[Estimating the basic reproduction number of COVID-19 in Wuhan, China].
Identifieur interne : 000327 ( PubMed/Curation ); précédent : 000326; suivant : 000328[Estimating the basic reproduction number of COVID-19 in Wuhan, China].
Auteurs : Y. Wang [République populaire de Chine] ; X Y You [République populaire de Chine] ; Y J Wang ; L P Peng [République populaire de Chine] ; Z C Du [République populaire de Chine] ; S. Gilmour ; D. Yoneoka ; J. Gu [République populaire de Chine] ; C. Hao [République populaire de Chine] ; Y T Hao [République populaire de Chine] ; J H Li [République populaire de Chine]Source :
- Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi [ 0254-6450 ] ; 2020.
Abstract
Objective: The number of confirmed and suspected cases of the COVID-19 in Hubei province is still increasing. However, the estimations of the basic reproduction number of COVID-19 varied greatly across studies. The objectives of this study are 1) to estimate the basic reproduction number (R(0)) of COVID-19 reflecting the infectiousness of the virus and 2) to assess the effectiveness of a range of controlling intervention. Method: The reported number of daily confirmed cases from January 17 to February 8, 2020 in Hubei province were collected and used for model fit. Four methods, the exponential growth (EG), maximum likelihood estimation (ML), sequential Bayesian method (SB) and time dependent reproduction numbers (TD), were applied to estimate the R(0). Result: Among the four methods, the EG method fitted the data best. The estimated R(0) was 3.49 (95% CI: 3.42-3.58) by using EG method. The R(0) was estimated to be 2.95 (95%CI: 2.86-3.03) after taking control measures. Conclusion: In the early stage of the epidemic, it is appropriate to estimate R(0) using the EG method. Meanwhile, timely and effective control measures were warranted to further reduce the spread of COVID-19.
DOI: 10.3760/cma.j.cn112338-20200210-00086
PubMed: 32125128
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Y J Wang<affiliation><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</nlm:affiliation><wicri:noCountry code="subField">Japan 104-0045</wicri:noCountry></affiliation><affiliation><nlm:affiliation>Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</nlm:affiliation><wicri:noCountry code="subField">Japan 104-0045</wicri:noCountry></affiliation><affiliation><nlm:affiliation>Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</nlm:affiliation><wicri:noCountry code="subField">Japan 104-0045</wicri:noCountry></affiliation>Le document en format XML
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Wang</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080</wicri:regionArea></affiliation></author><author><name sortKey="You, X Y" sort="You, X Y" uniqKey="You X" first="X Y" last="You">X Y You</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080</wicri:regionArea></affiliation></author><author><name sortKey="Wang, Y J" sort="Wang, Y J" uniqKey="Wang Y" first="Y J" last="Wang">Y J Wang</name><affiliation><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</nlm:affiliation><wicri:noCountry code="subField">Japan 104-0045</wicri:noCountry></affiliation></author><author><name sortKey="Peng, L P" sort="Peng, L P" uniqKey="Peng L" first="L P" last="Peng">L P Peng</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080</wicri:regionArea></affiliation></author><author><name sortKey="Du, Z C" sort="Du, Z C" uniqKey="Du Z" first="Z C" last="Du">Z C Du</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080</wicri:regionArea></affiliation></author><author><name sortKey="Gilmour, S" sort="Gilmour, S" uniqKey="Gilmour S" first="S" last="Gilmour">S. Gilmour</name><affiliation><nlm:affiliation>Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</nlm:affiliation><wicri:noCountry code="subField">Japan 104-0045</wicri:noCountry></affiliation></author><author><name sortKey="Yoneoka, D" sort="Yoneoka, D" uniqKey="Yoneoka D" first="D" last="Yoneoka">D. Yoneoka</name><affiliation><nlm:affiliation>Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</nlm:affiliation><wicri:noCountry code="subField">Japan 104-0045</wicri:noCountry></affiliation></author><author><name sortKey="Gu, J" sort="Gu, J" uniqKey="Gu J" first="J" last="Gu">J. Gu</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275</wicri:regionArea></affiliation></author><author><name sortKey="Hao, C" sort="Hao, C" uniqKey="Hao C" first="C" last="Hao">C. Hao</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275</wicri:regionArea></affiliation></author><author><name sortKey="Hao, Y T" sort="Hao, Y T" uniqKey="Hao Y" first="Y T" last="Hao">Y T Hao</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275</wicri:regionArea></affiliation></author><author><name sortKey="Li, J H" sort="Li, J H" uniqKey="Li J" first="J H" last="Li">J H Li</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32125128</idno><idno type="pmid">32125128</idno><idno type="doi">10.3760/cma.j.cn112338-20200210-00086</idno><idno type="wicri:Area/PubMed/Corpus">000327</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000327</idno><idno type="wicri:Area/PubMed/Curation">000327</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000327</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">[Estimating the basic reproduction number of COVID-19 in Wuhan, China].