Supplementary material from "A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities"
Version 2 2021-08-25, 06:43
Version 1 2021-08-12, 05:56
Posted on 2021-08-25 - 06:43
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 population wear facemasks and 40% comply with prompt testing, respectively. 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.
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Yin, Ling; Zhang, Hao; Li, Yuan; Liu, Kang; Chen, Tianmu; Luo, Wei; et al. (2021). Supplementary material from "A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities". The Royal Society. Collection. https://doi.org/10.6084/m9.figshare.c.5557215.v2
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AUTHORS (16)
LY
Ling Yin
HZ
Hao Zhang
YL
Yuan Li
KL
Kang Liu
TC
Tianmu Chen
WL
Wei Luo
SL
Shengjie Lai
YL
Ye Li
XT
Xiujuan Tang
LN
Li Ning
SF
Shengzhong Feng
YW
Yanjie Wei
ZZ
Zhiyuan Zhao
YW
Ying Wen
LM
Liang Mao
SM
Shujiang Mei