COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modeling

  1. Elba Raimúndez
  2. Erika Dudkin
  3. Jakob Vanhoefer
  4. Emad Alamoudi
  5. Simon Merkt
  6. Lara Fuhrmann
  7. Fan Bai
  8. Jan Hasenauer

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Abstract

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  1. Version published to 10.1016/j.epidem.2021.100439
  2. ScreenIT

    SciScore for 10.1101/2020.04.19.20071597: (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
    The calculation of parameter confidence intervals and MCMC sampling was carried out using the MATLAB toolboxes PESTO (https://github.com/ICB-DCM/PESTO) [78] and AMICI (https://github.com/ICB-DCM/AMICI) [81, 82].
    https://github.com/ICB-DCM/PESTO
    suggested: (PESTO, RRID:SCR_016891)
    The complete implementation (including the respective version of the used toolboxes) and data are available on ZENODO (https://doi.org/10.5281/zenodo.3757227).
    ZENODO
    suggested: (ZENODO, RRID:SCR_004129)
    This includes the MATLAB code as well as the the specification of the parameter estimation problems as PEtab files [84] (with the model in SBML format [85]).
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    In our opinion there are two reasons for this: Besides parameter estimation, the aforementioned limitations of case numbers were observed in the model selection process. The data did, for instance, not allow to unravel that a large number of the asymptomatic cases is not detected. We observed that detailed prior information is required if merely case numbers are employed for parameter estimation. While literature-based priors are used in many manuscripts [65], we hypothesise that it would be better to use information about individual cases for parameter estimation and model selection. In particular the date of the onset of symptoms, the date of the positive test, and the date of recovery/death for individuals is highly relevant. These data are being collected and analysed [54, 66–68], but should in the future be shared much earlier. Furthermore, randomised testing would be required, ideally using antibody tests to determine the fraction of completely asymptomatic patients. Such studies are usually not possible during an initial phase of a pandemic but are now on the way [69]. This study does not offer new insights into the COVID-19 pandemic. However, it pin-points important pitfalls and showcases the relevance of the underlying assumptions and the available data. Furthermore, it demonstrates that even a proper uncertainty analysis using state-of-the-art frequentist or Bayesian approaches does not ensure that the true parameters and dynamics are captured within the uncertainty...

    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

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  3. Version published to 10.1101/2020.04.19.20071597v1 on medRxiv