Statistical Inference for Coronavirus Infected Patients in Wuhan

  1. Yong-Dao Zhou
  2. Jianghu James Dong

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Abstract

The new coronavirus outbreak has seriously affected the quality of life in China. Wuhan is the disaster area, where the number of cases has increased rapidly. However, the current measures of infected patients in Wuhan are still underestimated.

Objective

To estimate the overall infected patients in Wuhan from several sampled data. The correct estimated infected patients can be helpful for the government to arrange the needed beds in hospital wards to meet the actual needs.

Design

We proposed to use the sampling survey to estimate the overall infected patients in Wuhan. The sampling survey is a kind of non-comprehensive survey. It selected some units from all the survey objects to carry out the survey and made the estimation and inference to all the survey objects. Sampling surveys can obtain information that reflects the overall situation, although it is not a comprehensive survey.

Setting

We estimated the overall infection rate in Wenzhou city, which has a better data collection system. Simultaneously, another different samples of Wuhan tourists to Singapore will be used to validate the infection rate in Wenzhou city. Combined these two samples, we give the estimation of the number of infected patients in Wuhan and other prefecture-level cities in Hubei Province.

Participants

The number of people who returned from Wuhan to Wenzhou was selected from the daily notification of the pneumonia epidemic caused by a new coronavirus infection in the city.

Exposures for observational studies

The daily rate of the pneumonia epidemic caused by the new coronavirus infection in Wenzhou City. The numerator is the number of people diagnosed and whether each person diagnosed had a history of living in Wuhan. The denominator is the total number of people returning to Wenzhou from Wuhan. Based on this rate, it is reasonable to predict the number of the infected patients.

Main Outcome(s) and Measure(s)

According to the most conservative estimate from our proposed sampling method, at least total 54,000 infected patients are in Wuhan. Therefore, the current 8,000 beds in hospital wards and the 20,000 beds in square-class hospitals are far away from meeting the actual needs.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.02.10.20021774: (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.
    • 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.

  2. ScreenIT

    SciScore for 10.1101/2020.02.10.20021774: (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

    Software and Algorithms
    SentencesResources
    Conclusions: （1）The current measures of infected patients are yet to be upgraded.
    Conclusions
    suggested: None

    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).

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, please follow this link.

  3. Version published to 10.1101/2020.02.10.20021774 on medRxiv