & Biliary Sciences, New Delhi. Anti SARS CoV-2 IgG antibodies were detected by using the VITROS assay on VITROS 3600 instrument based on Chemiluminescent technology as per the kit literature [4].
    Anti SARS CoV-2 IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    Statistical Analysis: the sociodemographic data of the participants collected through a brief interview schedule was entered in Microsoft Excel 2013 and merged with their antibody test results.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    The data were analysed with IBM SPSS Version 25 (Armonk, NY:
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    IBM Corp) and Stata 14 (StataCorp, USA).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.09.09.21263331v1 on medRxiv