Quantifying the impacts of human mobility restriction on the spread of coronavirus disease 2019: an empirical analysis from 344 cities of China.
10.1097/CM9.0000000000001763
- Author:
Jing TAN
1
;
Shao-Yang ZHAO
2
;
Yi-Quan XIONG
1
;
Chun-Rong LIU
1
;
Shi-Yao HUANG
1
;
Xin LU
3
;
Lehana THABANE
4
;
Feng XIE
5
;
Xin SUN
1
;
Wei-Min LI
6
Author Information
1. Chinese Evidence-Based Medicine Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
2. School of Economics, Sichuan University, Chengdu, Sichuan 610041, China.
3. College of Systems Engineering, National University of Defense Technology, Changsha, Hunan 410073, China.
4. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario L8S 4L8, Canada.
5. Center for Health Economics and Policy Analysis, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario L8S 4L8, Canada.
6. Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
- Publication Type:Journal Article
- MeSH:
COVID-19;
China/epidemiology*;
Cities;
Humans;
SARS-CoV-2
- From:
Chinese Medical Journal
2021;134(20):2438-2446
- CountryChina
- Language:English
-
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.
