Early epidemiological analysis of the 2019-nCoV outbreak based on a crowdsourced data

  1. Kaiyuan Sun
  2. Jenny Chen
  3. Cécile Viboud

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Abstract

As the outbreak of novel 2019 coronavirus (2019-nCoV) progresses within China and beyond, there is a need for rapidly available epidemiological data to guide situational awareness and intervention strategies. Here we present an effort to compile epidemiological information on 2019-nCoV from media news reports and a physician community website (dxy.cn) between Jan 20, 2020 and Jan 30, 2020, as the outbreak entered its 7 th week. We compiled a line list of patients reported in China and internationally and daily case counts by Chinese province. We describe the demographics, hospitalization and reporting delays for 288 patients, over time and geographically. We find a decrease in case detection lags in provinces outside of Wuhan and internationally, compared to Wuhan, and after Jan 18, 2020, as outbreak awareness increased. The rapid progression of reported cases in different provinces of China is consistent with local transmission beyond Wuhan. The age profile of cases points at a deficit among children under 15 years of age, possibly related to prior immunity with related coronavirus or behavioral differences. Overall, our datasets, which have been publicly available since Jan 21, 2020, align with official reports from Chinese authorities published more than a week later. Availability of publicly available datasets in the early stages of an outbreak is important to encourage disease modeling efforts by independent academic modeling teams and provide robust evidence to guide interventions.

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  1. ScreenIT

    SciScore for 10.1101/2020.01.31.20019935: (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

    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:
    As a caveat, this is an early report of a rapidly evolving situation and the parameters discussed here could change quickly. In the coming weeks, we will continue to monitor the epidemiology of this outbreak using data from news reports and official sources.

    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.

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  2. Version published to 10.1101/2020.01.31.20019935 on medRxiv