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

Identifieur interne : 000C63 ( Pmc/Corpus ); précédent : 000C62; suivant : 000C64

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

Auteurs : Zifeng Yang ; Zhiqi Zeng ; Ke Wang ; Sook-San Wong ; Wenhua Liang ; Mark Zanin ; Peng Liu ; Xudong Cao ; Zhongqiang Gao ; Zhitong Mai ; Jingyi Liang ; Xiaoqing Liu ; Shiyue Li ; Yimin Li ; Feng Ye ; Weijie Guan ; Yifan Yang ; Fei Li ; Shengmei Luo ; Yuqi Xie ; Bin Liu ; Zhoulang Wang ; Shaobo Zhang ; Yaonan Wang ; Nanshan Zhong ; Jianxing He

Source :

RBID : PMC:7139011

Abstract

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.


Url:
DOI: 10.21037/jtd.2020.02.64
PubMed: 32274081
PubMed Central: 7139011

Links to Exploration step

PMC:7139011

Le document en format XML

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<title xml:lang="en" level="a" type="main">Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions</title>
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<name sortKey="Yang, Zifeng" sort="Yang, Zifeng" uniqKey="Yang Z" first="Zifeng" last="Yang">Zifeng Yang</name>
<affiliation>
<nlm:aff id="aff1">National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University,
<institution>State Key Laboratory of Respiratory Disease (Guangzhou Medical University)</institution>
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<addr-line>Guangzhou 510230</addr-line>
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<country>China</country>
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<affiliation>
<nlm:aff id="aff2">Macau Institute for Applied Research in Medicine and Health, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau,
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;</nlm:aff>
</affiliation>
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<name sortKey="Zeng, Zhiqi" sort="Zeng, Zhiqi" uniqKey="Zeng Z" first="Zhiqi" last="Zeng">Zhiqi Zeng</name>
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<nlm:aff id="aff1">National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University,
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<name sortKey="Wang, Ke" sort="Wang, Ke" uniqKey="Wang K" first="Ke" last="Wang">Ke Wang</name>
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<nlm:aff id="aff4">School of Public Health, The University of Hong Kong, Hong Kong,
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<name sortKey="Liu, Peng" sort="Liu, Peng" uniqKey="Liu P" first="Peng" last="Liu">Peng Liu</name>
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<name sortKey="Cao, Xudong" sort="Cao, Xudong" uniqKey="Cao X" first="Xudong" last="Cao">Xudong Cao</name>
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<name sortKey="Mai, Zhitong" sort="Mai, Zhitong" uniqKey="Mai Z" first="Zhitong" last="Mai">Zhitong Mai</name>
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<addr-line>Guangzhou 510230</addr-line>
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<name sortKey="Liang, Jingyi" sort="Liang, Jingyi" uniqKey="Liang J" first="Jingyi" last="Liang">Jingyi Liang</name>
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<addr-line>Guangzhou 510230</addr-line>
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<name sortKey="Liu, Xiaoqing" sort="Liu, Xiaoqing" uniqKey="Liu X" first="Xiaoqing" last="Liu">Xiaoqing Liu</name>
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<nlm:aff id="aff1">National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University,
<institution>State Key Laboratory of Respiratory Disease (Guangzhou Medical University)</institution>
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<addr-line>Guangzhou 510230</addr-line>
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<country>China</country>
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<name sortKey="Li, Shiyue" sort="Li, Shiyue" uniqKey="Li S" first="Shiyue" last="Li">Shiyue Li</name>
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<nlm:aff id="aff1">National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University,
<institution>State Key Laboratory of Respiratory Disease (Guangzhou Medical University)</institution>
,
<addr-line>Guangzhou 510230</addr-line>
,
<country>China</country>
;</nlm:aff>
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<author>
<name sortKey="Li, Yimin" sort="Li, Yimin" uniqKey="Li Y" first="Yimin" last="Li">Yimin Li</name>
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<nlm:aff id="aff1">National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University,
