The epidemiological characteristics of 2019 novel coronavirus diseases (COVID-19) in Jingmen, Hubei, China

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Abstract

There is currently a global outbreak of coronavirus disease 2019 (COVID-19), and its epidemic characteristics in the areas where the outbreak has been successfully controlled are rarely reported.

Describe the epidemic characteristics of COVID-19 in Jingmen, Hubei, introduce the local prevention and control experience, and observe the impact of various prevention and control measures on the number of new cases.

All the COVID-19 patients diagnosed in the municipal districts of Jingmen from January 12 to February 29, 2020 were enrolled in this study. We described epidemiological data and observed the impact of control measures on the epidemic.

Of the 219 cases (110 men and 109 women), 88 (40%) had exposure to Wuhan. The median age was 48 years (range, 2–88 years; IQR, 35–60). Thirty-three severe patients with a median age of 66 years (range, 33–82 years, IQR, 57–76) were treated in intensive care units; out of these patients, 66.7% (22) were men and 19 (57.5%) had chronic diseases, including hypertension, diabetes, heart failure, stroke, and renal insufficiency. Under the control measures, the number of new patients gradually decreased and nearly disappeared after 18 days. Wearing masks in all kinds of situations prevents most infections and is one of the most effective prevention and control measures.

In conclusion, all people are susceptible to COVID-19, and older males and those with comorbid conditions are more likely to become severe cases. Even though COVID-19 is highly contagious, control measures have proven to be very effective, particularly wearing masks, which could prevent most infections.

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  1. SciScore for 10.1101/2020.03.07.20031393: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the First People’s Hospital of Jingmen Ethics Committee.
    Consent: The patients’ written informed consent was obtained before data was collected.
    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: We detected the following sentences addressing limitations in the study:
    There were three limitations in this study. First, only 219 patients diagnosed with COVID-19 were included. Second, the unconfirmed suspected cases in the early stages were excluded in the analyses. Some patients were discharged as suspected patients after one negative nucleic acid test. Many patients had a second or third check before being diagnosed. Third, most of the data were from patients’ medical records, with the potential of a few of them not being accurate enough. In conclusion, all people are susceptible to COVID-19, and older males and those with comorbid conditions are more likely to become severe cases. Even though COVID-19 is highly contagious, control measures have proven to be very effective, particularly wearing masks, which could prevent most infections. Declaration of interests We declare no competing interests

    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.