Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China

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Abstract

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  1. SciScore for 10.1101/2020.03.06.20032177: (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

    No key resources detected.


    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:
    The study has limitations. The present reported data are insufficient to understand the full epidemiological pattern of COVID-19 transmission and new potential outbreaks. For example, the estimates in this manuscript have a certain extent of uncertainty and delays due to the limitations in reporting mechanisms over the course of the natural history of the cases, the impact of other potential asymptomatic cases and some unreported cases. Some studies conducted assumption that a small fraction, 20%, were not reported(Liu et al., 2020b) and others reported the estimated asymptomatic proportion was 17.9%(Mizumoto et al., 2020) or 60%(Qiu, 2020).Evidently, such asymptomatic infectious cases are not fully reported by authorities. However, some studies suggested crowdsourced data could be compiled and analyzed as an complementation of officially released data, this perhaps help improving the analysis results(Arbia, 2020;Leung and Leung, 2020;Sun et al., 2020). In the future, we will evaluate how will the number of unreported cases influence the severity of the epidemic and consider involving these unreported data to our mathematical model. As concluded from the WHO-China Joint Mission report(Organization), the COVID-19 transmission dynamics are inherently contextual, as are the dynamics for any outbreak, and people worldwide need to work together to defend against this disease, including 1) to enhance the understanding of the evolving COVID-19 outbreak in China and the nature and th...

    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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