<institution>State Key Laboratory of Respiratory Disease (Guangzhou Medical University)</institution>
,
<addr-line>Guangzhou 510230</addr-line>
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<country>China</country>
;</nlm:aff>
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<name sortKey="Ye, Feng" sort="Ye, Feng" uniqKey="Ye F" first="Feng" last="Ye">Feng Ye</name>
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<nlm:aff id="aff1">National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University,
<institution>State Key Laboratory of Respiratory Disease (Guangzhou Medical University)</institution>
,
<addr-line>Guangzhou 510230</addr-line>
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<country>China</country>
;</nlm:aff>
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<author>
<name sortKey="Guan, Weijie" sort="Guan, Weijie" uniqKey="Guan W" first="Weijie" last="Guan">Weijie Guan</name>
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<name sortKey="Yang, Yifan" sort="Yang, Yifan" uniqKey="Yang Y" first="Yifan" last="Yang">Yifan Yang</name>
<affiliation>
<nlm:aff id="aff6">Transwarp Technologies (Shanghai) Co.,
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,
<addr-line>Shanghai 200030</addr-line>
,
<country>China</country>
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</affiliation>
</author>
<author>
<name sortKey="Li, Fei" sort="Li, Fei" uniqKey="Li F" first="Fei" last="Li">Fei Li</name>
<affiliation>
<nlm:aff id="aff6">Transwarp Technologies (Shanghai) Co.,
<institution>Ltd.</institution>
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<addr-line>Shanghai 200030</addr-line>
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<country>China</country>
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</affiliation>
</author>
<author>
<name sortKey="Luo, Shengmei" sort="Luo, Shengmei" uniqKey="Luo S" first="Shengmei" last="Luo">Shengmei Luo</name>
<affiliation>
<nlm:aff id="aff6">Transwarp Technologies (Shanghai) Co.,
<institution>Ltd.</institution>
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<addr-line>Shanghai 200030</addr-line>
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<country>China</country>
;</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Xie, Yuqi" sort="Xie, Yuqi" uniqKey="Xie Y" first="Yuqi" last="Xie">Yuqi Xie</name>
<affiliation>
<nlm:aff id="aff1">National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University,
<institution>State Key Laboratory of Respiratory Disease (Guangzhou Medical University)</institution>
,
<addr-line>Guangzhou 510230</addr-line>
,
<country>China</country>
;</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Liu, Bin" sort="Liu, Bin" uniqKey="Liu B" first="Bin" last="Liu">Bin Liu</name>
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<nlm:aff id="aff7">
<institution>Kunming University of Science and Technology</institution>
,
<addr-line>Kunming 650504</addr-line>
,
<country>China</country>
</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Wang, Zhoulang" sort="Wang, Zhoulang" uniqKey="Wang Z" first="Zhoulang" last="Wang">Zhoulang Wang</name>
<affiliation>
<nlm:aff id="aff1">National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University,
<institution>State Key Laboratory of Respiratory Disease (Guangzhou Medical University)</institution>
,
<addr-line>Guangzhou 510230</addr-line>
,
<country>China</country>
;</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Zhang, Shaobo" sort="Zhang, Shaobo" uniqKey="Zhang S" first="Shaobo" last="Zhang">Shaobo Zhang</name>
<affiliation>
<nlm:aff id="aff3">Hengqin WhaleMed Technology Co.,
<institution>Ltd.</institution>
,
<addr-line>Zhuhai 519000</addr-line>
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<country>China</country>
;</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Wang, Yaonan" sort="Wang, Yaonan" uniqKey="Wang Y" first="Yaonan" last="Wang">Yaonan Wang</name>
<affiliation>
<nlm:aff id="aff3">Hengqin WhaleMed Technology Co.,
<institution>Ltd.</institution>
,
<addr-line>Zhuhai 519000</addr-line>
,
<country>China</country>
;</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Zhong, Nanshan" sort="Zhong, Nanshan" uniqKey="Zhong N" first="Nanshan" last="Zhong">Nanshan Zhong</name>
<affiliation>
<nlm:aff id="aff1">National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University,
<institution>State Key Laboratory of Respiratory Disease (Guangzhou Medical University)</institution>
,
<addr-line>Guangzhou 510230</addr-line>
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<country>China</country>
