SARS-CoV-2 Genetic Diversity and Lineage Dynamics in Egypt during the First 18 Months of the Pandemic

  1. Wael H. Roshdy
  2. Mohamed K. Khalifa
  3. James Emmanuel San
  4. Houriiyah Tegally
  5. Eduan Wilkinson
  6. Shymaa Showky
  7. Darren Patrick Martin
  8. Monika Moir
  9. Amel Naguib
  10. Nancy Elguindy
  11. Mokhtar R. Gomaa
  12. Manal Fahim
  13. Hanaa Abu Elsood
  14. Amira Mohsen
  15. Ramy Galal
  16. Mohamed Hassany
  17. Richard J. Lessells
  18. Ahmed A. Al-Karmalawy
  19. Rabeh EL-Shesheny
  20. Ahmed M. Kandeil
  21. Mohamed A. Ali
  22. Tulio de Oliveira

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Abstract

COVID-19 was first diagnosed in Egypt on 14 February 2020. By the end of November 2021, over 333,840 cases and 18,832 deaths had been reported. As part of the national genomic surveillance, 1027 SARS-CoV-2 near whole-genomes were generated and published by the end of July 2021. Here we describe the genomic epidemiology of SARS-CoV-2 in Egypt over this period using a subset of 976 high-quality Egyptian genomes analyzed together with a representative set of global sequences within a phylogenetic framework. A single lineage, C.36, introduced early in the pandemic was responsible for most of the cases in Egypt. Furthermore, to remain dominant in the face of mounting immunity from previous infections and vaccinations, this lineage acquired several mutations known to confer an adaptive advantage. These results highlight the value of continuous genomic surveillance in regions where VOCs are not predominant and the need for enforcement of public health measures to prevent expansion of the existing lineages.

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  1. Version published to 10.3390/v14091878
  2. ScreenIT

    SciScore for 10.1101/2022.01.05.22268646: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethics statement: Ethical approval was obtained from the Ethics Committee of the National Research Centre, Giza, Egypt protocol number 14155, on 22 March 2020.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Firstly, the topology was assessed in TemPest (Rambaut et al., 2016) to remove any potential outlier sequences.
    TemPest
    suggested: (TempEst, RRID:SCR_017304)
    A custom python script was used to count the number of discrete changes occurring as we transcend the topology from the root towards the tips.
    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: We detected the following sentences addressing limitations in the study:
    The main limitations of this study were (i) that most of the analysed genomic sequences where from a single Egyptian city (i.e. Cairo) and (ii) that the volume of sequencing was lower during the second and third waves that it was during the first wave. These two limitations greatly complicated the reconstruction of viral transmission dynamics within the country. There is a need for a more robust surveillance strategy in Egypt and this need to be met with sustainable funding to support genomic epidemiology initiatives to ensure that representative samples are collected across the country at regular time-intervals. A final important limitation of our study also is that the epidemiological data needed to contextualize much of the genomic sequencing data was absent: for example, patient travel details that might have been useful to corroborate the inferred importation and exportation events were not generally available. Epidemiological data of this sort is vital because the slow evolutionary rate of the virus at the onset of the pandemic meant that genetic linkages between sequences in different countries does not categorically prove that viral movements occurred between those countries. In summary, by combining genomic and epidemiological data, we have been able to illuminate the viral dynamics behind the COVID19epidemic in Egypt. Particularly, we have shown that if not controlled, non-VOC lineages can evolve rapidly within a single country to acquire adaptive and pathogenic pro...

    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

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  3. Version published to 10.1101/2022.01.05.22268646v1 on medRxiv