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.
Highlights
The novel coronavirus disease 2019 (COVID-19), first identified in Wuhan, Hubei province, in December 2019, has spread rapidly across China and even globally [1], [2]
In this study, we aimed to investigate the effect of reduced human mobility on COVID-19 transmission in severely affected Chinese cities during the first wave of COVID-19 epidemic in China, based on the assumption that the decline in new case solely depends on the mobility
DISTRIBUTION OF LAG AND CORRELATION COEFFICIENT There was a significant positive lagged correlation (p < 0.05) between the mobility time series and COVID-19 new case in all cities except Jining, Qianjiang and Dongguan; we excluded them from the analysis of lag and correlation coefficient
Summary
The novel coronavirus disease 2019 (COVID-19), first identified in Wuhan, Hubei province, in December 2019, has spread rapidly across China and even globally [1], [2]. On March 11, The World Health Organization (WHO) declared the COVID-19 outbreak a pandemic, which has posed a threat to global public health. Effective vaccine and specific therapeutic drug, many countries have implemented non-pharmaceutical interventions (NPIs), of which human mobility restrictions is an essential component [4]–[6]. Several studies have investigated the effect of human mobility and control measures on the COVID-19 pandemic around the world [7]–[9]. Human mobility is one of the key factor in the spread of infectious diseases [10], [11].
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