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author:

Yin, Ling (Yin, Ling.) [1] | Zhang, Hao (Zhang, Hao.) [2] | Li, Yuan (Li, Yuan.) [3] | Liu, Kang (Liu, Kang.) [4] | Chen, Tianmu (Chen, Tianmu.) [5] | Luo, Wei (Luo, Wei.) [6] | Lai, Shengjie (Lai, Shengjie.) [7] | Li, Ye (Li, Ye.) [8] | Tang, Xiujuan (Tang, Xiujuan.) [9] | Ning, Li (Ning, Li.) [10] | Feng, Shengzhong (Feng, Shengzhong.) [11] | Wei, Yanjie (Wei, Yanjie.) [12] | Zhao, Zhiyuan (Zhao, Zhiyuan.) [13] | Wen, Ying (Wen, Ying.) [14] | Mao, Liang (Mao, Liang.) [15] | Mei, Shujiang (Mei, Shujiang.) [16]

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EI

Abstract:

Before herd immunity against Coronavirus disease 2019 (COVID-19) is achieved by mass vaccination, science-based guidelines for non-pharmaceutical interventions are urgently needed to reopen megacities. This study integrated massive mobile phone tracking records, census data and building characteristics into a spatially explicit agent-based model to simulate COVID-19 spread among 11.2 million individuals living in Shenzhen City, China. After validation by local epidemiological observations, the model was used to assess the probability of COVID-19 resurgence if sporadic cases occurred in a fully reopened city. Combined scenarios of three critical non-pharmaceutical interventions (contact tracing, mask wearing and prompt testing) were assessed at various levels of public compliance. Our results show a greater than 50% chance of disease resurgence if the city reopened without contact tracing. However, tracing household contacts, in combination with mandatory mask use and prompt testing, could suppress the probability of resurgence under 5% within four weeks. If household contact tracing could be expanded to work/class group members, the COVID resurgence could be avoided if 80% of the population wear facemasks and 40% comply with prompt testing. Our assessment, including modelling for different scenarios, helps public health practitioners tailor interventions within Shenzhen City and other world megacities under a variety of suppression timelines, risk tolerance, healthcare capacity and public compliance. © 2021 The Authors.

Keyword:

Autonomous agents Computational methods Health risks mHealth Population statistics Risk assessment Risk perception Simulation platform Wear of materials

Community:

  • [ 1 ] [Yin, Ling]Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Guangdong, Shenzhen; 518055, China
  • [ 2 ] [Zhang, Hao]Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Guangdong, Shenzhen; 518055, China
  • [ 3 ] [Zhang, Hao]University of Chinese Academy of Sciences, Beijing; 100049, China
  • [ 4 ] [Li, Yuan]Shenzhen Center for Disease Control and Prevention, Guangdong, Shenzhen; 518055, China
  • [ 5 ] [Liu, Kang]Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Guangdong, Shenzhen; 518055, China
  • [ 6 ] [Liu, Kang]University of Chinese Academy of Sciences, Beijing; 100049, China
  • [ 7 ] [Chen, Tianmu]State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Fujian, Xiamen; 361102, China
  • [ 8 ] [Luo, Wei]Geography Department, National University of Singapore, AS2-03-01, 1 Arts Link, Singapore; 117570, Singapore
  • [ 9 ] [Lai, Shengjie]WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton; SO17 1BJ, United Kingdom
  • [ 10 ] [Li, Ye]Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Guangdong, Shenzhen; 518055, China
  • [ 11 ] [Tang, Xiujuan]Shenzhen Center for Disease Control and Prevention, Guangdong, Shenzhen; 518055, China
  • [ 12 ] [Ning, Li]Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Guangdong, Shenzhen; 518055, China
  • [ 13 ] [Feng, Shengzhong]National Supercomputing Center in Shenzhen, Guangdong, Shenzhen; 518055, China
  • [ 14 ] [Wei, Yanjie]Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Guangdong, Shenzhen; 518055, China
  • [ 15 ] [Zhao, Zhiyuan]Academy of Digital China (Fujian), Fuzhou University, Fujian, Fuzhou; 350108, China
  • [ 16 ] [Wen, Ying]Shenzhen Center for Disease Control and Prevention, Guangdong, Shenzhen; 518055, China
  • [ 17 ] [Mao, Liang]Department of Geography, University of Florida, Gainesville; FL; 32611, United States
  • [ 18 ] [Mei, Shujiang]Shenzhen Center for Disease Control and Prevention, Guangdong, Shenzhen; 518055, China

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Source :

Journal of the Royal Society Interface

ISSN: 1742-5689

Year: 2021

Issue: 181

Volume: 18

4 . 2 9 3

JCR@2021

3 . 7 0 0

JCR@2023

ESI HC Threshold:186

JCR Journal Grade:2

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 28

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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