The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance
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Abstract
Investment in Africa over the past year with regards to SARS-CoV-2 genotyping has led to a massive increase in the number of sequences, exceeding 100,000 genomes generated to track the pandemic on the continent. Our results show an increase in the number of African countries able to sequence within their own borders, coupled with a decrease in sequencing turnaround time. Findings from this genomic surveillance underscores the heterogeneous nature of the pandemic but we observe repeated dissemination of SARS-CoV-2 variants within the continent. Sustained investment for genomic surveillance in Africa is needed as the virus continues to evolve, particularly in the low vaccination landscape. These investments are very crucial for preparedness and response for future pathogen outbreaks.
One-Sentence Summary
Expanding Africa SARS-CoV-2 sequencing capacity in a fast evolving pandemic.
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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 Sentences Resources 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). Betasuggested: (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). FastTreesuggested: (FastTree, RRID:SCR_015501)Th… 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 Sentences Resources 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). Betasuggested: (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). FastTreesuggested: (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. TempEstsuggested: (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. pythonsuggested: NoneResults 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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