Phylodynamic insights on the early spread of the COVID-19 pandemic and the efficacy of intervention measures

  1. Jiansi Gao
  2. Michael R. May
  3. Bruce Rannala
  4. Brian R. Moore

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Abstract

We performed phylodynamic analyses of all available SARS-CoV-2 genomes from the early phase of the COVID-19 pandemic—combined with a novel dataset on contemporary global air-travel volume—to assess the efficacy of public-health measures on viral geographic spread. Globally, viral dispersal rates are significantly correlated with air-travel volume, and widespread international air-travel bans imposed against China by early February coincide with a significant reduction in geographic viral spread. In North America, the efficacy of this travel ban was temporary, possibly due to the lack of both containment measures against other infected regions and domestic mitigation measures. By contrast, in China, domestic mitigation measures were correlated with a long-term reduction in viral spread, despite repeated international introductions. Our study supports a role for both targeted international containment and domestic mitigation measures as critical components of a more comprehensive public-health strategy to mitigate future outbreaks caused by the emergence of novel SARS-CoV-2 variants.

Phylodynamic analyses reveal that variation in rates of early geographic spread of COVID-19 are correlated with intervention measures.

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  1. ScreenIT

    SciScore for 10.1101/2021.05.01.442286: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The repositories also contain BEAST XML scripts used to perform the phylodynamic analyses, R scripts used to perform simulations and post processing, and a modified version of the BEAST program used for some of the analyses in this study.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)

    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: 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.

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

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.05.01.442286v1 on bioRxiv