[Fitting and forecasting the trend of COVID-19 by SEIR(+ CAQ) dynamic model].
Identifieur interne : 000389 ( PubMed/Curation ); précédent : 000388; suivant : 000390[Fitting and forecasting the trend of COVID-19 by SEIR(+ CAQ) dynamic model].
Auteurs : Y Y Wei [République populaire de Chine] ; Z Z Lu [République populaire de Chine] ; Z C Du [République populaire de Chine] ; Z J Zhang [République populaire de Chine] ; Y. Zhao [République populaire de Chine] ; S P Shen [République populaire de Chine] ; B. Wang [République populaire de Chine] ; Y T Hao [République populaire de Chine] ; F. Chen [République populaire de Chine]Source :
- Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi [ 0254-6450 ] ; 2020.
Abstract
Objectives: Fitting and forecasting the trend of COVID-19 epidemics. Methods: Based on SEIR dynamic model, considering the COVID-19 transmission mechanism, infection spectrum and prevention and control procedures, we developed SEIR(+ CAQ) dynamic model to fit the frequencies of laboratory confirmed cases obtained from the government official websites. The data from January 20, 2020 to February 7, 2020 were used to fit the model, while the left data between February 8-12 were used to evaluate the quality of forecasting. Results: According to the cumulative number of confirmed cases between January 29 to February 7, the fitting bias of SEIR(+ CAQ) model for overall China (except for cases of Hubei province), Hubei province (except for cases of Wuhan city) and Wuhan city was less than 5%. For the data of subsequent 5 days between February 8 to 12, which were not included in the model fitting, the prediction biases were less than 10%. Regardless of the cases diagnosed by clinical examines, the numbers of daily emerging cases of China (Hubei province not included), Hubei Province (Wuhan city not included) and Wuhan city reached the peak in the early February. Under the current strength of prevention and control, the total number of laboratory- confirmed cases in overall China will reach 80 417 till February 29, 2020, respectively. Conclusions: The proposed SEIR(+ CAQ) dynamic model fits and forecasts the trend of novel coronavirus pneumonia well and provides evidence for decision making.
DOI: 10.3760/cma.j.cn112338-20200216-00106
PubMed: 32113198
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Zhao</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea></affiliation></author><author><name sortKey="Shen, S P" sort="Shen, S P" uniqKey="Shen S" first="S P" last="Shen">S P Shen</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea></affiliation></author><author><name sortKey="Wang, B" sort="Wang, B" uniqKey="Wang B" first="B" last="Wang">B. Wang</name><affiliation wicri:level="1"><nlm:affiliation>Meinian Institute of Health, Beijing 100191, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Meinian Institute of Health, Beijing 100191</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>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080</wicri:regionArea></affiliation></author><author><name sortKey="Chen, F" sort="Chen, F" uniqKey="Chen F" first="F" last="Chen">F. Chen</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32113198</idno><idno type="pmid">32113198</idno><idno type="doi">10.3760/cma.j.cn112338-20200216-00106</idno><idno type="wicri:Area/PubMed/Corpus">000389</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000389</idno><idno type="wicri:Area/PubMed/Curation">000389</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000389</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">[Fitting and forecasting the trend of COVID-19 by SEIR(+ CAQ) dynamic model].</title><author><name sortKey="Wei, Y Y" sort="Wei, Y Y" uniqKey="Wei Y" first="Y Y" last="Wei">Y Y Wei</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea></affiliation></author><author><name sortKey="Lu, Z Z" sort="Lu, Z Z" uniqKey="Lu Z" first="Z Z" last="Lu">Z Z Lu</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</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>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080</wicri:regionArea></affiliation></author><author><name sortKey="Zhang, Z J" sort="Zhang, Z J" uniqKey="Zhang Z" first="Z J" last="Zhang">Z J Zhang</name><affiliation wicri:level="1"><nlm:affiliation>Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032</wicri:regionArea></affiliation></author><author><name sortKey="Zhao, Y" sort="Zhao, Y" uniqKey="Zhao Y" first="Y" last="Zhao">Y. Zhao</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea></affiliation></author><author><name sortKey="Shen, S P" sort="Shen, S P" uniqKey="Shen S" first="S P" last="Shen">S P Shen</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea></affiliation></author><author><name sortKey="Wang, B" sort="Wang, B" uniqKey="Wang B" first="B" last="Wang">B. Wang</name><affiliation wicri:level="1"><nlm:affiliation>Meinian Institute of Health, Beijing 100191, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Meinian Institute of Health, Beijing 100191</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>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080</wicri:regionArea></affiliation></author><author><name sortKey="Chen, F" sort="Chen, F" uniqKey="Chen F" first="F" last="Chen">F. Chen</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166</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>Objectives:</b> Fitting and forecasting the trend of COVID-19 epidemics. <b>Methods:</b> Based on SEIR dynamic model, considering