[Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak].
Identifieur interne : 000D35 ( Ncbi/Merge ); précédent : 000D34; suivant : 000D36[Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak].
Auteurs : S Y Tang [République populaire de Chine] ; Y N Xiao [République populaire de Chine] ; Z H Peng [République populaire de Chine] ; H B Shen [République populaire de Chine]Source :
- Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi [ 0254-6450 ] ; 2020.
Abstract
Since December 2019, the outbreak of COVID-19 in Wuhan has spread rapidly due to population movement during the Spring Festival holidays. Since January 23rd, 2020, the strategies of containment and contact tracing followed by quarantine and isolation has been implemented extensively in mainland China, and the rates of detection and confirmation have been continuously increased, which have effectively suppressed the rapid spread of the epidemic. In the early stage of the outbreak of COVID-19, it is of great practical significance to analyze the transmission risk of the epidemic and evaluate the effectiveness and timeliness of prevention and control strategies by using mathematical models and combining with a small amount of real-time updated multi-source data. On the basis of our previous research, we systematically introduce how to establish the transmission dynamic models in line with current Chinese prevention and control strategies step by step, according to the different epidemic stages and the improvement of the data. By summarized our modelling and assessing ideas, the model formulations vary from autonomous to non-autonomous dynamic systems, the risk assessment index changes from the basic regeneration number to the effective regeneration number, and the epidemic development and assessment evolve from the early SEIHR transmission model-based dynamics to the recent dynamics which are mainly associated with the variation of the isolated and suspected population sizes.
DOI: 10.3760/cma.j.cn112338-20200216-00107
PubMed: 32129581
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Jiaotong University, Xi'an 710049, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Center for the Intersection of Mathematics and Life Sciences, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049</wicri:regionArea><wicri:noRegion>Xi'an 710049</wicri:noRegion></affiliation></author><author><name sortKey="Peng, Z H" sort="Peng, Z H" uniqKey="Peng Z" first="Z H" last="Peng">Z H Peng</name><affiliation wicri:level="1"><nlm:affiliation>Center for Global Health, Nanjing Medical University, Nanjing 210029, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Center for Global Health, Nanjing Medical University, Nanjing 210029</wicri:regionArea><wicri:noRegion>Nanjing 210029</wicri:noRegion></affiliation></author><author><name sortKey="Shen, H B" sort="Shen, H B" uniqKey="Shen H" first="H B" last="Shen">H B Shen</name><affiliation wicri:level="1"><nlm:affiliation>School of Public Health, Nanjing Medical University, Nanjing 211166, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Public Health, Nanjing Medical University, Nanjing 211166</wicri:regionArea><wicri:noRegion>Nanjing 211166</wicri:noRegion></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">Since December 2019, the outbreak of COVID-19 in Wuhan has spread rapidly due to population movement during the Spring Festival holidays. Since January 23rd, 2020, the strategies of containment and contact tracing followed by quarantine and isolation has been implemented extensively in mainland China, and the rates of detection and confirmation have been continuously increased, which have effectively suppressed the rapid spread of the epidemic. In the early stage of the outbreak of COVID-19, it is of great practical significance to analyze the transmission risk of the epidemic and evaluate the effectiveness and timeliness of prevention and control strategies by using mathematical models and combining with a small amount of real-time updated multi-source data. On the basis of our previous research, we systematically introduce how to establish the transmission dynamic models in line with current Chinese prevention and control strategies step by step, according to the different epidemic stages and the improvement of the data. By summarized our modelling and assessing ideas, the model formulations vary from autonomous to non-autonomous dynamic systems, the risk assessment index changes from the basic regeneration number to the effective regeneration number, and the epidemic development and assessment evolve from the early SEIHR transmission model-based dynamics to the recent dynamics which are mainly associated with the variation of the isolated and suspected population sizes.