The burden of active infection and anti-SARS-CoV-2 IgG antibodies in the general population: Results from a statewide sentinel-based population survey in Karnataka, India

  1. Giridhara R. Babu
  2. Rajesh Sundaresan
  3. Siva Athreya
  4. Jawaid Akhtar
  5. Pankaj Kumar Pandey
  6. Parimala S. Maroor
  7. M. Rajagopal Padma
  8. R. Lalitha
  9. Mohammed Shariff
  10. Lalitha Krishnappa
  11. C.N. Manjunath
  12. Mysore Kalappa Sudarshan
  13. Gopalkrishna Gururaj
  14. Timmanahalli Sobagaiah Ranganath
  15. Kumar D.E. Vasanth
  16. Pradeep Banandur
  17. Deepa Ravi
  18. Shilpa Shiju
  19. Eunice Lobo
  20. Asish Satapathy
  21. Lokesh Alahari
  22. Prameela Dinesh
  23. Vinitha Thakar
  24. Anita Desai
  25. Ambica Rangaiah
  26. Ashok Munivenkatappa
  27. Krishna S
  28. Shantala Gowdara Basawarajappa
  29. H.G. Sreedhara
  30. Siddesh KC
  31. Amrutha Kumari B
  32. Nawaz Umar
  33. Mythri BA
  34. Ravi Vasanthapuram

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Abstract

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  1. Version published to 10.1016/j.ijid.2021.05.043
  2. ScreenIT

    SciScore for 10.1101/2020.12.04.20243949: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Data collection: After obtaining written informed consent, information on basic demographic details, exposure history to laboratory-confirmed COVID-19 cases, symptoms suggestive of COVID-19 in the preceding one month, and clinical history were recorded on a web-based application designed specifically for the study and were linked to the samples using the ICMR Specimen Referral Forms for COVID-19.
    IRB: Ethical considerations: The Institutional Ethics Committee (IEC) of the Indian Institute of Public Health-Bengaluru campus reviewed and approved the study (vide.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    SARS-CoV-2-specific IgG antibodies were detected using a commercially available, validated, and ICMR-approved kit (Covid Kavach Anti SARS-CoV-2 IgG antibody detection ELISA, Zydus Cadila, India)19.
    SARS-CoV-2-specific IgG
    suggested: None
    Anti SARS-CoV-2 IgG
    suggested: None
    The results were interpreted as positive or negative for SARS-CoV-2 IgG antibodies based on the cut-off value of optical densities obtained with positive and negative samples provided in the kit.
    SARS-CoV-2 IgG
    suggested: None
    Statistical analysis: To enable joint estimation of IgG prevalence, active infection, and total prevalence, we first modelled an individual to be in one of four disease states: having active infection but no IgG antibodies, having IgG antibodies but no evidence of active infection, having both IgG antibodies and active infection, and finally having neither active infection nor IgG antibodies.
    no IgG antibodies, having IgG
    suggested: None
    The disease state of the individual is, however, hidden and can only be inferred from the RAT, the RT-PCR, and the IgG antibody test outcomes.
    IgG
    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: 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 10.1101/2020.12.04.20243949v1 on medRxiv