;</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="He, Jianxing" sort="He, Jianxing" uniqKey="He J" first="Jianxing" last="He">Jianxing He</name>
<affiliation>
<nlm:aff id="aff1">National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University,
<institution>State Key Laboratory of Respiratory Disease (Guangzhou Medical University)</institution>
,
<addr-line>Guangzhou 510230</addr-line>
,
<country>China</country>
;</nlm:aff>
</affiliation>
</author>
</analytic>
<series>
<title level="j">Journal of Thoracic Disease</title>
<idno type="ISSN">2072-1439</idno>
<idno type="eISSN">2077-6624</idno>
<imprint>
<date when="2020">2020</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc>
<textClass></textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">
<sec>
<title>Background</title>
<p>The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with
<italic>chunyun</italic>
, 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.</p>
</sec>
<sec>
<title>Methods</title>
<p>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.</p>
</sec>
<sec>
<title>Results</title>
<p>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.</p>
</sec>
<sec>
<title>Conclusions</title>
<p>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.</p>
</sec>
</div>
</front>
</TEI>
<pmc article-type="research-article">
<pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">J Thorac Dis</journal-id>
<journal-id journal-id-type="iso-abbrev">J Thorac Dis</journal-id>
<journal-id journal-id-type="publisher-id">JTD</journal-id>
<journal-title-group>
<journal-title>Journal of Thoracic Disease</journal-title>
</journal-title-group>
<issn pub-type="ppub">2072-1439</issn>
<issn pub-type="epub">2077-6624</issn>
<publisher>
<publisher-name>AME Publishing Company</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">32274081</article-id>
<article-id pub-id-type="pmc">7139011</article-id>
<article-id pub-id-type="publisher-id">jtd-12-03-165</article-id>
<article-id pub-id-type="doi">10.21037/jtd.2020.02.64</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Yang</surname>
<given-names>Zifeng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="afn2">
<sup>#</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zeng</surname>
<given-names>Zhiqi</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="afn2">
<sup>#</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Ke</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="afn2">
<sup>#</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wong</surname>
<given-names>Sook-San</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="author-notes" rid="afn2">
<sup>#</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liang</surname>
<given-names>Wenhua</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="afn2">
<sup>#</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zanin</surname>
<given-names>Mark</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="author-notes" rid="afn2">
<sup>#</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Peng</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<xref ref-type="author-notes" rid="afn2">
<sup>#</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cao</surname>
<given-names>Xudong</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Gao</surname>
<given-names>Zhongqiang</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Mai</surname>
<given-names>Zhitong</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liang</surname>
<given-names>Jingyi</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Xiaoqing</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Shiyue</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Yimin</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ye</surname>
<given-names>Feng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Guan</surname>
<given-names>Weijie</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Yang</surname>
<given-names>Yifan</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Fei</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Luo</surname>
<given-names>Shengmei</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Xie</surname>
<given-names>Yuqi</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Bin</given-names>
</name>
<xref ref-type="aff" rid="aff7">
<sup>7</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Zhoulang</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Shaobo</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Yaonan</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhong</surname>
<given-names>Nanshan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>He</surname>
