Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS
Identifieur interne : 000D25 ( Pmc/Curation ); précédent : 000D24; suivant : 000D26Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS
Auteurs : Kaihao LiangSource :
- Infection, Genetics and Evolution [ 1567-1348 ] ; 2020.
Abstract
The purpose of this paper is to reveal the spread rules of the three pneumonia: COVID-19, SARS and MERS. We compare the new spread characteristics of COVID-19 with those of SARS and MERS. By considering the growth rate and inhibition constant of infectious diseases, their propagation growth model is established. The parameters of the three coronavirus transmission growth models are obtained by nonlinear fitting. Parametric analysis shows that the growth rate of COVID-19 is about twice that of the SARS and MERS, and the COVID-19 doubling cycle is two to three days, suggesting that the number of COVID-19 patients would double in two to three days without human intervention. The infection inhibition constant in Hubei is two orders of magnitude lower than in other regions, which reasonably explains the situation of the COVID-19 outbreak in Hubei.
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DOI: 10.1016/j.meegid.2020.104306
PubMed: 32278147
PubMed Central: 7141629
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS</title><author><name sortKey="Liang, Kaihao" sort="Liang, Kaihao" uniqKey="Liang K" first="Kaihao" last="Liang">Kaihao Liang</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32278147</idno><idno type="pmc">7141629</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141629</idno><idno type="RBID">PMC:7141629</idno><idno type="doi">10.1016/j.meegid.2020.104306</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">000D25</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000D25</idno><idno type="wicri:Area/Pmc/Curation">000D25</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000D25</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS</title><author><name sortKey="Liang, Kaihao" sort="Liang, Kaihao" uniqKey="Liang K" first="Kaihao" last="Liang">Kaihao Liang</name></author></analytic><series><title level="j">Infection, Genetics and Evolution</title><idno type="ISSN">1567-1348</idno><idno type="eISSN">1567-7257</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The purpose of this paper is to reveal the spread rules of the three pneumonia: COVID-19, SARS and MERS. We compare the new spread characteristics of COVID-19 with those of SARS and MERS. By considering the growth rate and inhibition constant of infectious diseases, their propagation growth model is established. The parameters of the three coronavirus transmission growth models are obtained by nonlinear fitting. Parametric analysis shows that the growth rate of COVID-19 is about twice that of the SARS and MERS, and the COVID-19 doubling cycle is two to three days, suggesting that the number of COVID-19 patients would double in two to three days without human intervention. The infection inhibition constant in Hubei is two orders of magnitude lower than in other regions, which reasonably explains the situation of the COVID-19 outbreak in Hubei.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Bi, N" uniqKey="Bi N">N. Bi</name></author><author><name sortKey="Liang, K H" uniqKey="Liang K">K.H. 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Hui</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Li, R" uniqKey="Li R">R. Li</name></author><author><name sortKey="Wang, W" uniqKey="Wang W">W. Wang</name></author><author><name sortKey="Shu, P" uniqKey="Shu P">P. Shu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Li, Z" uniqKey="Li Z">Z. Li</name></author><author><name sortKey="Chen, X" uniqKey="Chen X">X. Chen</name></author><author><name sortKey="Teng, H" uniqKey="Teng H">H. Teng</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Liang, K H" uniqKey="Liang K">K.H. Liang</name></author><author><name sortKey="Clay, M J" uniqKey="Clay M">M.J. Clay</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lipsitch, M" uniqKey="Lipsitch M">M. Lipsitch</name></author><author><name sortKey="Ted, C" uniqKey="Ted C">C. Ted</name></author><author><name sortKey="Ben, C" uniqKey="Ben C">C. Ben</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Liu, Q" uniqKey="Liu Q">Q. Liu</name></author><author><name sortKey="Ajelli, M" uniqKey="Ajelli M">M. Ajelli</name></author><author><name sortKey="Aleta, A" uniqKey="Aleta A">A. Aleta</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lombardi, A" uniqKey="Lombardi A">A. Lombardi</name></author><author><name sortKey="Bozzi, G" uniqKey="Bozzi G">G. Bozzi</name></author><author><name sortKey="Mangioni, D" uniqKey="Mangioni D">D. Mangioni</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lu, Y" uniqKey="Lu Y">Y. Lu</name></author><author><name sortKey="Liu, J" uniqKey="Liu