The effectiveness of travel restriction measures in alleviating the COVID-19 epidemic: evidence from Shenzhen, China

Abstract

With the expansion of the global novel coronavirus disease (COVID-19) pandemic, unprecedented interventions have been widely implemented in many countries, including China. In view of this scenario, this research aims to explore the effectiveness of population mobility restriction in alleviating epidemic transmission during different stages of the outbreak. Taking Shenzhen, a city with a large immigrant population in China, as a case study, the real-time reproduction number of COVID-19 is estimated by statistical methods to represent the dynamic spatiotemporal transmission pattern of COVID-19. Furthermore, migration data between Shenzhen and other provinces are collected to investigate the impact of nationwide population flow on near-real-time dynamic reproductive numbers. The results show that traffic flow control between populated cities has an inhibitory effect on urban transmission, but this effect is not significant in the late stage of the epidemic spread in China. This finding implies that the government should limit international and domestic population movement starting from the very early stage of the outbreak. This work confirms the effectiveness of travel restriction measures in the face of COVID-19 in China and provides new insight for densely populated cities in imposing intervention measures at various stages of the transmission cycle.

Keywords: COVID-19; Correlation analysis; Population mobility; Real-time reproduction number; Shenzhen.

Fig. 1

Proportion map of the migration index of five regions and total areas into Shenzhen

Fig. 2

Estimation of $R_t$ and 95% confidence intervals by five methods: a exponential growth method (EG); b maximum likelihood estimation (MLE); c sequential Bayesian method (SB); d time-dependent reproduction numbers (TD); e EpiEstim R package (Epi)

Fig. 3

Existing infection cases for the actual data and estimated results based on $R_t$. a The estimation period of EG starts on 19th January; b the estimation period of MLE starts on 19th January; c the estimation period of SB starts on 14th January; d the estimation period of TD starts on 13th January; e the estimation period of Epi starts on 21st January. Note that this figure presents data from 19th January to 3rd May

Fig. 4

Width of confidence intervals resulting from the five methods

Fig. 5

Correlation between $R_t$ and the migration index for total areas and Guangdong

Fig. 6

Correlation between R_t and the migration index for Hunan, Guangxi and Jiangxi

Fig. 7

Correlation between R_t and the migration index for Beijing

Fig. 8

Correlation between R_t and the migration index for Hubei

Fig. 9

Hypothesis test on correlation differences between neighbouring provinces: a Guangdong and Hunan; b Guangdong and Guangxi; c Guangdong and Jiangxi

Fig. 10

Hypothesis test on correlation differences in Beijing or Hubei and the neighbouring provinces: a Hubei and Hunan; b Hubei and Guangxi; c Hubei and Jiangxi; d Beijing and Hunan; e Beijing and Guangxi; f Beijing and Jiangxi