Emergence of SARS-CoV-2 variants of concern in the pediatric population of the United States

  1. Jennifer Dien Bard
  2. Moiz Bootwalla
  3. Amy Leber
  4. Paul Planet
  5. Ahmed M. Moustafa
  6. Rebecca Harris
  7. Pei Ying Chen
  8. Lishuang Shen
  9. Dejerianne Ostrow
  10. Dennis Maglinte
  11. Jessica Flores
  12. Roy Somak
  13. Sarangarajan Ranganathan
  14. Elizabeth Perlman
  15. Xiaotian Zheng
  16. Rangaraj Selvarangan
  17. Dithi Banerjee
  18. Meghan Delaney
  19. Joseph Campos
  20. Drew G. Michael
  21. Eric Vilain
  22. Jonathan LoTempio
  23. James Dunn
  24. Sarah Jung
  25. Samuel R. Dominguez
  26. Alexander R. Judkins
  27. Xiaowu Gai

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Abstract

The evolution of SARS-CoV2 virus has led to the emergence of variants of concern (VOC). Children, particularly <12 years old not yet eligible for vaccines, continue to be important reservoirs of SARS-CoV-2 yet VOC prevalence data in this population is lacking. We report data from a genomic surveillance program that includes 9 U.S. children’s hospitals. Analysis of SARS-CoV-2 genomes from 2119 patients <19 years old between 03/20 to 04/21 identified 252 VOCs and 560 VOC signature mutations, most from 10/20 onwards. 75.00% of the VOC signature mutations detected were in children <12 years old, including 32 cases of B.1.1.7 and 346 of B.1.419/B.1417. From 02/21 to 04/21, B.1.1.7 prevalence increased from 1.28% to 72.22% corresponding with the decline of B.1419/B.1417 from 52.57% to 5.56% at one institution. There continues to be a need for ongoing genomic surveillance, particularly among young children who will be the last groups to be vaccinated.

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  1. Version published to 10.1101/2021.05.22.21257660v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.05.22.21257660: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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.

    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 10.1101/2021.05.22.21257660v1 on medRxiv