The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

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Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern—particularly Alpha, Beta, Delta, and Omicron—on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century.

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  1. SciScore for 10.1101/2022.04.17.22273906: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    Due to the sheer size of the dataset we opted to perform independent phylogenetic inferences on the main VOCs (Alpha n=2 365; Beta n=10 809; Delta n=13 911) that have spread on the African continent, as well as a separate inference for all non-VOC SARS-CoV-2 sequences (n=13 252).
    suggested: (BETA, RRID:SCR_007556)
    Each of the alignments were then used to infer maximum likelihood (ML) tree topologies in FastTree v 2.0 (45) using the General Time Reversible (GTR) model of nucleotide substitution and a total of 100 bootstrap replicates (46).
    suggested: (FastTree, RRID:SCR_015501)
    The resulting ML tree topologies were first inspected in TempEst (47) to identify any sequences that deviate more than 0.0001 from the residual mean.
    suggested: (TempEst, RRID:SCR_017304)
    Using a custom python script we could then count the number of state changes by iterating over each phylogeny from the root to the external tips.
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    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.
    • Thank you for including a protocol registration statement.

    Results from scite Reference Check: We found no unreliable references.

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