The time-series ages distribution of the reported COVID-2019 infected people suggests the undetected local spreading of COVID-2019 in Hubei and Guangdong provinces before 19 th Jan 2020

  1. Chao Wu
  2. Min Zheng

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Abstract

COVID-2019 is broken out in China. It becomes a severe public health disaster in one month. Find the period in which the spreading of COVID-2019 was overlooked, and understand the epidemiological characteristics of COVID-2019 in the period will provide valuable information for the countries facing the threats of COVID-2019. The most extensive epidemiological analysis of COVID-2019 shows that older people have lower infection rates compared to middle-aged persons. Common sense is that older people prefer to report their illness and get treatment from the hospital compared to middle-aged persons. We propose a hypothesis that when the spreading of COVID-2019 was overlooked, we will find more older cases than the middle-aged cases.

At first, we tested the hypothesis with 4597 COVID-2019 infected samples reported from 26 th Nov 2019 to 17 th Feb 2020 across the mainland of China. We found that 19 th Jan 2020 is a critical time point. Few samples were reported before that day, and most of them were older ones. Then samples were explosively increased after that day, and many of them were middle-aged people. We have demonstrated the hypothesis to this step.

Then, we grouped samples by their residences(provinces). We found that, in the provinces of Hubei and Guangdong, the ages of samples reported before 19 th Jan 2020 are significantly higher than the ages of samples reported after that day. It suggests the COVID-2019 may be spreading in Hubei and Guangdong provinces before 19 th Jan 2020 while people were unconscious of it.

At last, we proposed that the ages distribution of each-day-reported samples could serve as a warning indicator of whether all potential COVID-2019 infected people are found.

We think the power of our analysis is limit because 1. the work is data-driven, and 2. only ∼5% of the COVID-2019 infected people in China are included in the study. However, we believe it still shows some value for its ability to estimate the possible unfound COVID-2019 infected people.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.28.20040204: (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.

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  2. Version published to 10.1101/2020.03.28.20040204v1 on medRxiv