Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions
The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with , 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-sc...
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| Published in | Journal of thoracic disease Vol. 12; no. 3; pp. 165 - 174 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
China
AME Publishing Company
01.03.2020
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| Subjects | |
| Online Access | Get full text |
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| Abstract | The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with
, 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.
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.
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.
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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| AbstractList | 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.BACKGROUNDThe 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.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.METHODSWe 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.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.RESULTSWe 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.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.CONCLUSIONSOur 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. The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with , 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. 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. 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. 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. |
| Author | Li, Yimin Mai, Zhitong Xie, Yuqi Liang, Jingyi Zeng, Zhiqi Wang, Yaonan Zhong, Nanshan Luo, Shengmei Zhang, Shaobo Liu, Bin Yang, Zifeng Liu, Peng Wong, Sook-San Liu, Xiaoqing Li, Shiyue Wang, Ke Cao, Xudong He, Jianxing Zanin, Mark Guan, Weijie Ye, Feng Liang, Wenhua Wang, Zhoulang Yang, Yifan Li, Fei Gao, Zhongqiang |
| Author_xml | – sequence: 1 givenname: Zifeng surname: Yang fullname: Yang, Zifeng – sequence: 2 givenname: Zhiqi surname: Zeng fullname: Zeng, Zhiqi – sequence: 3 givenname: Ke surname: Wang fullname: Wang, Ke – sequence: 4 givenname: Sook-San surname: Wong fullname: Wong, Sook-San – sequence: 5 givenname: Wenhua surname: Liang fullname: Liang, Wenhua – sequence: 6 givenname: Mark surname: Zanin fullname: Zanin, Mark – sequence: 7 givenname: Peng surname: Liu fullname: Liu, Peng – sequence: 8 givenname: Xudong surname: Cao fullname: Cao, Xudong – sequence: 9 givenname: Zhongqiang surname: Gao fullname: Gao, Zhongqiang – sequence: 10 givenname: Zhitong surname: Mai fullname: Mai, Zhitong – sequence: 11 givenname: Jingyi surname: Liang fullname: Liang, Jingyi – sequence: 12 givenname: Xiaoqing surname: Liu fullname: Liu, Xiaoqing – sequence: 13 givenname: Shiyue surname: Li fullname: Li, Shiyue – sequence: 14 givenname: Yimin surname: Li fullname: Li, Yimin – sequence: 15 givenname: Feng surname: Ye fullname: Ye, Feng – sequence: 16 givenname: Weijie surname: Guan fullname: Guan, Weijie – sequence: 17 givenname: Yifan surname: Yang fullname: Yang, Yifan – sequence: 18 givenname: Fei surname: Li fullname: Li, Fei – sequence: 19 givenname: Shengmei surname: Luo fullname: Luo, Shengmei – sequence: 20 givenname: Yuqi surname: Xie fullname: Xie, Yuqi – sequence: 21 givenname: Bin surname: Liu fullname: Liu, Bin – sequence: 22 givenname: Zhoulang surname: Wang fullname: Wang, Zhoulang – sequence: 23 givenname: Shaobo surname: Zhang fullname: Zhang, Shaobo – sequence: 24 givenname: Yaonan surname: Wang fullname: Wang, Yaonan – sequence: 25 givenname: Nanshan surname: Zhong fullname: Zhong, Nanshan – sequence: 26 givenname: Jianxing surname: He fullname: He, Jianxing |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32274081$$D View this record in MEDLINE/PubMed |
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| Copyright | 2020 Journal of Thoracic Disease. All rights reserved. 2020 Journal of Thoracic Disease. All rights reserved. 2020 Journal of Thoracic Disease. |
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| Keywords | modeling Susceptible-Exposed-Infectious-Removed (SEIR) epidemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Coronavirus disease 2019 (COVID-19) |
| Language | English |
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| References | 32724894 - J Public Health Emerg. 2020 Jun;4 32394245 - Sci China Life Sci. 2020 Aug;63(8):1257-1260 33209357 - J Thorac Dis. 2020 Oct;12(10):5254-5258 32642080 - J Thorac Dis. 2020 May;12(5):1762-1765 |
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, the period of mass migration for the annual... The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the... |
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| Title | Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions |
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