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A conceptual model for the outbreak of Coronavirus disease 2019 (COVID-19) in Wuhan, China with individual reaction and governmental action

Identifieur interne : 000030 ( Pmc/Checkpoint ); précédent : 000029; suivant : 000031

A conceptual model for the outbreak of Coronavirus disease 2019 (COVID-19) in Wuhan, China with individual reaction and governmental action

Auteurs : Qianying Lin [États-Unis] ; Shi Zhao [République populaire de Chine] ; Daozhou Gao [République populaire de Chine] ; Yijun Lou [République populaire de Chine] ; Shu Yang [République populaire de Chine] ; Salihu S. Musa [République populaire de Chine] ; Maggie H. Wang [République populaire de Chine] ; Yongli Cai [République populaire de Chine] ; Weiming Wang [République populaire de Chine] ; Lin Yang [République populaire de Chine] ; Daihai He [République populaire de Chine]

Source :

RBID : PMC:7102659

Abstract

Highlights

For the ongoing novel coronavirus disease (CODID-19) outbreak in Wuhan, China, the Chinese government has implemented control measures such as city lockdown to mitigate the impact of the epidemic.

We model the outbreak in Wuhan with individual reaction and governmental action (holiday extension, city lockdown, hospitalisation and quarantine) based on some parameters of the 1918 influenza pandemic in London, United Kingdom.

We show the different effects of individual reaction and governmental action and preliminarily estimate the magnitude of these effects.

We also preliminarily estimate the time-varying reporting ratio.


Url:
DOI: 10.1016/j.ijid.2020.02.058
PubMed: 32145465
PubMed Central: 7102659


Affiliations:


