[Study on assessing early epidemiological parameters of coronavirus disease epidemic in China].
Identifieur interne : 000391 ( PubMed/Corpus ); précédent : 000390; suivant : 000392[Study on assessing early epidemiological parameters of coronavirus disease epidemic in China].
Auteurs : Q Q Song ; H. Zhao ; L Q Fang ; W. Liu ; C. Zheng ; Y. ZhangSource :
- Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi [ 0254-6450 ] ; 2020.
Abstract
Objective: To study the early dynamics of the epidemic of coronavirus disease (COVID-19) in China from 15 to 31 January, 2020, and estimate the corresponding epidemiological parameters (incubation period, generation interval and basic reproduction number) of the epidemic. Methods: By means of Weibull, Gamma and Lognormal distributions methods, we estimated the probability distribution of the incubation period and generation interval data obtained from the reported COVID-19 cases. Moreover, the AIC criterion was used to determine the optimal distribution. Considering the epidemic is ongoing, the exponential growth model was used to fit the incidence data of COVID-19 from 10 to 31 January, 2020, and exponential growth method, maximum likelihood method and SEIR model were used to estimate the basic reproduction number. Results: Early COVID-19 cases kept an increase in exponential growth manner before 26 January, 2020, then the increase trend became slower. The average incubation period was 5.01 (95%CI: 4.31-5.69) days; the average generation interval was 6.03 (95%CI: 5.20-6.91) days. The basic reproduction number was estimated to be 3.74 (95%CI: 3.63-3.87), 3.16 (95%CI: 2.90-3.43), and 3.91 (95%CI: 3.71-4.11) by three methods, respectively. Conclusions: The Gamma distribution fits both the generation interval and incubation period best, and the mean value of generation interval is 1.02 day longer than that of incubation period. The relatively high basic reproduction number indicates that the epidemic is still serious; Based on our analysis, the turning point of the epidemic would be seen on 26 January, the growth rate would be lower afterwards.
DOI: 10.3760/cma.j.cn112338-20200205-00069
PubMed: 32113196
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">[Study on assessing early epidemiological parameters of coronavirus disease epidemic in China].</title><author><name sortKey="Song, Q Q" sort="Song, Q Q" uniqKey="Song Q" first="Q Q" last="Song">Q Q Song</name><affiliation><nlm:affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhao, H" sort="Zhao, H" uniqKey="Zhao H" first="H" last="Zhao">H. Zhao</name><affiliation><nlm:affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</nlm:affiliation></affiliation></author><author><name sortKey="Fang, L Q" sort="Fang, L Q" uniqKey="Fang L" first="L Q" last="Fang">L Q Fang</name><affiliation><nlm:affiliation>Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China.</nlm:affiliation></affiliation></author><author><name sortKey="Liu, W" sort="Liu, W" uniqKey="Liu W" first="W" last="Liu">W. Liu</name><affiliation><nlm:affiliation>Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zheng, C" sort="Zheng, C" uniqKey="Zheng C" first="C" last="Zheng">C. Zheng</name><affiliation><nlm:affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Y" sort="Zhang, Y" uniqKey="Zhang Y" first="Y" last="Zhang">Y. Zhang</name><affiliation><nlm:affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32113196</idno><idno type="pmid">32113196</idno><idno type="doi">10.3760/cma.j.cn112338-20200205-00069</idno><idno type="wicri:Area/PubMed/Corpus">000391</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000391</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">[Study on assessing early epidemiological parameters of coronavirus disease epidemic in China].</title><author><name sortKey="Song, Q Q" sort="Song, Q Q" uniqKey="Song Q" first="Q Q" last="Song">Q Q Song</name><affiliation><nlm:affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhao, H" sort="Zhao, H" uniqKey="Zhao H" first="H" last="Zhao">H. Zhao</name><affiliation><nlm:affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</nlm:affiliation></affiliation></author><author><name sortKey="Fang, L Q" sort="Fang, L Q" uniqKey="Fang L" first="L Q" last="Fang">L Q Fang</name><affiliation><nlm:affiliation>Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China.</nlm:affiliation></affiliation></author><author><name sortKey="Liu, W" sort="Liu, W" uniqKey="Liu W" first="W" last="Liu">W. Liu</name><affiliation><nlm:affiliation>Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zheng, C" sort="Zheng, C" uniqKey="Zheng C" first="C" last="Zheng">C. Zheng</name><affiliation><nlm:affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Y" sort="Zhang, Y" uniqKey="Zhang Y" first="Y" last="Zhang">Y. Zhang</name><affiliation><nlm:affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</nlm:affiliation></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> To study the early dynamics of the epidemic of coronavirus disease (COVID-19) in China from 15 to 31 January, 2020, and estimate the corresponding epidemiological parameters (incubation period, generation interval and basic reproduction number) of the epidemic. <b>Methods:</b> By means of Weibull, Gamma and Lognormal distributions methods, we estimated the probability distribution of the incubation period and generation interval data obtained from