Epidemiological Investigation and Prevention Control Analysis of the Longitudinal Distribution of COVID-19 in Henan Province, China

  1. Xianguang Yang
  2. Xuelin Chen
  3. Cuihong Ding
  4. Zhibo Bai
  5. Jingyi Zhu
  6. Gege Sun
  7. Guoying Yu

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Abstract

Epidemic prevention and control in China have entered a new stage of normalization. This article analyzes the epidemiological characteristics of COVID-19 in Henan Province and summarizes the effective disease prevention and control means and measures at the prefecture level; the normalized private data provide a theoretical reference for the formulation and conduct of future prevention and control work. At the same time, these epidemic prevention and control findings can also be used for reference in other countries and regions.

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  1. Version published to 10.1128/msphere.00867-20
  2. ScreenIT

    SciScore for 10.1101/2020.07.25.20161844: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    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: 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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2020.07.25.20161844v1 on medRxiv