Serveur d'exploration sur le confinement (PubMed)

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.

Infection Density and Epidemic Size of COVID-19 in China outside the Hubei province.

Identifieur interne : 002465 ( Main/Corpus ); précédent : 002464; suivant : 002466

Infection Density and Epidemic Size of COVID-19 in China outside the Hubei province.

Auteurs : Yukun Liu ; Jing Qin ; Yan Fan ; Yong Zhou ; Dean A. Follmann ; Chiung-Yu Huang

Source :

RBID : pubmed:32511453

Abstract

The novel coronavirus (COVID-19) has spread to almost all countries in the world, claiming more than 160,000 lives and sickening more than 2,400,000 people by April 21, 2020. There has been research showing that on average, each infected person spreads the infection to more than two persons. Therefore the majority of the population is at risk of infection if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown, as a significant proportion of infected individuals only exhibit mild symptoms or are even asymptomatic. A timely assessment of the evolving epidemic size is crucial for resource allocation and triage decisions. In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID-19 infected persons in China outside the Hubei province. We estimate the infection density among infected and show that the drastic control measures enforced throughout China following the lockdown of Wuhan City effectively slowed down the spread of the disease in two weeks. Our findings from China are expected to provide guidelines and enlightenment for surveillance and control activities of COVID-19 in other countries around the world.