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PMC:7102659

Le document en format XML

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<p id="p0005">For the ongoing novel coronavirus disease (CODID-19) outbreak in Wuhan, China, the Chinese government has implemented control measures such as city lockdown to mitigate the impact of the epidemic.</p>
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<p id="p0010">We model the outbreak in Wuhan with individual reaction and governmental action (holiday extension, city lockdown, hospitalisation and quarantine) based on some parameters of the 1918 influenza pandemic in London, United Kingdom.</p>
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<p id="p0015">We show the different effects of individual reaction and governmental action and preliminarily estimate the magnitude of these effects.</p>
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<p id="p0020">We also preliminarily estimate the time-varying reporting ratio.</p>
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<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Int J Infect Dis</journal-id>
<journal-id journal-id-type="iso-abbrev">Int. J. Infect. Dis</journal-id>
<journal-title-group>
<journal-title>International Journal of Infectious Diseases</journal-title>
</journal-title-group>
<issn pub-type="ppub">1201-9712</issn>
<issn pub-type="epub">1878-3511</issn>
<publisher>
<publisher-name>The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">32145465</article-id>
<article-id pub-id-type="pmc">7102659</article-id>
<article-id pub-id-type="publisher-id">S1201-9712(20)30117-X</article-id>
<article-id pub-id-type="doi">10.1016/j.ijid.2020.02.058</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>A conceptual model for the outbreak of Coronavirus disease 2019 (COVID-19) in Wuhan, China with individual reaction and governmental action</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" id="aut0005">
<name>
<surname>Lin</surname>
<given-names>Qianying</given-names>
</name>
<email>qianying@umich.edu</email>
<xref rid="aff0005" ref-type="aff">a</xref>
<xref rid="fn0005" ref-type="fn">1</xref>
</contrib>
<contrib contrib-type="author" id="aut0010">
<name>
<surname>Zhao</surname>
<given-names>Shi</given-names>
</name>
<email>zhaoshi.cmsa@gmail.com</email>
<xref rid="aff0010" ref-type="aff">b</xref>
<xref rid="aff0015" ref-type="aff">c</xref>
<xref rid="fn0005" ref-type="fn">1</xref>
</contrib>
<contrib contrib-type="author" id="aut0015">
<name>
<surname>Gao</surname>
<given-names>Daozhou</given-names>
</name>
<email>dzgao@shnu.edu.cn</email>
<xref rid="aff0020" ref-type="aff">d</xref>
</contrib>
<contrib contrib-type="author" id="aut0020">
<name>
<surname>Lou</surname>
<given-names>Yijun</given-names>
</name>
<email>yijun.lou@polyu.edu.hk</email>
<xref rid="aff0025" ref-type="aff">e</xref>
</contrib>
<contrib contrib-type="author" id="aut0025">
<name>
<surname>Yang</surname>
<given-names>Shu</given-names>
</name>
<email>sishiyu1978@qq.com</email>
<xref rid="aff0030" ref-type="aff">f</xref>
</contrib>
<contrib contrib-type="author" id="aut0030">
<name>
<surname>Musa</surname>
<given-names>Salihu S</given-names>
</name>
<email>salihu-sabiu.musa@connect.polyu.hk</email>
<xref rid="aff0025" ref-type="aff">e</xref>
</contrib>
<contrib contrib-type="author" id="aut0035">
<name>
<surname>Wang</surname>
<given-names>Maggie H</given-names>
</name>
<email>haitian.wang@gmail.com</email>
<xref rid="aff0010" ref-type="aff">b</xref>
<xref rid="aff0015" ref-type="aff">c</xref>
</contrib>
<contrib contrib-type="author" id="aut0040">
<name>
<surname>Cai</surname>
<given-names>Yongli</given-names>
</name>
<email>yonglicai@hytc.edu.cn</email>
<xref rid="aff0035" ref-type="aff">g</xref>
</contrib>
<contrib contrib-type="author" id="aut0045">
<name>
<surname>Wang</surname>
<given-names>Weiming</given-names>
</name>
<email>weimingwang2003@163.com</email>
<xref rid="aff0035" ref-type="aff">g</xref>
<xref rid="cor0005" ref-type="corresp"></xref>
</contrib>
<contrib contrib-type="author" id="aut0050">
<name>
<surname>Yang</surname>
<given-names>Lin</given-names>
</name>
<email>l.yang@polyu.edu.hk</email>
<xref rid="aff0040" ref-type="aff">h</xref>
<xref rid="cor0005" ref-type="corresp"></xref>
</contrib>
<contrib contrib-type="author" id="aut0055">
<name>
<surname>He</surname>
<given-names>Daihai</given-names>
</name>
<email>daihai.he@polyu.edu.hk</email>
<xref rid="aff0025" ref-type="aff">e</xref>
<xref rid="cor0005" ref-type="corresp"></xref>
</contrib>
</contrib-group>
<aff id="aff0005">
<label>a</label>
Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, USA</aff>
<aff id="aff0010">
<label>b</label>
JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China</aff>
<aff id="aff0015">
<label>c</label>
Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, China</aff>
<aff id="aff0020">
<label>d</label>
Mathematics and Science College, Shanghai Normal University, Shanghai, China</aff>
<aff id="aff0025">
<label>e</label>
Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China</aff>
<aff id="aff0030">
<label>f</label>
College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, China</aff>
<aff id="aff0035">
<label>g</label>
School of Mathematics and Statistics, Huaiyin Normal University, Huai’an, China</aff>
<aff id="aff0040">
<label>h</label>
School of Nursing, Hong Kong Polytechnic University, Hong Kong, China</aff>
