ArticleIEEE access : practical innovations, open solutions2020
Quantifying the Time-Lag Effects of Human Mobility on the COVID-19 Transmission: A Multi-City Study in China.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 40 citations in OpenAlex.
- IntelliScheduler: an edge-cloud computing environment hybrid deep learning framework for task scheduling based on learning.Scientific reports · 2026Article
- Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: A Bayesian spatiotemporal modelling study.PLOS global public health · 2025Article
- Case fatality rate considering the lag time from the onset of COVID-19 infection to related death from 2020 to 2022 in Japan.IJID regions · 2023Article
- The Effectiveness of Mobility Restrictions on Controlling the Spread of COVID-19 in a Resistant Population.International journal of environmental research and public health · 2023Article
- Quantifying human mobility resilience to the COVID-19 pandemic: A case study of Beijing, China.Sustainable cities and society · 2023Article
- Novel indicator for the spread of new coronavirus disease 2019 and its association with human mobility in Japan.Scientific reports · 2023Article
- The impact of urban spatial environment on COVID-19: a case study in Beijing.Frontiers in public health · 2023Article
- Inter-prefectural Travel and Network Connectedness During the COVID-19 Pandemic in Japan.Journal of epidemiology · 2022Article
- Urban spatial epidemic simulation model: A case study of the second COVID-19 outbreak in Beijing, China.Transactions in GIS : TG · 2022Article
- Time series cross-correlation between home range and number of infected people during the COVID-19 pandemic in a suburban city.PloS one · 2022Article
- Pandemic wave trends in COVID-19 cases, mobility reduction, and climate parameters in major metropolitan areas in the United States.Environment systems & decisions · 2022Article
- Understanding the Geography of COVID-19 Case Fatality Rates in China: A Spatial Autoregressive Probit-Log Linear Hurdle Analysis.Frontiers in public health · 2022Article
- A wind speed threshold for increased outdoor transmission of coronavirus: an ecological study.BMC infectious diseases · 2021Article
- How did human dwelling and working intensity change over different stages of COVID-19 in Beijing?Sustainable cities and society · 2021Article
- Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China.Environmental science and pollution research international · 2021Article
- Effects of transport-related COVID-19 policy measures: A case study of six developed countries.Transport policy · 2021Article
- Human mobility behavior in COVID-19: A systematic literature review and bibliometric analysis.Sustainable cities and society · 2021Article
- Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost.PloS one · 2021Article
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8 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
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.
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