Serological identification of SARS-CoV-2 infections among children visiting a hospital during the initial Seattle outbreak

  1. Adam S. Dingens
  2. Katharine H. D. Crawford
  3. Amanda Adler
  4. Sarah L. Steele
  5. Kirsten Lacombe
  6. Rachel Eguia
  7. Fatima Amanat
  8. Alexandra C. Walls
  9. Caitlin R. Wolf
  10. Michael Murphy
  11. Deleah Pettie
  12. Lauren Carter
  13. Xuan Qin
  14. Neil P. King
  15. David Veesler
  16. Florian Krammer
  17. Jane A. Dickerson
  18. Helen Y. Chu
  19. Janet A. Englund
  20. Jesse D. Bloom

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Abstract

Children are strikingly underrepresented in COVID-19 case counts. In the United States, children represent 22% of the population but only 1.7% of confirmed SARS-CoV-2 cases as of April 2, 2020. One possibility is that symptom-based viral testing is less likely to identify infected children, since they often experience milder disease than adults. Here, to better assess the frequency of pediatric SARS-CoV-2 infection, we serologically screen 1,775 residual samples from Seattle Children’s Hospital collected from 1,076 children seeking medical care during March and April of 2020. Only one child was seropositive in March, but seven were seropositive in April for a period seroprevalence of ≈1%. Most seropositive children (6/8) were not suspected of having had COVID-19. The sera of seropositive children have neutralizing activity, including one that neutralized at a dilution > 1:18,000. Therefore, an increasing number of children seeking medical care were infected by SARS-CoV-2 during the early Seattle outbreak despite few positive viral tests.

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  1. Version published to 10.1038/s41467-020-18178-1
  2. ScreenIT

    SciScore for 10.1101/2020.05.26.20114124: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The sample collection and this study were approved by the Institutional Review Boards of Seattle Children′s Hospital and the University of Washington.
    Consent: This study was granted a waiver of consent since it used residual clinical samples and existing clinical data.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Each plate also contained two positive control wells (CR302218,19, an anti-SARS-CoV-1 monoclonal antibody that reacts to the SARS-CoV-2 RBD, at 0.5 µg/mL) and two negative control wells (pooled human sera taken from 2017-2018 (Gemini Biosciences, 100-110, lot H86W03J, pooled from 75 donors).
    anti-SARS-CoV-1
    suggested: None
    Goat anti-human IgG-Fc horseradish peroxidase (HRP)-conjugated antibody (Bethyl Labs, A80-104P) was diluted 1:3,000 in PBS-T containing 1% milk and 50 µL was added to each well.
    Goat anti-human IgG-Fc horseradish peroxidase
    suggested: (Novus Cat# NB 7449, RRID:AB_524652)
    anti-human IgG-Fc
    suggested: (Bethyl Cat# A80-104P, RRID:AB_67064)
    This assay, which detects IgG antibodies to SARS-CoV-2 nucleocapsid protein, was run on the Abbott Architect instrument according to manufacturer′s instructions.
    SARS-CoV-2 nucleocapsid protein ,
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Target cells were HEK-293T cells transduced to express hACE230 (BEI Resources, NR-52511).
    HEK-293T
    suggested: None
    Software and Algorithms
    SentencesResources
    This assay, which detects IgG antibodies to SARS-CoV-2 nucleocapsid protein, was run on the Abbott Architect instrument according to manufacturer′s instructions.
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    Neutralization curves were plotted using the neutcurve Python package (https://jbloomlab.github.io/neutcurve/, 0.3.1).
    Python
    suggested: (IPython, RRID:SCR_001658)

    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 10.1101/2020.05.26.20114124 on medRxiv