</title><author><name sortKey="Wang, Y" sort="Wang, Y" uniqKey="Wang Y" first="Y" last="Wang">Y. Wang</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080</wicri:regionArea></affiliation></author><author><name sortKey="You, X Y" sort="You, X Y" uniqKey="You X" first="X Y" last="You">X Y You</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080</wicri:regionArea></affiliation></author><author><name sortKey="Wang, Y J" sort="Wang, Y J" uniqKey="Wang Y" first="Y J" last="Wang">Y J Wang</name><affiliation><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</nlm:affiliation><wicri:noCountry code="subField">Japan 104-0045</wicri:noCountry></affiliation></author><author><name sortKey="Peng, L P" sort="Peng, L P" uniqKey="Peng L" first="L P" last="Peng">L P Peng</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080</wicri:regionArea></affiliation></author><author><name sortKey="Du, Z C" sort="Du, Z C" uniqKey="Du Z" first="Z C" last="Du">Z C Du</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080</wicri:regionArea></affiliation></author><author><name sortKey="Gilmour, S" sort="Gilmour, S" uniqKey="Gilmour S" first="S" last="Gilmour">S. Gilmour</name><affiliation><nlm:affiliation>Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</nlm:affiliation><wicri:noCountry code="subField">Japan 104-0045</wicri:noCountry></affiliation></author><author><name sortKey="Yoneoka, D" sort="Yoneoka, D" uniqKey="Yoneoka D" first="D" last="Yoneoka">D. Yoneoka</name><affiliation><nlm:affiliation>Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</nlm:affiliation><wicri:noCountry code="subField">Japan 104-0045</wicri:noCountry></affiliation></author><author><name sortKey="Gu, J" sort="Gu, J" uniqKey="Gu J" first="J" last="Gu">J. Gu</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275</wicri:regionArea></affiliation></author><author><name sortKey="Hao, C" sort="Hao, C" uniqKey="Hao C" first="C" last="Hao">C. Hao</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275</wicri:regionArea></affiliation></author><author><name sortKey="Hao, Y T" sort="Hao, Y T" uniqKey="Hao Y" first="Y T" last="Hao">Y T Hao</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275</wicri:regionArea></affiliation></author><author><name sortKey="Li, J H" sort="Li, J H" uniqKey="Li J" first="J H" last="Li">J H Li</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275</wicri:regionArea></affiliation></author></analytic><series><title level="j">Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi</title><idno type="ISSN">0254-6450</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><b>Objective:</b> The number of confirmed and suspected cases of the COVID-19 in Hubei province is still increasing. However, the estimations of the basic reproduction number of COVID-19 varied greatly across studies. The objectives of this study are 1) to estimate the basic reproduction number (<i>R(0)</i>) of COVID-19 reflecting the infectiousness of the virus and 2) to assess the effectiveness of a range of controlling intervention. <b>Method:</b> The reported number of daily confirmed cases from January 17 to February 8, 2020 in Hubei province were collected and used for model fit. Four methods, the exponential growth (EG), maximum likelihood estimation (ML), sequential Bayesian method (SB) and time dependent reproduction numbers (TD), were applied to estimate the <i>R(0)</i>. <b>Result:</b> Among the four methods, the EG method fitted the data best. The estimated <i>R(0)</i> was 3.49 (95% <i>CI</i>: 3.42-3.58) by using EG method. The <i>R(0)</i> was estimated to be 2.95 (95%<i>CI</i>: 2.86-3.03) after taking control measures. <b>Conclusion:</b> In the early stage of the epidemic, it is appropriate to estimate <i>R(0)</i> using the EG method. Meanwhile, timely and effective control measures were warranted to further reduce the spread of COVID-19.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32125128</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>03</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0254-6450</ISSN><JournalIssue CitedMedium="Print"><Volume>41</Volume><Issue>4</Issue><PubDate><Year>2020</Year><Month>Mar</Month><Day>03</Day></PubDate></JournalIssue><Title>Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi</Title><ISOAbbreviation>Zhonghua Liu Xing Bing Xue Za Zhi</ISOAbbreviation></Journal><ArticleTitle>[Estimating the basic reproduction number of COVID-19 in Wuhan, China].