Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations

  1. Houssein H. Ayoub
  2. Hiam Chemaitelly
  3. Ghina R. Mumtaz
  4. Shaheen Seedat
  5. Susanne F. Awad
  6. Monia Makhoul
  7. Laith J. Abu-Raddad

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Abstract

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  1. Version published to 10.1016/j.gloepi.2020.100042
  2. ScreenIT

    SciScore for 10.1101/2020.04.08.20058214: (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
    Analyses were performed in MATLAB R2019a [7].
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    This study has limitations. Model projections are contingent on the quality and representativeness of the input data. For instance, we assumed infection levels to be as officially documented, but evidence suggested that many infections may have been undocumented, particularly in the early phase of the outbreak [12]. The natural history of this infection is not yet firmly established, and the case management protocols have evolved over time [19, 20]. Mortality data seem to suggest that the standard of care improved over time especially in recent weeks when the healthcare sector was no longer overwhelmed with a large case load. We assumed a time-independent risk of disease mortality, and therefore we may have overestimated the true (final) CFR. We used a deterministic compartmental model, but this type of model may not be representative of the stochastic transmission dynamics when the number of infections is small, such as in the very early phase of the epidemic, thereby adding uncertainty to our estimate for the day of outbreak emergence. Despite these limitations, our parsimonious model, tailored to the nature of available data, was able to reproduce the COVID-19 epidemic as observed in China, and provided insights about infection transmission and disease progression in the population.

    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.

    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.

  3. Version published to 10.1101/2020.04.08.20058214v1 on medRxiv