J">J. Liu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ning, Y" uniqKey="Ning Y">Y. Ning</name></author><author><name sortKey="Liu, X" uniqKey="Liu X">X. Liu</name></author><author><name sortKey="Cheng, H" uniqKey="Cheng H">H. Cheng</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Nishiura, H" uniqKey="Nishiura H">H. Nishiura</name></author><author><name sortKey="Endo, A" uniqKey="Endo A">A. Endo</name></author><author><name sortKey="Saitoh, M" uniqKey="Saitoh M">M. Saitoh</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Riley, S" uniqKey="Riley S">S. Riley</name></author><author><name sortKey="Christophe, F" uniqKey="Christophe F">F. Christophe</name></author><author><name sortKey="Donnelly, C" uniqKey="Donnelly C">C. Donnelly</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tan, X" uniqKey="Tan X">X. Tan</name></author><author><name sortKey="Feng, E" uniqKey="Feng E">E. Feng</name></author><author><name sortKey="Xu, G" uniqKey="Xu G">G. Xu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Yang, Z" uniqKey="Yang Z">Z. Yang</name></author><author><name sortKey="Yuan, Z" uniqKey="Yuan Z">Z. Yuan</name></author><author><name sortKey="Jia, Z" uniqKey="Jia Z">Z. Jia</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhang, X" uniqKey="Zhang X">X. Zhang</name></author><author><name sortKey="Wu, J" uniqKey="Wu J">J. Wu</name></author><author><name sortKey="Zhao, P" uniqKey="Zhao P">P. Zhao</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Infect Genet Evol</journal-id><journal-id journal-id-type="iso-abbrev">Infect. Genet. Evol</journal-id><journal-title-group><journal-title>Infection, Genetics and Evolution</journal-title></journal-title-group><issn pub-type="ppub">1567-1348</issn><issn pub-type="epub">1567-7257</issn><publisher><publisher-name>Published by Elsevier B.V.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32278147</article-id><article-id pub-id-type="pmc">7141629</article-id><article-id pub-id-type="publisher-id">S1567-1348(20)30137-4</article-id><article-id pub-id-type="doi">10.1016/j.meegid.2020.104306</article-id><article-id pub-id-type="publisher-id">104306</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS</article-title></title-group><contrib-group><contrib contrib-type="author" id="au0005"><name><surname>Liang</surname><given-names>Kaihao</given-names></name><email>karman03@126.com</email></contrib></contrib-group><aff id="af0005">College of Computational Science, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China</aff><pub-date pub-type="pmc-release"><day>8</day><month>4</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="ppub"><month>8</month><year>2020</year></pub-date><pub-date pub-type="epub"><day>8</day><month>4</month><year>2020</year></pub-date><volume>82</volume><fpage>104306</fpage><lpage>104306</lpage><history><date date-type="received"><day>10</day><month>3</month><year>2020</year></date><date date-type="rev-recd"><day>24</day><month>3</month><year>2020</year></date><date date-type="accepted"><day>27</day><month>3</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 Published by Elsevier B.V.</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 id="ab0005"><p>The purpose of this paper is to reveal the spread rules of the three pneumonia: COVID-19, SARS and MERS. We compare the new spread characteristics of COVID-19 with those of SARS and MERS. By considering the growth rate and inhibition constant of infectious diseases, their propagation growth model is established. The parameters of the three coronavirus transmission growth models are obtained by nonlinear fitting. Parametric analysis shows that the growth rate of COVID-19 is about twice that of the SARS and MERS, and the COVID-19 doubling cycle is two to three days, suggesting that the number of COVID-19 patients would double in two to three days without human intervention. The infection inhibition constant in Hubei is two orders of magnitude lower than in other regions, which reasonably explains the situation of the COVID-19 outbreak in Hubei.</p></abstract><abstract abstract-type="author-highlights" id="ab0010"><title>Highlights</title><p><list list-type="simple" id="l0005"><list-item id="li0005"><label>•</label><p id="p0005">The growth rate of SARS-CoV-2 is about twice that of the SARS and MERS viruses.</p></list-item><list-item id="li0010"><label>•</label><p id="p0010">Doubling cycle for the cases of COVID-19 is two to three days if without intervention.</p></list-item><list-item id="li0015"><label>•</label><p id="p0015">The infection inhibition constant in Hubei is two orders of magnitude lower than in other regions.</p></list-item></list></p></abstract><kwd-group id="ks0005"><title>Keywords</title><kwd>Coronavirus, COVID-19, SARS, MERS, Infectious kinetics</kwd><kwd><italic>2008 MSC:</italic> R181.2</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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