Detecting Rapid Spread of SARS-CoV-2 Variants, France, January 26–February 16, 2021

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Abstract

No abstract available

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  1. SciScore for 10.1101/2021.02.20.21251927: (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

    No key resources detected.


    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:
    A limitation of this study is that in spite of its density, sampling was performed retrospectively. This could generate biases if, for instance, transmission chains associated with variants are increasingly sampled. However, we do know if the sample was performed in a hospital and find that these are associated with a lower probability to detect a variant. This is consistent with the 14-day delay between infection and hospitalisation [12]. Another limitation is that specific RT-PCR does not have the resolution of full genome sequencing and other variants of concern may be underestimated or missed with this approach. However, the time scale considered and the relatively slow evolutionary rate of SARS-CoV-2 make this approach appropriate to monitor variant spread. Furthermore, it allows for exhaustive and timely testing of all the positive tests. These results illustrate that variant-specific RT-PCRs are an interesting option for SARS-CoV-2 epidemic monitoring because of their cheap price and rapid deployment. They also reveal that the spread of B.1.1.7, B.1.153, and P.1 SARS-CoV-2 variants in France is faster than predicted [13], although with strong spatial heterogeneity, which stresses the importance of swift public health responses through vaccination and non-pharmaceutical interventions.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04738331RecruitingAnalysing the French COVID-19 Epidemic Using a National SARS…


    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.

    About SciScore

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