Modeling the Epidemic Growth of Preprints on COVID-19 and SARS-CoV-2

  1. Giovani L. Vasconcelos
  2. Luan P. Cordeiro
  3. Gerson C. Duarte-Filho
  4. Arthur A. Brum

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Abstract

The response of the scientific community to the global health emergency caused by the COVID-19 pandemic has produced an unprecedented number of manuscripts in a short period of time, the vast majority of which have been shared in the form of preprints posted on online preprint repositories before peer review. This surge in preprint publications has in itself attracted considerable attention, although mostly in the bibliometrics literature. In the present study we apply a mathematical growth model, known as the generalized Richards model, to describe the time evolution of the cumulative number of COVID-19 related preprints. This mathematical approach allows us to infer several important aspects concerning the underlying growth dynamics, such as its current stage and its possible evolution in the near future. We also analyze the rank-frequency distribution of preprints servers, ordered by the number of COVID-19 preprints they host, and find that it follows a power law in the low rank (high frequency) region, with the high rank (low frequency) tail being better described by a q -exponential function. The Zipf-like law in the high frequency regime indicates the presence of a cumulative advantage effect, whereby servers that already have more preprints receive more submissions.

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  1. Version published to 10.3389/fphy.2021.603502
  2. ScreenIT

    SciScore for 10.1101/2020.09.08.20190470: (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
    There are preprint repositories that specialize in certain areas, such as: arXiv for physics and mathematics; bioRxiv and medRxiv for biomedical sciences; SSRN for the social sciences; and RePEc for economics research.
    arXiv
    suggested: (arXiv, RRID:SCR_006500)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    In the next section we shall investigate the evidence of power-law behavior in the frequency distribution of COVID-19 preprints by repositories. 2.4 Statistical Fits: To perform the statistical fit for the GRM, we employed the Levenberg-Marquardt algorithm to solve the non-linear least square optimization problem, as implemented in the lmfit package for Python [27], which provides the parameter estimates and their respective errors.
    Python
    suggested: (IPython, RRID:SCR_001658)
    The computer codes for the statistical fits were written in the Python language, and the plots were produced with the data visualisation library Matplotlib.
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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.

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  3. Version published to 10.1101/2020.09.08.20190470 on medRxiv