Population Movement, City Closure in Wuhan, and Geographical Expansion of the COVID-19 Infection in China in January 2020

  1. Kun Liu
  2. Siqi Ai
  3. Shuxuan Song
  4. Guanghu Zhu
  5. Fei Tian
  6. Huan Li
  7. Yuan Gao
  8. Yinglin Wu
  9. Shiyu Zhang
  10. Zhongjun Shao
  11. Qiyong Liu
  12. Hualiang Lin

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Abstract

Background

The unprecedented outbreak of corona virus disease 2019 (COVID-19) infection in Wuhan City has caused global concern; the outflow of the population from Wuhan was believed to be a main reason for the rapid and large-scale spread of the disease, so the government implemented a city-closure measure to prevent its transmission considering the large amount of travel before the Chinese New Year.

Methods

Based on the daily reported new cases and the population-movement data between 1 and 31 January, we examined the effects of population outflow from Wuhan on the geographical expansion of the infection in other provinces and cities of China, as well as the impacts of the city closure in Wuhan using different closing-date scenarios.

Results

We observed a significantly positive association between population movement and the number of the COVID-19 cases. The spatial distribution of cases per unit of outflow population indicated that the infection in some areas with a large outflow of population might have been underestimated, such as Henan and Hunan provinces. Further analysis revealed that if the city-closure policy had been implemented 2 days earlier, 1420 (95% confidence interval, 1059–1833) cases could have been prevented, and if 2 days later, 1462 (1090–1886) more cases would have been possible.

Conclusions

Our findings suggest that population movement might be one important trigger for the transmission of COVID-19 infection in China, and the policy of city closure is effective in controlling the epidemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.02.04.20020339: (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
    Data collection: The data on the daily number of cases were derived from the real-time update of the China Health Commission (http://www.nhc.gov.cn/), 2019-nCoV epidemic report on Phoenix and Dingxiangyuan website.
    Phoenix
    suggested: (Phoenix, RRID:SCR_003163)

    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 a few limitations of our study. Firstly, for practical reasons, we used an indicator to reflect the real-time magnitude of population movements, which was acceptable considering our research purpose. Secondly, the influence of some important factors, such as socioeconomic and demographic characteristics, were not considered. Thirdly, it is assumed that the infected travelers in the population are randomly distributed (28), and that there was no significant difference in surveillance capability between cities (18), which might not be the case in reality. In addition, daily data used in this study was reported infection data, rather than the actual number of incident cases. More detailed and further in-depth studies are warranted in the future. In conclusion, our study indicates that the population outflow from Wuhan might be one important trigger of the 2019-nCoV infection transmission in China, and the policy of city closure is effective to prevent the epidemic and earlier implementation would be more effective. The magnitude of epidemic might be under-estimated and should be paid more attention, such as Henan and Hunan provinces.

    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. Version published to 10.1093/cid/ciaa422
  3. Version published to 10.1101/2020.02.04.20020339v1 on medRxiv