the COVID-19 transmission mechanism, infection spectrum and prevention and control procedures, we developed SEIR(+ CAQ) dynamic model to fit the frequencies of laboratory confirmed cases obtained from the government official websites. The data from January 20, 2020 to February 7, 2020 were used to fit the model, while the left data between February 8-12 were used to evaluate the quality of forecasting. <b>Results:</b> According to the cumulative number of confirmed cases between January 29 to February 7, the fitting bias of SEIR(+ CAQ) model for overall China (except for cases of Hubei province), Hubei province (except for cases of Wuhan city) and Wuhan city was less than 5%. For the data of subsequent 5 days between February 8 to 12, which were not included in the model fitting, the prediction biases were less than 10%. Regardless of the cases diagnosed by clinical examines, the numbers of daily emerging cases of China (Hubei province not included), Hubei Province (Wuhan city not included) and Wuhan city reached the peak in the early February. Under the current strength of prevention and control, the total number of laboratory- confirmed cases in overall China will reach 80 417 till February 29, 2020, respectively. <b>Conclusions:</b> The proposed SEIR(+ CAQ) dynamic model fits and forecasts the trend of novel coronavirus pneumonia well and provides evidence for decision making.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32113198</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>[Fitting and forecasting the trend of COVID-19 by SEIR(+ CAQ) dynamic model].</ArticleTitle><Pagination><MedlinePgn>470-475</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3760/cma.j.cn112338-20200216-00106</ELocationID><Abstract><AbstractText><b>Objectives:</b> Fitting and forecasting the trend of COVID-19 epidemics. <b>Methods:</b> Based on SEIR dynamic model, considering the COVID-19 transmission mechanism, infection spectrum and prevention and control procedures, we developed SEIR(+ CAQ) dynamic model to fit the frequencies of laboratory confirmed cases obtained from the government official websites. The data from January 20, 2020 to February 7, 2020 were used to fit the model, while the left data between February 8-12 were used to evaluate the quality of forecasting. <b>Results:</b> According to the cumulative number of confirmed cases between January 29 to February 7, the fitting bias of SEIR(+ CAQ) model for overall China (except for cases of Hubei province), Hubei province (except for cases of Wuhan city) and Wuhan city was less than 5%. For the data of subsequent 5 days between February 8 to 12, which were not included in the model fitting, the prediction biases were less than 10%. Regardless of the cases diagnosed by clinical examines, the numbers of daily emerging cases of China (Hubei province not included), Hubei Province (Wuhan city not included) and Wuhan city reached the peak in the early February. Under the current strength of prevention and control, the total number of laboratory- confirmed cases in overall China will reach 80 417 till February 29, 2020, respectively. <b>Conclusions:</b> The proposed SEIR(+ CAQ) dynamic model fits and forecasts the trend of novel coronavirus pneumonia well and provides evidence for decision making.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wei</LastName><ForeName>Y Y</ForeName><Initials>YY</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lu</LastName><ForeName>Z Z</ForeName><Initials>ZZ</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Du</LastName><ForeName>Z C</ForeName><Initials>ZC</Initials><AffiliationInfo><Affiliation>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Z J</ForeName><Initials>ZJ</Initials><AffiliationInfo><Affiliation>Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhao</LastName><ForeName>Y</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shen</LastName><ForeName>S P</ForeName><Initials>SP</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>B</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Meinian Institute of Health, Beijing 100191, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hao</LastName><ForeName>Y T</ForeName><Initials>YT</Initials><AffiliationInfo><Affiliation>Department of Medical Statistics, School of Public Health, Zhongshan University, Guangzhou 510080, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>F</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>chi</Language><GrantList CompleteYN="Y"><Grant><GrantID>81530088 , 81973142</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> 拟合并预测新型冠状病毒肺炎（COVID-19）疫情的发展趋势，为疫情防控提供科学依据。 <b>方法：</b> 基于SEIR动力学模型，考虑COVID-19的传播机制、感染谱、隔离措施等，建立SEIR(＋CAQ)传播动力学模型。基于官方公布的每日确诊病例数进行建模，利用1月20日至2月7日的报告疫情数据进行拟合。采用2月8－12日的数据评估预测效果，并进行疫情预测。 <b>结果：</b> SEIR(＋CAQ)模型对全国（湖北省除外）和湖北省（武汉市除外）的累计确诊病例数的过去10日拟合偏差<5%；未来5日预测偏差<10%，略有高估。全国（湖北省除外）和湖北省（武汉市除外）的每日新增确诊病例数已于2月1－2日达峰值；武汉市亦已于2月9日达到高峰。在当前防控措施不变的情况下，截至2月29日，预计全国累计确诊病例将达80 417例。预测结果尚未包含临床诊断病例。 <b>结论：</b> SEIR(＋CAQ)模型可用于COVID-19肺炎疫情趋势预测，为疫情防控决策和效果评价提供参考。.</AbstractText></OtherAbstract><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Epidemic forecasting</Keyword><Keyword MajorTopicYN="N">Novel coronavirus pneumonia</Keyword><Keyword MajorTopicYN="N">SEIR(+ CAQ) dynamic model</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">32113198</ArticleId><ArticleId IdType="doi">10.3760/cma.j.cn112338-20200216-00106</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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