DOI: 10.1101/2020.04.23.20074708
PubMed: 32511453
PubMed Central: PMC7239081

Links to Exploration step

pubmed:32511453

Le document en format XML

<record>
<TEI>
<teiHeader>
<fileDesc>
<titleStmt>
<title xml:lang="en">Infection Density and Epidemic Size of COVID-19 in China outside the Hubei province.</title>
<author>
<name sortKey="Liu, Yukun" sort="Liu, Yukun" uniqKey="Liu Y" first="Yukun" last="Liu">Yukun Liu</name>
<affiliation>
<nlm:affiliation>KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai 200262, China.</nlm:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Qin, Jing" sort="Qin, Jing" uniqKey="Qin J" first="Jing" last="Qin">Jing Qin</name>
<affiliation>
<nlm:affiliation>Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institute of Health, Rockville, Maryland 20852, United States.</nlm:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Fan, Yan" sort="Fan, Yan" uniqKey="Fan Y" first="Yan" last="Fan">Yan Fan</name>
<affiliation>
<nlm:affiliation>School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China.</nlm:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Zhou, Yong" sort="Zhou, Yong" uniqKey="Zhou Y" first="Yong" last="Zhou">Yong Zhou</name>
<affiliation>
<nlm:affiliation>KLATASDS-MOE, Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai 200062, China.</nlm:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Follmann, Dean A" sort="Follmann, Dean A" uniqKey="Follmann D" first="Dean A" last="Follmann">Dean A. Follmann</name>
<affiliation>
<nlm:affiliation>Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institute of Health, Rockville, Maryland 20852, United States.</nlm:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Huang, Chiung Yu" sort="Huang, Chiung Yu" uniqKey="Huang C" first="Chiung-Yu" last="Huang">Chiung-Yu Huang</name>
<affiliation>
<nlm:affiliation>Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, California, 94158, United States.</nlm:affiliation>
</affiliation>
</author>
</titleStmt>
<publicationStmt>
<idno type="wicri:source">PubMed</idno>
<date when="2020">2020</date>
<idno type="RBID">pubmed:32511453</idno>
<idno type="pmid">32511453</idno>
<idno type="doi">10.1101/2020.04.23.20074708</idno>
<idno type="pmc">PMC7239081</idno>
<idno type="wicri:Area/Main/Corpus">002465</idno>
<idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">002465</idno>
</publicationStmt>
<sourceDesc>
<biblStruct>
<analytic>
<title xml:lang="en">Infection Density and Epidemic Size of COVID-19 in China outside the Hubei province.</title>
<author>
<name sortKey="Liu, Yukun" sort="Liu, Yukun" uniqKey="Liu Y" first="Yukun" last="Liu">Yukun Liu</name>
<affiliation>
<nlm:affiliation>KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai 200262, China.</nlm:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Qin, Jing" sort="Qin, Jing" uniqKey="Qin J" first="Jing" last="Qin">Jing Qin</name>
<affiliation>
<nlm:affiliation>Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institute of Health, Rockville, Maryland 20852, United States.</nlm:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Fan, Yan" sort="Fan, Yan" uniqKey="Fan Y" first="Yan" last="Fan">Yan Fan</name>
<affiliation>
<nlm:affiliation>School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China.</nlm:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Zhou, Yong" sort="Zhou, Yong" uniqKey="Zhou Y" first="Yong" last="Zhou">Yong Zhou</name>
<affiliation>
<nlm:affiliation>KLATASDS-MOE, Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai 200062, China.</nlm:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Follmann, Dean A" sort="Follmann, Dean A" uniqKey="Follmann D" first="Dean A" last="Follmann">Dean A. Follmann</name>
<affiliation>
<nlm:affiliation>Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institute of Health, Rockville, Maryland 20852, United States.</nlm:affiliation>
</affiliation>
</author>
<author>
<name sortKey="Huang, Chiung Yu" sort="Huang, Chiung Yu" uniqKey="Huang C" first="Chiung-Yu" last="Huang">Chiung-Yu Huang</name>
<affiliation>
<nlm:affiliation>Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, California, 94158, United States.</nlm:affiliation>
</affiliation>
</author>
</analytic>
<series>
<title level="j">medRxiv : the preprint server for health sciences</title>
<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">The novel coronavirus (COVID-19) has spread to almost all countries in the world, claiming more than 160,000 lives and sickening more than 2,400,000 people by April 21, 2020. There has been research showing that on average, each infected person spreads the infection to more than two persons. Therefore the majority of the population is at risk of infection if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown, as a significant proportion of infected individuals only exhibit mild symptoms or are even asymptomatic. A timely assessment of the evolving epidemic size is crucial for resource allocation and triage decisions. In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID-19 infected persons in China outside the Hubei province. We estimate the infection density among infected and show that the drastic control measures enforced throughout China following the lockdown of Wuhan City effectively slowed down the spread of the disease in two weeks. Our findings from China are expected to provide guidelines and enlightenment for surveillance and control activities of COVID-19 in other countries around the world.</div>
</front>
</TEI>
<pubmed>
<MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM">
<PMID Version="1">32511453</PMID>
<DateRevised>
<Year>2020</Year>
<Month>09</Month>
<Day>28</Day>
</DateRevised>
<Article PubModel="Electronic">
<Journal>
<JournalIssue CitedMedium="Internet">
<PubDate>
<Year>2020</Year>
<Month>Apr</Month>
<Day>28</Day>
</PubDate>
</JournalIssue>
<Title>medRxiv : the preprint server for health sciences</Title>