<given-names>Jianxing</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<aff id="aff1">
<label>1</label>
National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University,
<institution>State Key Laboratory of Respiratory Disease (Guangzhou Medical University)</institution>
,
<addr-line>Guangzhou 510230</addr-line>
,
<country>China</country>
;</aff>
<aff id="aff2">
<label>2</label>
Macau Institute for Applied Research in Medicine and Health, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau,
<country>China</country>
;</aff>
<aff id="aff3">
<label>3</label>
Hengqin WhaleMed Technology Co.,
<institution>Ltd.</institution>
,
<addr-line>Zhuhai 519000</addr-line>
,
<country>China</country>
;</aff>
<aff id="aff4">
<label>4</label>
School of Public Health, The University of Hong Kong, Hong Kong,
<country>China</country>
;</aff>
<aff id="aff5">
<label>5</label>
Jinling Institute of Technology, Nanjing Innovative Data Technologies,
<institution>Inc.</institution>
,
<addr-line>Nanjing 210014</addr-line>
,
<country>China</country>
;</aff>
<aff id="aff6">
<label>6</label>
Transwarp Technologies (Shanghai) Co.,
<institution>Ltd.</institution>
,
<addr-line>Shanghai 200030</addr-line>
,
<country>China</country>
;</aff>
<aff id="aff7">
<label>7</label>
<institution>Kunming University of Science and Technology</institution>
,
<addr-line>Kunming 650504</addr-line>
,
<country>China</country>
</aff>
</contrib-group>
<author-notes>
<fn id="afn1">
<p>
<italic>Contributions:</italic>
(I) Conception and design: J He, N Zhong; (II) Administrative support: J He, N Zhong; (III) Provision of study materials or patients: Not applicable; (IV) Collection and assembly of data: Z Mai, J Liang, X Liu, S Li, Y Li, F Ye, W Guan, Y Yang, F Li, S Luo, Y Xie, B Liu, Z Wang, S Zhang, Y Wang; (V) Data analysis and interpretation: J He, Z Yang, Z Zeng, K Wang, SS Wong, W Liang, M Zanin, P Liu, X Cao, Z Gao; (VI) Manuscript writing: J He, Z Yang, Z Zeng, K Wang, SS Wong, W Liang, M Zanin, P Liu; (VII) Final approval of manuscript: All authors.</p>
</fn>
<fn id="afn2" fn-type="equal">
<label>#</label>
<p>These authors contributed equally to this work.</p>
</fn>
<corresp id="cor1">
<italic>Correspondence to:</italic>
Jianxing He, MD; Nanshan Zhong, MD. National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease (Guangzhou Medical University), Guangzhou 510120, China. Email:
<email xlink:href="hejx@vip.163.com">hejx@vip.163.com</email>
;
<email xlink:href="nanshan@vip.163.com">nanshan@vip.163.com</email>
.</corresp>
<fn fn-type="COI-statement">
<p>
<italic>Conflicts of Interest:</italic>
NZ serves as the unpaid Editor-in Chief of
<italic>Journal of Thoracic Disease</italic>
. JH serves as the unpaid Executive Editor-in-Chief of
<italic>Journal of Thoracic Disease</italic>
. WL serves as an unpaid Editorial Board Member (Thoracic Surgery) of
<italic>Journal of Thoracic Disease</italic>
. The other authors have no conflicts of interest to declare.</p>
</fn>
</author-notes>
<pub-date pub-type="epub-ppub">
<month>3</month>
<year>2020</year>
</pub-date>
<pmc-comment>Fake ppub date generated by PMC from publisher pub-date/@pub-type='epub-ppub' </pmc-comment>
<pub-date pub-type="ppub">
<month>3</month>
<year>2020</year>
</pub-date>
<volume>12</volume>
<issue>3</issue>
<fpage>165</fpage>
<lpage>174</lpage>
<history>
<date date-type="received">
<day>27</day>
<month>2</month>
<year>2020</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>2</month>
<year>2020</year>
</date>
</history>
<permissions>
<copyright-statement>2020 Journal of Thoracic Disease. All rights reserved.</copyright-statement>
<copyright-year>2020</copyright-year>
<copyright-holder>Journal of Thoracic Disease.</copyright-holder>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with
<italic>chunyun</italic>
, 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.</p>
</sec>
<sec>
<title>Methods</title>
<p>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.</p>
</sec>
<sec>
<title>Results</title>
<p>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.</p>
</sec>
<sec>
<title>Conclusions</title>
<p>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.</p>
</sec>
</abstract>
<kwd-group kwd-group-type="author">
<title>Keywords: </title>
<kwd>Coronavirus disease 2019 (COVID-19)</kwd>
<kwd>severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)</kwd>
<kwd>epidemic</kwd>
<kwd>modeling</kwd>
<kwd>Susceptible-Exposed-Infectious-Removed (SEIR)</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>

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