Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19

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Abstract

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  1. SciScore for 10.1101/2021.06.10.21257749: (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
    4.3 EpiTopics-Stage 1: Unsupervised dynamic embedded topic: The first stage of EpiTopics (Fig. 1a) is built upon our previous model called MixMedia [13], which was adapted from The Embedded Topic Model (ETM) [22] and The Dynamic Embedded Topic Model (DETM) [14].
    MixMedia
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.


    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.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

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