Genomic and Epidemiological Analysis of SARS-CoV-2 Viruses in Sri Lanka

  1. Chandima Jeewandara
  2. Deshni Jayathilaka
  3. Diyanath Ranasinghe
  4. Nienyun Sharon Hsu
  5. Dinuka Ariyaratne
  6. Tibutius Thanesh Jayadas
  7. Deshan Madhusanka Panambara Arachchige
  8. Benjamin B. Lindsey
  9. Laksiri Gomes
  10. Matthew D. Parker
  11. Ananda Wijewickrama
  12. Malika Karunaratne
  13. Graham S. Ogg
  14. Thushan I. de Silva
  15. Gathsaurie Neelika Malavige

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Abstract

Background: In order to understand the molecular epidemiology of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in Sri Lanka, since March 2020, we carried out genomic sequencing overlaid on available epidemiological data until April 2021.

Methods: Whole genome sequencing was carried out on diagnostic sputum or nasopharyngeal swabs from 373 patients with COVID-19. Molecular clock phylogenetic analysis was undertaken to further explore dominant lineages.

Results: The B.1.411 lineage was most prevalent, which was established in Sri Lanka and caused outbreaks throughout the country until March 2021. The estimated time of the most recent common ancestor (tMRCA) of this lineage was June 1, 2020 (with 95% lower and upper bounds March 30 to July 27) suggesting cryptic transmission may have occurred, prior to a large epidemic starting in October 2020. Returning travellers were identified with infections caused by lineage B.1.258, as well as the more transmissible B.1.1.7 lineage, which has replaced B.1.411 to fuel the ongoing large outbreak in the country.

Conclusions: The large outbreak that started in early October, is due to spread of a single virus lineage, B.1.411 until the end of March 2021, when B.1.1.7 emerged and became the dominant lineage.

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  1. Version published to 10.3389/fmicb.2021.722838
  2. Version published to 10.1101/2021.05.05.21256384v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.05.05.21256384: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethical approval for the study was obtained by the Ethics Review Committee of the University of Sri Jayewardenepura.
    Field Sample Permit: Each sampling location was indicated by a coloured bubble proportionate to the number of sequences sampled within.
    Sex as a biological variablenot detected.
    RandomizationThe first step included randomly selecting one sequence per country per epi week.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The alignment and maximum likelihood tree construction were performed using MAFFT and IQ-TREE2 as described above.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)

    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.

  4. Version published to 10.1101/2021.05.05.21256384v1 on medRxiv