The emergence of coronavirus disease 2019 (COVID-19) has infected more than 62 million people worldwide. Control responses varied across countries with different outcomes in terms of epidemic size and social disruption. This study presents an age-specific susceptible-exposed-infected-recovery-death model that considers the unique characteristics of COVID-19 to examine the effectiveness of various non-pharmaceutical interventions (NPIs) in New York City (NYC). Numerical experiments from our model show that the control policies implemented in NYC reduced the number of infections by 72% [interquartile range (IQR) 53–95] and the number of deceased cases by 76% (IQR 58–96) by the end of 2020. Among all the NPIs, social distancing for the entire population and protection for the elderly in public facilities is the most effective control measure in reducing severe infections and deceased cases. School closure policy may not work as effectively as one might expect in terms of reducing the number of deceased cases. Our simulation results provide novel insights into the city-specific implementation of NPIs with minimal social disruption considering the locations and population characteristics.
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The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City
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February 2021
Research Article|
February 09 2021
The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City
Jiannan Yang
;
Jiannan Yang
1
School of Data Science, City University of Hong Kong
, Hong Kong 999077, China
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Qingpeng Zhang
;
Qingpeng Zhang
a)
1
School of Data Science, City University of Hong Kong
, Hong Kong 999077, China
a)Authors to whom correspondence should be addressed: qingpeng.zhang@cityu.edu.hk and dajun.zeng@ia.ac.cn
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Zhidong Cao;
Zhidong Cao
2
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
, Beijing 100000, China
3
School of Artificial Intelligence, University of Chinese Academy of Sciences
, Beijing 100000, China
4
Shenzhen Artificial Intelligence and Data Science Institute (Longhua)
, Shenzhen 518131, China
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Jianxi Gao;
Jianxi Gao
5
Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute
, Troy, New York 12180, USA
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Dirk Pfeiffer
;
Dirk Pfeiffer
6
Department of Infectious Diseases and Public Health, City University of Hong Kong
, Hong Kong 999077, China
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Lu Zhong;
Lu Zhong
5
Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute
, Troy, New York 12180, USA
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Daniel Dajun Zeng
Daniel Dajun Zeng
a)
2
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
, Beijing 100000, China
3
School of Artificial Intelligence, University of Chinese Academy of Sciences
, Beijing 100000, China
4
Shenzhen Artificial Intelligence and Data Science Institute (Longhua)
, Shenzhen 518131, China
a)Authors to whom correspondence should be addressed: qingpeng.zhang@cityu.edu.hk and dajun.zeng@ia.ac.cn
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a)Authors to whom correspondence should be addressed: qingpeng.zhang@cityu.edu.hk and dajun.zeng@ia.ac.cn
Chaos 31, 021101 (2021)
Article history
Received:
December 15 2020
Accepted:
December 30 2020
Citation
Jiannan Yang, Qingpeng Zhang, Zhidong Cao, Jianxi Gao, Dirk Pfeiffer, Lu Zhong, Daniel Dajun Zeng; The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City. Chaos 1 February 2021; 31 (2): 021101. https://doi.org/10.1063/5.0040560
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