Quantifying the impacts of human mobility restriction on the spread of coronavirus disease 2019: an empirical analysis from 344 cities of China

  1. Jing Tan
  2. Shao-Yang Zhao
  3. Yi-Quan Xiong
  4. Chun-Rong Liu
  5. Shi-Yao Huang
  6. Xin Lu
  7. Lehana Thabane
  8. Feng Xie
  9. Xin Sun
  10. Wei-Min Li

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Abstract

Background:

Since the outbreak of coronavirus disease 2019 (COVID-19), human mobility restriction measures have raised controversies, partly because of the inconsistent findings. An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction. The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19.

Methods:

Our study applied the difference-in-difference (DID) model to assess the declines of population mobility at the city level, and used the log–log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders.

Results:

The DID model showed that a continual expansion of the relative declines over time in 2020. After 4 weeks, population mobility declined by −54.81% (interquartile range, −65.50% to −43.56%). The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks (ie, 1% decline of population mobility was associated with 0.72% [95% CI: 0.50%–0.93%] reduction of cumulative cases for 1 week, 1.42% 2 weeks, 1.69% 3 weeks, 1.72% 4 weeks, 1.64% 5 weeks, and 1.52% 6 weeks). The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter. The effects on cumulative cases differed by cities of different population sizes, with greater effects seen in larger cities.

Conclusions:

Persistent population mobility restrictions are well deserved. Implementation of mobility restrictions in major cities with large population sizes may be even more important.

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  1. Version published to 10.1097/cm9.0000000000001763
  2. ScreenIT

    SciScore for 10.1101/2020.07.13.20148668: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Ethics Review Board of West China Hospital, Sichuan University (2020-99).
    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:
    These studies provided important insights about the impacts of mobility restriction measures, but had limitation given the use of modelling, whereby assumptions are often employed. Up to now, empirical studies still fall short.39 One early study assessed the impacts of human mobility restriction on COVID-19 cases at the first week, and the other suggested that travel restriction was more useful in the early outbreak, but attenuated if the outbreak was expanded. However, the extent to which the mobility restriction policy led to mobility decline and whether human mobility restriction causally controlled the spread of COVID-19 were not yet established. An earlier study, using mobility data from four metropolitan areas in USA, mainly examined the temporal correlation between timing of public policy measures and cumulative cases of COVID-19,17 but indicated a lack of causality due to the nature of descriptive analyses.17 Strengths and limitations: Our study has several strengths. Firstly, we have used rigorous methods to assess the causal effects of human mobility restriction on the spread of COVID-19. We have also profiled the declines of population mobility by using the DID model, which avoided the reverse causality and confounding by usual fluctuation of population mobility over time. Secondly, we included important confounders in the models with precise measurements, such as number of times of population movements from Wuhan to imported regions, number of residents (ten thous...

    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.
    • Thank you for including a protocol registration statement.

    About SciScore

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  3. Version published to 10.1101/2020.07.13.20148668v1 on medRxiv