The potential impact of the COVID-19 pandemic on the tuberculosis epidemic a modelling analysis

  1. Lucia Cilloni
  2. Han Fu
  3. Juan F Vesga
  4. David Dowdy
  5. Carel Pretorius
  6. Sevim Ahmedov
  7. Sreenivas A. Nair
  8. Andrei Mosneaga
  9. Enos Masini
  10. Suvanand Sahu
  11. Nimalan Arinaminpathy

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Abstract

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  1. Version published to 10.1016/j.eclinm.2020.100603
  2. ScreenIT

    SciScore for 10.1101/2020.05.16.20104075: (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
    Role of the funding source: SA is employed by USAID and SAN, AM, EM and SS are employed by the Stop TB Partnership.
    USAID
    suggested: (USAID, RRID:SCR_012846)

    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.

    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 10.1101/2020.05.16.20104075 on medRxiv