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Effectiveness of Contact Tracing, Mask Wearing and Prompt Testing on Suppressing COVID-19 Resurgences in Megacities: An Individual-Based Modelling Study

48 Pages Posted: 25 Jan 2021

See all articles by Ling Yin

Ling Yin

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology

Hao Zhang

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology

Yuan Li

Shenzhen Center for Disease Control and Prevention

Kang Liu

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology

Tianmu Chen

Xiamen University - State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics

Wei Luo

Geography Department, National University of Singapore

Shengjie Lai

University of Southampton - WorldPop

Ye Li

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology

Xiujuan Tang

Shenzhen Center for Disease Control and Prevention

LI Ning

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology

Shengzhong Feng

National Supercomputing Center in Shenzhen

Yanjie Wei

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology

Zhiyuan Zhao

The Academy of Digital China (Fujian), Fuzhou University

Ying Wen

Shenzhen Center for Disease Control and Prevention

Liang Mao

University of Florida - Department of Geography

Shujiang Mei

Shenzhen Center for Disease Control and Prevention

More...

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Abstract

Background: Secondary waves of COVID-19 loom in many countries as strict physical distancing measures have been lifted. Since megacities have been hardest hit by the disease, science-based guidelines of non-pharmaceutical interventions are still in need for post-epidemic management in ‘business as usual’ cities before vaccines are widely available. This study aims to investigate the combined effects of contact tracing, mask wearing, and prompt testing on minimizing the risk of next COVID-19 waves in megacities.

Methods: We integrated 5·8 million mobile phone users’ trajectory records into a spatially explicit individual-based model for simulating COVID-19 spread among 4·5 million households, 230 thousand workplaces (including schools), and other public places in 0.6 million buildings in Shenzhen city, China, which has been gradually reopened. Government interventions were incorporated to reconstruct the actual course of the 1st wave epidemic. After validated by empirical data, the model was used to assess the probability of resurgences if sporadic cases occurred in a fully reopened city under different scenarios of contact tracing settings (household, work, school, and public place), mask use, and test-seeking behavior along with receding public vigilance.

Findings: Our model well predicted the spatiotemporal dynamics of the 1st wave epidemic in Shenzhen, by age distribution of symptomatic cases, and household secondary attack rate (11·02%). After city reopens, our results show a 50% chance or less of suppressing disease resurgence if not implementing contact tracing. Tracing household contacts, in combination with mandatory (100% compliance) mask use and prompt testing could limit the probability of next outbreak under 5%. If contact tracing can be expanded to work/class group members, the public compliance of masking and testing can be relaxed to 80% and 40%, respectively, to achieve the same suppression target. Further scaled-up contact tracing that includes casual contacts can suppress resurgences with a low compliance to mask use (40%) and prompt testing (20%-40%).

Interpretation: To minimize the risk of resurgence in a reopened city, the local government is expected to spare no efforts to trace close contacts in household, workplace and school for a confirmed case. The authorities should promote mask use in a public space and encourage people with COVID-19-like symptoms to testing within two days after illness onset, along with measures such as sick leave compensation and extensive temperature screening in public places.

Funding Statement: National Scientific Foundation of China, R & D project of key areas in Guangdong Province, Bill & Melinda Gates Foundation, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology

Declaration of Interests: We declare no competing interests.

Keywords: COVID-19; individual-based model; contact tracing; mask wearing; prompt testing

Suggested Citation

Yin, Ling and Zhang, Hao and Li, Yuan and Liu, Kang and Chen, Tianmu and Luo, Wei and Lai, Shengjie and Li, Ye and Tang, Xiujuan and Ning, LI and Feng, Shengzhong and Wei, Yanjie and Zhao, Zhiyuan and Wen, Ying and Mao, Liang and Mei, Shujiang, Effectiveness of Contact Tracing, Mask Wearing and Prompt Testing on Suppressing COVID-19 Resurgences in Megacities: An Individual-Based Modelling Study (12/14/2020). Available at SSRN: https://ssrn.com/abstract=3750214 or http://dx.doi.org/10.2139/ssrn.3750214

Ling Yin (Contact Author)

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology ( email )

1068 Xueyuan Boulevard, Shenzhen, Guangdong
Shenzhen, Guangdong 518055
China

Hao Zhang

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology ( email )

1068 Xueyuan Boulevard, Shenzhen, Guangdong
Shenzhen, Guangdong 518055
China

Yuan Li

Shenzhen Center for Disease Control and Prevention ( email )

Shenzhen, 518055
China

Kang Liu

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology ( email )

1068 Xueyuan Boulevard, Shenzhen, Guangdong
Shenzhen, Guangdong 518055
China

Tianmu Chen

Xiamen University - State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics ( email )

Xiamen
China
+86-13661934715 (Phone)

Wei Luo

Geography Department, National University of Singapore ( email )

Shengjie Lai

University of Southampton - WorldPop ( email )

United Kingdom

Ye Li

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology ( email )

1068 Xueyuan Boulevard, Shenzhen, Guangdong
Shenzhen, Guangdong 518055
China

Xiujuan Tang

Shenzhen Center for Disease Control and Prevention ( email )

LI Ning

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology ( email )

1068 Xueyuan Boulevard, Shenzhen, Guangdong
Shenzhen, Guangdong 518055
China

Shengzhong Feng

National Supercomputing Center in Shenzhen ( email )

Yanjie Wei

Chinese Academy of Sciences (CAS) - Shenzhen Institute of Advanced Technology ( email )

Zhiyuan Zhao

The Academy of Digital China (Fujian), Fuzhou University ( email )

Ying Wen

Shenzhen Center for Disease Control and Prevention ( email )

Liang Mao

University of Florida - Department of Geography ( email )

3141 Turlington Hall
PO Box 117315
Gainesville, FL 32611
United States

Shujiang Mei

Shenzhen Center for Disease Control and Prevention ( email )

Shenzhen, 518055
China

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