CovidV2, an2020, Analysis, bibRecord, 000045

[Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak].

Identifieur interne : 000045 ( an2020/Analysis ); précédent : 000044; suivant : 000046

[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.

RBID : pubmed:32129581

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

Affiliations:

République populaire de Chine

Links to Exploration step

pubmed:32129581

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">[Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak].</title>
<author><name sortKey="Tang, S Y" sort="Tang, S Y" uniqKey="Tang S" first="S Y" last="Tang">S Y Tang</name>
<affiliation wicri:level="1"><nlm:affiliation>School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119</wicri:regionArea>
<wicri:noRegion>Xi'an 710119</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Xiao, Y N" sort="Xiao, Y N" uniqKey="Xiao Y" first="Y N" last="Xiao">Y N Xiao</name>
<affiliation wicri:level="1"><nlm:affiliation>Center for the Intersection of Mathematics and Life Sciences, School of Mathematics and Statistics, Xi'an 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>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2020">2020</date>
<idno type="RBID">pubmed:32129581</idno>
<idno type="pmid">32129581</idno>
<idno type="doi">10.3760/cma.j.cn112338-20200216-00107</idno>
<idno type="wicri:Area/PubMed/Corpus">000842</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000842</idno>
<idno type="wicri:Area/PubMed/Curation">000842</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000842</idno>
<idno type="wicri:Area/PubMed/Checkpoint">000038</idno>
<idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000038</idno>
<idno type="wicri:Area/Ncbi/Merge">000D35</idno>
<idno type="wicri:Area/Ncbi/Curation">000D35</idno>
<idno type="wicri:Area/Ncbi/Checkpoint">000D35</idno>
<idno type="wicri:doubleKey">0254-6450:2020:Tang S:prediction:modeling:with</idno>
<idno type="wicri:Area/Main/Merge">000045</idno>
<idno type="wicri:Area/Main/Curation">000045</idno>
<idno type="wicri:Area/Main/Exploration">000045</idno>
<idno type="wicri:Area/an2020/Extraction">000045</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">[Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak].</title>
<author><name sortKey="Tang, S Y" sort="Tang, S Y" uniqKey="Tang S" first="S Y" last="Tang">S Y Tang</name>
<affiliation wicri:level="1"><nlm:affiliation>School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>School of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710119</wicri:regionArea>
<wicri:noRegion>Xi'an 710119</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Xiao, Y N" sort="Xiao, Y N" uniqKey="Xiao Y" first="Y N" last="Xiao">Y N Xiao</name>
<affiliation wicri:level="1"><nlm:affiliation>Center for the Intersection of Mathematics and Life Sciences, School of Mathematics and Statistics, Xi'an 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>
<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)

EXPLOR_STEP=$WICRI_ROOT/Wicri/Sante/explor/CovidV2/Data/an2020/Analysis

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000045 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/an2020/Analysis/biblio.hfd -nk 000045 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Wicri/Sante
   |area=    CovidV2
   |flux=    an2020
   |étape=   Analysis
   |type=    RBID
   |clé=     pubmed:32129581
   |texte=   [Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak].
}}

Pour générer des pages wiki

HfdIndexSelect -h $EXPLOR_AREA/Data/an2020/Analysis/RBID.i   -Sk "pubmed:32129581" \
       | HfdSelect -Kh $EXPLOR_AREA/Data/an2020/Analysis/biblio.hfd   \
       | NlmPubMed2Wicri -a CovidV2

This area was generated with Dilib version V0.6.33.
Data generation: Sat Mar 28 17:51:24 2020. Site generation: Sun Jan 31 15:35:48 2021

Serveur d'exploration Covid (26 mars)

[Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak].

[Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak].

Source :

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki

	Serveur d'exploration Covid (26 mars)
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.