Genomic epidemiology of a densely sampled COVID-19 outbreak in China

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Abstract

Analysis of genetic sequence data from the SARS-CoV-2 pandemic can provide insights into epidemic origins, worldwide dispersal, and epidemiological history. With few exceptions, genomic epidemiological analysis has focused on geographically distributed data sets with few isolates in any given location. Here, we report an analysis of 20 whole SARS- CoV-2 genomes from a single relatively small and geographically constrained outbreak in Weifang, People’s Republic of China. Using Bayesian model-based phylodynamic methods, we estimate a mean basic reproduction number (R0) of 3.4 (95% highest posterior density interval: 2.1–5.2) in Weifang, and a mean effective reproduction number (Rt) that falls below 1 on 4 February. We further estimate the number of infections through time and compare these estimates to confirmed diagnoses by the Weifang Centers for Disease Control. We find that these estimates are consistent with reported cases and there is unlikely to be a large undiagnosed burden of infection over the period we studied.

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  1. SciScore for 10.1101/2020.03.09.20033365: (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
    SentencesResources
    Phylogenetic analysis: We aligned the 20 Weifang sequences using MAFFT (Katoh and Standley, 2013) with a previous alignment of 50 non-identical SARS-CoV 2 sequences from outside of Weifang (Volz etal., 2020), provided by GISAID (Elbe and Buckland-Merrett, 2017).
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Bayesian phylogenetic analysis was carried out using BEAST 2.6.1 (Bouckaert et al., 2019) using a HKY+G4 substitution model and a strict molecular clock.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    The phylodynamic model was implemented using the PhyDyn package v1.3.7 (Volz and Siveroni, 2018) using the QL likelihood approximation and the RKODE solver.
    PhyDyn
    suggested: (PhyDyn, RRID:SCR_018544)

    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.
    • No protocol registration statement was detected.

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