Changed transmission epidemiology of COVID-19 at early stage: A nationwide population-based piecewise mathematical modelling study

  1. Jiawen Hou
  2. Jie Hong
  3. Boyun Ji
  4. Bowen Dong
  5. Yue Chen
  6. Michael P. Ward
  7. Wei Tu
  8. Zhen Jin
  9. Jian Hu
  10. Qing Su
  11. Wenge Wang
  12. Zheng Zhao
  13. Shuang Xiao
  14. Jiaqi Huang
  15. Wei Lin
  16. Zhijie Zhang

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Abstract

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  1. Version published to 10.1016/j.tmaid.2020.101918
  2. ScreenIT

    SciScore for 10.1101/2020.03.27.20045757: (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
    Data sources: Estimation and prediction: The key parameters in the above models were estimated using MATLAB R2018b through the least square technique.
    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:
    There are several limitations for this study. Firstly, individuals with no or only mild symptoms might not seek treatment, and therefore, not be included in the dataset, especially for epidemic stages 1 and 2. This reporting bias could result in underestimation for the number of cases as well as R0. Secondly, we did not include epidemic data from Hong Kong, Macau, Taiwan, and foreign countries. Spatial and temporal heterogeneity would have been larger if these data were included. Finally, we used data from laboratory diagnosed cases, and therefore, the number of predicted cases after February 12 would be lower than the officially reported number of both clinically and laboratory diagnosed cases, when the revised reporting rule was implemented; unfortunately, it is not possible to differentiate the numbers of clinically- and laboratory-diagnosed cases.

    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 10.1101/2020.03.27.20045757v1 on medRxiv