Your browser doesn't support javascript.
Quantifying the Time-Lag Effects of Human Mobility on the COVID-19 Transmission: A Multi-City Study in China.
Xi, Wang; Pei, Tao; Liu, Qiyong; Song, Ci; Liu, Yaxi; Chen, Xiao; Ma, Jia; Zhang, Zhixin.
  • Xi W; State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of Sciences Beijing 100101 China.
  • Pei T; College of Resources and EnvironmentUniversity of Chinese Academy of Sciences Beijing 100049 China.
  • Liu Q; State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of Sciences Beijing 100101 China.
  • Song C; College of Resources and EnvironmentUniversity of Chinese Academy of Sciences Beijing 100049 China.
  • Liu Y; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023 China.
  • Chen X; State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, National Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and Prevention Beijing 102206 China.
  • Ma J; State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of Sciences Beijing 100101 China.
  • Zhang Z; College of Resources and EnvironmentUniversity of Chinese Academy of Sciences Beijing 100049 China.
IEEE Access ; 8: 216752-216761, 2020.
Article in English | MEDLINE | ID: covidwho-1003891
ABSTRACT
The first wave of the 2019 novel coronavirus (COVID-19) epidemic in China showed there was a lag between the reduction in human mobility and the decline in COVID-19 transmission and this lag was different in cities. A prolonged lag would cause public panic and reflect the inefficiency of control measures. This study aims to quantify this time-lag effect and reveal its influencing socio-demographic and environmental factors, which is helpful to policymaking in controlling COVID-19 and other potential infectious diseases in the future. We combined city-level mobility index and new case time series for 80 most affected cities in China from Jan 17 to Feb 29, 2020. Cross correlation analysis and spatial autoregressive model were used to estimate the lag length and determine influencing factors behind it, respectively. The results show that mobility is strongly correlated with COVID-19 transmission in most cities with lags of 10 days (interquartile range 8 - 11 days) and correlation coefficients of 0.68 ± 0.12. This time-lag is consistent with the incubation period plus time for reporting. Cities with a shorter lag appear to have a shorter epidemic duration. This lag is shorter in cities with larger volume of population flow from Wuhan, higher designated hospitals density and urban road density while economically advantaged cities tend to have longer time lags. These findings suggest that cities with compact urban structure should strictly adhere to human mobility restrictions, while economically prosperous cities should also strengthen other non-pharmaceutical interventions to control the spread of the virus.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Randomized controlled trials Language: English Journal: IEEE Access Year: 2020 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Randomized controlled trials Language: English Journal: IEEE Access Year: 2020 Document Type: Article