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32129581</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>04</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>04</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>[Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak].</ArticleTitle><Pagination><MedlinePgn>480-484</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3760/cma.j.cn112338-20200216-00107</ELocationID><Abstract><AbstractText>Since December 2019, the outbreak of COVID-19 in Wuhan has spread rapidly due to population movement during the Spring Festival holidays. Since January 23rd, 2020, the strategies of containment and contact tracing followed by quarantine and isolation has been implemented extensively in mainland China, and the rates of detection and confirmation have been continuously increased, which have effectively suppressed the rapid spread of the epidemic. In the early stage of the outbreak of COVID-19, it is of great practical significance to analyze the transmission risk of the epidemic and evaluate the effectiveness and timeliness of prevention and control strategies by using mathematical models and combining with a small amount of real-time updated multi-source data. On the basis of our previous research, we systematically introduce how to establish the transmission dynamic models in line with current Chinese prevention and control strategies step by step, according to the different epidemic stages and the improvement of the data. By summarized our modelling and assessing ideas, the model formulations vary from autonomous to non-autonomous dynamic systems, the risk assessment index changes from the basic regeneration number to the effective regeneration number, and the epidemic development and assessment evolve from the early SEIHR transmission model-based dynamics to the recent dynamics which are mainly associated with the variation of the isolated and suspected population sizes.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Tang</LastName><ForeName>S Y</ForeName><Initials>SY</Initials><AffiliationInfo><Affiliation>School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xiao</LastName><ForeName>Y N</ForeName><Initials>YN</Initials><AffiliationInfo><Affiliation>Center for the Intersection of Mathematics and Life Sciences, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Peng</LastName><ForeName>Z H</ForeName><Initials>ZH</Initials><AffiliationInfo><Affiliation>Center for Global Health, Nanjing Medical University, Nanjing 210029, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shen</LastName><ForeName>H B</ForeName><Initials>HB</Initials><AffiliationInfo><Affiliation>School of Public Health, Nanjing Medical University, Nanjing 211166, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>chi</Language><GrantList CompleteYN="Y"><Grant><GrantID>81673275</GrantID><Agency>Natural Science Foundation of China</Agency><Country></Country></Grant><Grant><GrantID>2018ZX10715002-004-002, 2018ZX10713001-001</GrantID><Agency>National Science and Technology Major Project 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>04</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>自2019年12月以来，武汉爆发的COVID-19疫情由于春节人口流动快速蔓延，自2020年1月23日起全国大范围实施围堵缓疫策略，并不断提高检测和检出率，有效地抑制了疫情快速蔓延的趋势。在COVID-19爆发的早期，如何利用数学模型并结合少量和实时更新的多源数据，对疫情进行风险分析，评估防控策略的有效性和时效性等具有非常重要的现实意义。本研究将结合前期研究基础，系统介绍如何依据疫情发展的不同阶段和数据的完善，逐步建立符合我国防控策略的COVID-19传播动力学模型，给出模型由自治到非自治，风险评估指标由基本再生数到有效再生数，疫情发展与评估由早期的SEIHR传播动力学决定到最终取决于隔离人群和疑似人群规模的演变等的重要研究思路。.</AbstractText></OtherAbstract><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Dynamical model</Keyword><Keyword MajorTopicYN="N">Reproductive number</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32129581</ArticleId><ArticleId IdType="doi">10.3760/cma.j.cn112338-20200216-00107</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Tang, S Y" sort="Tang, S Y" uniqKey="Tang S" first="S Y" last="Tang">S Y Tang</name></noRegion><name sortKey="Peng, Z H" sort="Peng, Z H" uniqKey="Peng Z" first="Z H" last="Peng">Z H Peng</name><name sortKey="Shen, H B" sort="Shen, H B" uniqKey="Shen H" first="H B" last="Shen">H B Shen</name><name sortKey="Xiao, Y N" sort="Xiao, Y N" uniqKey="Xiao Y" first="Y N" last="Xiao">Y N Xiao</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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