<ISOAbbreviation>medRxiv</ISOAbbreviation>
</Journal>
<ArticleTitle>Infection Density and Epidemic Size of COVID-19 in China outside the Hubei province.</ArticleTitle>
<ELocationID EIdType="pii" ValidYN="Y">2020.04.23.20074708</ELocationID>
<ELocationID EIdType="doi" ValidYN="Y">10.1101/2020.04.23.20074708</ELocationID>
<Abstract>
<AbstractText>The novel coronavirus (COVID-19) has spread to almost all countries in the world, claiming more than 160,000 lives and sickening more than 2,400,000 people by April 21, 2020. There has been research showing that on average, each infected person spreads the infection to more than two persons. Therefore the majority of the population is at risk of infection if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown, as a significant proportion of infected individuals only exhibit mild symptoms or are even asymptomatic. A timely assessment of the evolving epidemic size is crucial for resource allocation and triage decisions. In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID-19 infected persons in China outside the Hubei province. We estimate the infection density among infected and show that the drastic control measures enforced throughout China following the lockdown of Wuhan City effectively slowed down the spread of the disease in two weeks. Our findings from China are expected to provide guidelines and enlightenment for surveillance and control activities of COVID-19 in other countries around the world.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y">
<Author ValidYN="Y">
<LastName>Liu</LastName>
<ForeName>Yukun</ForeName>
<Initials>Y</Initials>
<AffiliationInfo>
<Affiliation>KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai 200262, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Qin</LastName>
<ForeName>Jing</ForeName>
<Initials>J</Initials>
<AffiliationInfo>
<Affiliation>Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institute of Health, Rockville, Maryland 20852, United States.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Fan</LastName>
<ForeName>Yan</ForeName>
<Initials>Y</Initials>
<AffiliationInfo>
<Affiliation>School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Zhou</LastName>
<ForeName>Yong</ForeName>
<Initials>Y</Initials>
<AffiliationInfo>
<Affiliation>KLATASDS-MOE, Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai 200062, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Follmann</LastName>
<ForeName>Dean A</ForeName>
<Initials>DA</Initials>
<AffiliationInfo>
<Affiliation>Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institute of Health, Rockville, Maryland 20852, United States.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Huang</LastName>
<ForeName>Chiung-Yu</ForeName>
<Initials>CY</Initials>
<AffiliationInfo>
<Affiliation>Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, California, 94158, United States.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList>
<PublicationType UI="D000076942">Preprint</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic">
<Year>2020</Year>
<Month>04</Month>
<Day>28</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo>
<Country>United States</Country>
<MedlineTA>medRxiv</MedlineTA>
<NlmUniqueID>101767986</NlmUniqueID>
</MedlineJournalInfo>
<KeywordList Owner="NOTNLM">
<Keyword MajorTopicYN="N">COVID-19 epidemic</Keyword>
<Keyword MajorTopicYN="N">back calculation</Keyword>
<Keyword MajorTopicYN="N">incubation period</Keyword>
<Keyword MajorTopicYN="N">infection time</Keyword>
</KeywordList>
</MedlineCitation>
<PubmedData>
<History>
<PubMedPubDate PubStatus="entrez">
<Year>2020</Year>
<Month>6</Month>
<Day>9</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed">
<Year>2020</Year>
<Month>6</Month>
<Day>9</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline">
<Year>2020</Year>
<Month>6</Month>
<Day>9</Day>
<Hour>6</Hour>
<Minute>1</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>epublish</PublicationStatus>
<ArticleIdList>
<ArticleId IdType="pubmed">32511453</ArticleId>
<ArticleId IdType="doi">10.1101/2020.04.23.20074708</ArticleId>
<ArticleId IdType="pmc">PMC7239081</ArticleId>
</ArticleIdList>
<ReferenceList>
<Reference>
<Citation>N Engl J Med. 2020 Mar 26;382(13):1199-1207</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">31995857</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>Int J Infect Dis. 2020 Mar;92:214-217</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">32007643</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>Sci Adv. 2020 Aug 14;6(33):eabc1202</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">32851189</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>Lancet. 1986 Dec 6;2(8519):1320-2</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">2878184</ArticleId>
</ArticleIdList>
</Reference>
<Reference>
<Citation>Philos Trans R Soc Lond B Biol Sci. 1997 Jul 29;352(1355):803-38</Citation>
<ArticleIdList>
<ArticleId IdType="pubmed">9279898</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
</PubmedData>
</pubmed>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Sante/explor/LockdownV1/Data/Main/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 002465 | SxmlIndent | more

Ou

HfdSelect -h $EXPLOR_AREA/Data/Main/Corpus/biblio.hfd -nk 002465 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Sante
   |area=    LockdownV1
   |flux=    Main
   |étape=   Corpus
   |type=    RBID
   |clé=     pubmed:32511453
   |texte=   Infection Density and Epidemic Size of COVID-19 in China outside the Hubei province.
}}

Pour générer des pages wiki

HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Corpus/RBID.i   -Sk "pubmed:32511453" \
       | HfdSelect -Kh $EXPLOR_AREA/Data/Main/Corpus/biblio.hfd   \
       | NlmPubMed2Wicri -a LockdownV1 

Wicri

This area was generated with Dilib version V0.6.38.
Data generation: Sun Jan 31 08:28:27 2021. Site generation: Sun Jan 31 08:33:49 2021