Population mobility change for controlling the transmission of COVID-19: mobile phone data analysis in nine cities of China

ABSTRACT

Mobility restriction measures were the main tools to control the spread of COVID-19, but the extent to which the mobility has decreased remained unsure. We investigated the change in local population mobility and its correlation with COVID-19 infections, using 1185 billion aggregated mobile phone data records in nine main cities in China from 10 January to 24 February 2020. The mobility fell by as much as 79.57% compared to the normal days in 2020 and by 58.13% compared to the same lunar period in 2019. The daily incidence of COVID-19 was significantly correlated with local daily mobility (R² = 0.77, P < 0.001). The instantaneous reproduction number R(t) declined by 3% when mobility was reduced by 10% in the GLM analysis (P < 0.05). Our study indicated that the decreased mobility level, driven by a mixture effect of holiday and public health interventions, could substantially reduce the transmission of COVID-19 to a low level. Our study could provide evidence of mobility restriction to control local transmission for other places facing COVID-19 outbreaks or potential next waves.

KEYWORDS:

• COVID-19
• mobile phone
• population mobility
• local transmission

Acknowledgements

We thank Zhucheng Zhang, Shuting Yang, and Jia Xu for their assistance with the data collection and technical assistance.

Disclosure statement

The authors declare no conflict of interest.

Author contributions

Jizhe Xia extracted the data, analyzed and interpreted data, and obtained funding for the study. Ying Zhou ran the study, analyzed and interpreted data, wrote the paper, and obtained funding for the study. Zhaoyang Yu participated in the mobile phone data processing and analysis. Erzhen Chen participated in discussions. Yang Yue participated in discussions on the undertaking of the study. Zhen Li analyzed and interpreted the data. All authors reviewed the paper for content and approved the final report.

Data and codes availability statement

The codes that support the findings of this study are available at github.com with the identifier [https://github.com/UISZU/MPDAChina]. We purchased the mobile phone signaling data from 2019 to 2020 from the service provider (China Unicom). Our data purchase agreement with China Unicom prohibits us from sharing these data with third parties, but interested parties can contact China Unicom to make the same data purchase.

Additional information

Funding

This work has received financial support from National Natural Science Foundation of China [grant numbers 42171400, 71961137003, 82103945], Research on Prevention and Control of COVID-19 in Guangdong Education Department [grant number 2020KZDZX1171], Natural Science Foundation of Guangdong [grant number 2021A1515011324], Natural Resources of Guangdong [grant number 202325] and Shenzhen Science and Technology Innovation Commission [grant number JCYJ20190808174209308].

Notes on contributors

Jizhe Xia

Jiizhe Xia is currently an associate professor in the Department of Urban Spatial Information Engineering, School of Architecture and Urban Planning, Shenzhen University. He received the PhD degree from George Mason University. His research interests are spatially intelligent computing and spatiotemporal big data.

Taicheng Li

Taicheng Li is currently a master student at Shenzhen University. His research interests focus on geographic big data mining and infectious disease modeling.

Zhaoyang Yu

Zhaoyang Yu received his bachelor degree from Shenzhen University. His research interests focus on geographic big data mining.

Erzhen Chen

Erzhen Chen is currently a professor and doctoral supervisor of Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. His research interests focus on Clinical and basic research of sepsis, severe acute pancreatitis and multiple organ dysfunction syndrome.

Yang Yue

Yang Yue is currently a professor in the Department of Urban Spatial Information Engineering, School of Architecture and Urban Planning, Shenzhen University. She received the PhD degree from the University of Hong Kong. Her research interests is trajectory data analysis and mining.

Zhen Li

Zhen Li received her master degree from Shenzhen University. Her research interests focus on health system accessibility and taxi trajectories data.

Ying Zhou

Ying Zhou received the PhD degree from the University of Hong Kong. Her research interests focus on the epidemiology of respiratory infectious diseases and the epidemiology of obesity and related chronic diseases.