Genetic diversity and evolution of SARS-CoV-2 in Belgium during the first wave outbreak

  1. Tony Wawina-Bokalanga
  2. Joan Martí-Carreras
  3. Bert Vanmechelen
  4. Mandy Bloemen
  5. Elke Wollants
  6. Lies Laenen
  7. Lize Cuypers
  8. Kurt Beuselinck
  9. Katrien Lagrou
  10. Emmanuel André
  11. Marc Van Ranst
  12. Piet Maes

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Abstract

SARS-CoV-2, the causative agent of COVID-19 was first detected in Belgium on 3rd February 2020, albeit the first epidemiological wave started in March and ended in June 2020. One year after the first epidemiological wave hit the country data analyses reveled the temporal and variant distribution of SARS-CoV-2 and its implication with Belgian epidemiological measures. In this study, 766 complete SARS-CoV-2 genomes of samples originating from the first epidemiological were sequenced to characterize the temporal and geographic distribution of the COVID-19 pandemic in Belgium through phylogenetic and variant analysis. Our analysis reveals the presence of the major circulating SARS-CoV-2 clades (G, GH and GR) and lineages circulating in Belgium at that time. Moreover, it contextualizes the density of SARS-CoV-2 cases over time with non-intervention measures taken to prevent the spread of SARS-CoV-2 in Belgium, specific international case imports and the functional implications of the most representative non-synonymous mutations present in Belgium between February to June 2020.

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  1. ScreenIT

    SciScore for 10.1101/2021.06.29.450330: (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.
    RandomizationSamples were randomly selected from a pseudo-anonymized database considering viral load (Ct < 20) and spread among collection dates and municipalities.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Sequencing was performed on MinION and GridION platforms using MinKNOW’s (v19.12) built-in basecalling, demultiplexing and adapter trimming (dual-barcode detection at 60% barcode sequence identity).
    MinION
    suggested: (MinION, RRID:SCR_017985)
    MinKNOW’s
    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.

    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.

  2. Version published to 10.1101/2021.06.29.450330v1 on bioRxiv