<author-notes>
<corresp id="cor0005">
<label></label>
Corresponding authors.
<email>weimingwang2003@163.com</email>
<email>l.yang@polyu.edu.hk</email>
<email>daihai.he@polyu.edu.hk</email>
</corresp>
<fn id="fn0005">
<label>1</label>
<p id="npar0010">These authors equally contributed..</p>
</fn>
</author-notes>
<pub-date pub-type="pmc-release">
<day>4</day>
<month>3</month>
<year>2020</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="epub">
<day>4</day>
<month>3</month>
<year>2020</year>
</pub-date>
<elocation-id></elocation-id>
<history>
<date date-type="received">
<day>31</day>
<month>1</month>
<year>2020</year>
</date>
<date date-type="rev-recd">
<day>26</day>
<month>2</month>
<year>2020</year>
</date>
<date date-type="accepted">
<day>27</day>
<month>2</month>
<year>2020</year>
</date>
</history>
<permissions>
<copyright-statement>© 2020 The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.</copyright-statement>
<copyright-year>2020</copyright-year>
<copyright-holder></copyright-holder>
<license>
<license-p>Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</license-p>
</license>
</permissions>
<abstract abstract-type="author-highlights" id="abs0005">
<title>Highlights</title>
<p>
<list list-type="simple" id="list0005">
<list-item id="listitem0005">
<label></label>
<p id="p0005">For the ongoing novel coronavirus disease (CODID-19) outbreak in Wuhan, China, the Chinese government has implemented control measures such as city lockdown to mitigate the impact of the epidemic.</p>
</list-item>
<list-item id="listitem0010">
<label></label>
<p id="p0010">We model the outbreak in Wuhan with individual reaction and governmental action (holiday extension, city lockdown, hospitalisation and quarantine) based on some parameters of the 1918 influenza pandemic in London, United Kingdom.</p>
</list-item>
<list-item id="listitem0015">
<label></label>
<p id="p0015">We show the different effects of individual reaction and governmental action and preliminarily estimate the magnitude of these effects.</p>
</list-item>
<list-item id="listitem0020">
<label></label>
<p id="p0020">We also preliminarily estimate the time-varying reporting ratio.</p>
</list-item>
</list>
</p>
</abstract>
<abstract id="abs0010">
<p>The ongoing Coronavirus Disease 2019 (COVID-19) outbreak, originated in the end of 2019 in Wuhan, China, has claimed more than 2200 lives and posed a huge threat to global public health. The Chinese government has implemented control measures including setting up special hospitals and travel restriction to mitigate the spread. We propose conceptual models for the outbreak in Wuhan with the consideration of individual behavioural reaction and governmental actions, e.g., holiday extension, travel restriction, hospitalisation and quarantine. We employed the estimates of these two key components from the 1918 influenza pandemic in London, United Kingdom, incorporated zoonotic introductions and the emigration, then computed future trends and the reporting ratio. The model is concise in structure, and it successfully captures the course of the COVID-19 outbreak, and thus sheds light on understanding the trends of the outbreak.</p>
</abstract>
<kwd-group id="kwd0005">
<title>Keywords</title>
<kwd>COVID-19</kwd>
<kwd>Epidemic</kwd>
<kwd>mathematical modelling</kwd>
<kwd>individual reaction</kwd>
<kwd>governmental action</kwd>
<kwd>city lock-down</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
<affiliations>
<list>
<country>
<li>République populaire de Chine</li>
<li>États-Unis</li>
</country>
<region>
<li>Guangdong</li>
<li>Michigan</li>
</region>
<settlement>
<li>Sha Tin</li>
<li>Shenzhen</li>
</settlement>
<orgName>
<li>Université chinoise de Hong Kong</li>
</orgName>
</list>
<tree>
<country name="États-Unis">
<region name="Michigan">
<name sortKey="Lin, Qianying" sort="Lin, Qianying" uniqKey="Lin Q" first="Qianying" last="Lin">Qianying Lin</name>
</region>
</country>
<country name="République populaire de Chine">
<noRegion>
<name sortKey="Zhao, Shi" sort="Zhao, Shi" uniqKey="Zhao S" first="Shi" last="Zhao">Shi Zhao</name>
</noRegion>
<name sortKey="Cai, Yongli" sort="Cai, Yongli" uniqKey="Cai Y" first="Yongli" last="Cai">Yongli Cai</name>
<name sortKey="Gao, Daozhou" sort="Gao, Daozhou" uniqKey="Gao D" first="Daozhou" last="Gao">Daozhou Gao</name>
<name sortKey="He, Daihai" sort="He, Daihai" uniqKey="He D" first="Daihai" last="He">Daihai He</name>
<name sortKey="Lou, Yijun" sort="Lou, Yijun" uniqKey="Lou Y" first="Yijun" last="Lou">Yijun Lou</name>
<name sortKey="Musa, Salihu S" sort="Musa, Salihu S" uniqKey="Musa S" first="Salihu S" last="Musa">Salihu S. Musa</name>
<name sortKey="Wang, Maggie H" sort="Wang, Maggie H" uniqKey="Wang M" first="Maggie H" last="Wang">Maggie H. Wang</name>
<name sortKey="Wang, Maggie H" sort="Wang, Maggie H" uniqKey="Wang M" first="Maggie H" last="Wang">Maggie H. Wang</name>
<name sortKey="Wang, Weiming" sort="Wang, Weiming" uniqKey="Wang W" first="Weiming" last="Wang">Weiming Wang</name>
<name sortKey="Yang, Lin" sort="Yang, Lin" uniqKey="Yang L" first="Lin" last="Yang">Lin Yang</name>
<name sortKey="Yang, Shu" sort="Yang, Shu" uniqKey="Yang S" first="Shu" last="Yang">Shu Yang</name>
<name sortKey="Zhao, Shi" sort="Zhao, Shi" uniqKey="Zhao S" first="Shi" last="Zhao">Shi Zhao</name>
</country>
</tree>
</affiliations>
</record>

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