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Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policie...

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Autores principales: Yang, Zifeng, Zeng, Zhiqi, Wang, Ke, Wong, Sook-San, Liang, Wenhua, Zanin, Mark, Liu, Peng, Cao, Xudong, Gao, Zhongqiang, Mai, Zhitong, Liang, Jingyi, Liu, Xiaoqing, Li, Shiyue, Li, Yimin, Ye, Feng, Guan, Weijie, Yang, Yifan, Li, Fei, Luo, Shengmei, Xie, Yuqi, Liu, Bin, Wang, Zhoulang, Zhang, Shaobo, Wang, Yaonan, Zhong, Nanshan, He, Jianxing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7139011/
https://www.ncbi.nlm.nih.gov/pubmed/32274081
http://dx.doi.org/10.21037/jtd.2020.02.64
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author Yang, Zifeng
Zeng, Zhiqi
Wang, Ke
Wong, Sook-San
Liang, Wenhua
Zanin, Mark
Liu, Peng
Cao, Xudong
Gao, Zhongqiang
Mai, Zhitong
Liang, Jingyi
Liu, Xiaoqing
Li, Shiyue
Li, Yimin
Ye, Feng
Guan, Weijie
Yang, Yifan
Li, Fei
Luo, Shengmei
Xie, Yuqi
Liu, Bin
Wang, Zhoulang
Zhang, Shaobo
Wang, Yaonan
Zhong, Nanshan
He, Jianxing
author_facet Yang, Zifeng
Zeng, Zhiqi
Wang, Ke
Wong, Sook-San
Liang, Wenhua
Zanin, Mark
Liu, Peng
Cao, Xudong
Gao, Zhongqiang
Mai, Zhitong
Liang, Jingyi
Liu, Xiaoqing
Li, Shiyue
Li, Yimin
Ye, Feng
Guan, Weijie
Yang, Yifan
Li, Fei
Luo, Shengmei
Xie, Yuqi
Liu, Bin
Wang, Zhoulang
Zhang, Shaobo
Wang, Yaonan
Zhong, Nanshan
He, Jianxing
author_sort Yang, Zifeng
collection PubMed
description BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. METHODS: We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. RESULTS: We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. CONCLUSIONS: Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.
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spelling pubmed-71390112020-04-09 Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions Yang, Zifeng Zeng, Zhiqi Wang, Ke Wong, Sook-San Liang, Wenhua Zanin, Mark Liu, Peng Cao, Xudong Gao, Zhongqiang Mai, Zhitong Liang, Jingyi Liu, Xiaoqing Li, Shiyue Li, Yimin Ye, Feng Guan, Weijie Yang, Yifan Li, Fei Luo, Shengmei Xie, Yuqi Liu, Bin Wang, Zhoulang Zhang, Shaobo Wang, Yaonan Zhong, Nanshan He, Jianxing J Thorac Dis Original Article BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. METHODS: We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. RESULTS: We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. CONCLUSIONS: Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size. AME Publishing Company 2020-03 /pmc/articles/PMC7139011/ /pubmed/32274081 http://dx.doi.org/10.21037/jtd.2020.02.64 Text en 2020 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Yang, Zifeng
Zeng, Zhiqi
Wang, Ke
Wong, Sook-San
Liang, Wenhua
Zanin, Mark
Liu, Peng
Cao, Xudong
Gao, Zhongqiang
Mai, Zhitong
Liang, Jingyi
Liu, Xiaoqing
Li, Shiyue
Li, Yimin
Ye, Feng
Guan, Weijie
Yang, Yifan
Li, Fei
Luo, Shengmei
Xie, Yuqi
Liu, Bin
Wang, Zhoulang
Zhang, Shaobo
Wang, Yaonan
Zhong, Nanshan
He, Jianxing
Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions
title Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions
title_full Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions
title_fullStr Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions
title_full_unstemmed Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions
title_short Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions
title_sort modified seir and ai prediction of the epidemics trend of covid-19 in china under public health interventions
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7139011/
https://www.ncbi.nlm.nih.gov/pubmed/32274081
http://dx.doi.org/10.21037/jtd.2020.02.64
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