the reported COVID-19 cases. Moreover, the AIC criterion was used to determine the optimal distribution. Considering the epidemic is ongoing, the exponential growth model was used to fit the incidence data of COVID-19 from 10 to 31 January, 2020, and exponential growth method, maximum likelihood method and SEIR model were used to estimate the basic reproduction number. <b>Results:</b> Early COVID-19 cases kept an increase in exponential growth manner before 26 January, 2020, then the increase trend became slower. The average incubation period was 5.01 (95%<i>CI</i>: 4.31-5.69) days; the average generation interval was 6.03 (95%<i>CI</i>: 5.20-6.91) days. The basic reproduction number was estimated to be 3.74 (95%<i>CI</i>: 3.63-3.87), 3.16 (95%<i>CI</i>: 2.90-3.43), and 3.91 (95%<i>CI</i>: 3.71-4.11) by three methods, respectively. <b>Conclusions:</b> The Gamma distribution fits both the generation interval and incubation period best, and the mean value of generation interval is 1.02 day longer than that of incubation period. The relatively high basic reproduction number indicates that the epidemic is still serious; Based on our analysis, the turning point of the epidemic would be seen on 26 January, the growth rate would be lower afterwards.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32113196</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>01</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>01</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>[Study on assessing early epidemiological parameters of coronavirus disease epidemic in China].</ArticleTitle><Pagination><MedlinePgn>461-465</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3760/cma.j.cn112338-20200205-00069</ELocationID><Abstract><AbstractText><b>Objective:</b> To study the early dynamics of the epidemic of coronavirus disease (COVID-19) in China from 15 to 31 January, 2020, and estimate the corresponding epidemiological parameters (incubation period, generation interval and basic reproduction number) of the epidemic. <b>Methods:</b> By means of Weibull, Gamma and Lognormal distributions methods, we estimated the probability distribution of the incubation period and generation interval data obtained from the reported COVID-19 cases. Moreover, the AIC criterion was used to determine the optimal distribution. Considering the epidemic is ongoing, the exponential growth model was used to fit the incidence data of COVID-19 from 10 to 31 January, 2020, and exponential growth method, maximum likelihood method and SEIR model were used to estimate the basic reproduction number. <b>Results:</b> Early COVID-19 cases kept an increase in exponential growth manner before 26 January, 2020, then the increase trend became slower. The average incubation period was 5.01 (95%<i>CI</i>: 4.31-5.69) days; the average generation interval was 6.03 (95%<i>CI</i>: 5.20-6.91) days. The basic reproduction number was estimated to be 3.74 (95%<i>CI</i>: 3.63-3.87), 3.16 (95%<i>CI</i>: 2.90-3.43), and 3.91 (95%<i>CI</i>: 3.71-4.11) by three methods, respectively. <b>Conclusions:</b> The Gamma distribution fits both the generation interval and incubation period best, and the mean value of generation interval is 1.02 day longer than that of incubation period. The relatively high basic reproduction number indicates that the epidemic is still serious; Based on our analysis, the turning point of the epidemic would be seen on 26 January, the growth rate would be lower afterwards.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Song</LastName><ForeName>Q Q</ForeName><Initials>QQ</Initials><AffiliationInfo><Affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhao</LastName><ForeName>H</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fang</LastName><ForeName>L Q</ForeName><Initials>LQ</Initials><AffiliationInfo><Affiliation>Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>W</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zheng</LastName><ForeName>C</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Y</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>chi</Language><GrantList CompleteYN="Y"><Grant><GrantID>11501035</GrantID><Agency>National Natural Science Foundation of China</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>01</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> 研究截至2020年1月31日新型冠状病毒肺炎疫情的早期流行动态，估计该疫情的基本再生数（<i>R(0)</i>）、潜伏期和世代间隔等流行病学参数。 <b>方法：</b> 使用威布尔、伽马和对数正态分布拟合从报告病例信息中获取的潜伏期和世代间隔数据的概率分布，采用Akaike信息准则确定最优模型。考虑到疫情还在流行中，应用指数增长模型拟合了2020年1月15－31日的疫情数据，并利用指数增长法、最大似然法和SEIR模型估计<i>R(0)</i>。 <b>结果：</b> 截至2020年1月26日早期疫情最遵循指数增长模式，随后增长趋势有所减缓。平均潜伏期为5.01（95%<i>CI</i>：4.31～5.69）d；平均世代间隔为6.03（95%<i>CI</i>：5.20～6.91）d。3种方法估计的<i>R(0)</i>分别为3.74（95%<i>CI</i>：3.63～3.87），3.16（95%<i>CI</i>：2.90～3.43）和3.91（95%<i>CI</i>：3.71～4.11）。 <b>结论：</b> 世代间隔和潜伏期都更符合伽马分布，世代间隔均值比潜伏期均值长1.02 d；<i>R(0)</i>较高，疫情形势较为严峻；1月26日是疫情动态的一个转折点，之后疫情增长趋势有所减缓。.</AbstractText></OtherAbstract><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Basic reproduction number</Keyword><Keyword MajorTopicYN="N">Generation interval</Keyword><Keyword MajorTopicYN="N">Incubation period</Keyword><Keyword MajorTopicYN="N">Novel coronavirus</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32113196</ArticleId><ArticleId IdType="doi">10.3760/cma.j.cn112338-20200205-00069</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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