</ArticleTitle><Pagination><MedlinePgn>476-479</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3760/cma.j.cn112338-20200210-00086</ELocationID><Abstract><AbstractText><b>Objective:</b> The number of confirmed and suspected cases of the COVID-19 in Hubei province is still increasing. However, the estimations of the basic reproduction number of COVID-19 varied greatly across studies. The objectives of this study are 1) to estimate the basic reproduction number (<i>R(0)</i>) of COVID-19 reflecting the infectiousness of the virus and 2) to assess the effectiveness of a range of controlling intervention. <b>Method:</b> The reported number of daily confirmed cases from January 17 to February 8, 2020 in Hubei province were collected and used for model fit. Four methods, the exponential growth (EG), maximum likelihood estimation (ML), sequential Bayesian method (SB) and time dependent reproduction numbers (TD), were applied to estimate the <i>R(0)</i>. <b>Result:</b> Among the four methods, the EG method fitted the data best. The estimated <i>R(0)</i> was 3.49 (95% <i>CI</i>: 3.42-3.58) by using EG method. The <i>R(0)</i> was estimated to be 2.95 (95%<i>CI</i>: 2.86-3.03) after taking control measures. <b>Conclusion:</b> In the early stage of the epidemic, it is appropriate to estimate <i>R(0)</i> using the EG method. Meanwhile, timely and effective control measures were warranted to further reduce the spread of COVID-19.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Y</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>You</LastName><ForeName>X Y</ForeName><Initials>XY</Initials><AffiliationInfo><Affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Y J</ForeName><Initials>YJ</Initials><AffiliationInfo><Affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Peng</LastName><ForeName>L P</ForeName><Initials>LP</Initials><AffiliationInfo><Affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Du</LastName><ForeName>Z C</ForeName><Initials>ZC</Initials><AffiliationInfo><Affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gilmour</LastName><ForeName>S</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yoneoka</LastName><ForeName>D</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Graduate School of Public Health, St. Luke's International University, Tokyo, Japan 104-0045.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gu</LastName><ForeName>J</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hao</LastName><ForeName>C</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hao</LastName><ForeName>Y T</ForeName><Initials>YT</Initials><AffiliationInfo><Affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>J H</ForeName><Initials>JH</Initials><AffiliationInfo><Affiliation>School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou 510275, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>chi</Language><GrantList CompleteYN="Y"><Grant><GrantID>81803334, 71774178, 71974212, 81973150</GrantID><Agency>National Natural Science Foundation of China</Agency><Country></Country></Grant><Grant><GrantID>18-301</GrantID><Agency>Chinese Medical Board of USA</Agency><Country></Country></Grant><Grant><GrantID>2018ZX10715004</GrantID><Agency>National Science and Technology Major Project of China</Agency><Country></Country></Grant><Grant><GrantID>2017A020212006</GrantID><Agency>Science and Technology Planning Project of Guangdong Province</Agency><Country></Country></Grant><Grant><GrantID>201607010332, 201607010368</GrantID><Agency>Science and Technology Research Project of Guangzhou</Agency><Country></Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D004740">English Abstract</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>03</Month><Day>03</Day></ArticleDate></Article><MedlineJournalInfo><Country>China</Country><MedlineTA>Zhonghua Liu Xing Bing Xue Za Zhi</MedlineTA><NlmUniqueID>8208604</NlmUniqueID><ISSNLinking>0254-6450</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><OtherAbstract Type="Publisher" Language="chi"><AbstractText><b>目的：</b> 目前湖北省的新型冠状病毒肺炎（COVID-19）确诊和疑似病例的数量仍在增加。国内外多个团队对疫情发展进行了模型预测，但结论并不统一。因此，开展本次疫情的预测模型研究、评估COVID-19的基本再生数（basic reproduction number，<i>R(0)</i>），对于评估病毒的传播能力以及一系列控制措施的效果具有重要意义。 <b>方法：</b> 收集从湖北省2020年1月17日到2月8日期间每天报告的确诊病例数等数据，分别采用指数增长方法（exponential growth，EG）、极大似然法（maximum likelihood estimation，ML）、序贯贝叶斯方法（sequential Bayesian method，SB）和时间相关基本再生数（time dependent reproduction numbers，TD）估计<i>R(0)</i>值。 <b>结果：</b> 由观测病例数和4种方法预测的病例数的拟合情况可知，EG方法拟合效果最优。EG方法估计COVID-19湖北省<i>R(0)</i>的值为3.49（95%CI：3.42～3.58）。采取封城控制手段期间，EG方法估算<i>R(0)</i>值为2.95（95%CI：2.86～3.03）。 <b>结论：</b> 在传染病流行初期，适合采用EG方法估算<i>R(0)</i>。同时需要采取及时有效的控制措施，进一步降低COVID-19的传播速率。.</AbstractText></OtherAbstract><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Basic reproduction number</Keyword><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Transmission rate</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32125128</ArticleId><ArticleId IdType="doi">10.3760/cma.j.cn112338-20200210-00086</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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