CovidFranceV1, Main, Exploration, bibRecord, 000426

The effect of human mobility and control measures on the COVID-19 epidemic in China.

Identifieur interne : 000426 ( Main/Exploration ); précédent : 000425; suivant : 000427

The effect of human mobility and control measures on the COVID-19 epidemic in China.

Auteurs : Moritz U G. Kraemer [Royaume-Uni, États-Unis] ; Chia-Hung Yang [États-Unis] ; Bernardo Gutierrez [Royaume-Uni, Équateur] ; Chieh-Hsi Wu [Royaume-Uni] ; Brennan Klein [États-Unis] ; David M. Pigott [États-Unis] ; Louis Du Plessis [Royaume-Uni] ; Nuno R. Faria [Royaume-Uni] ; Ruoran Li [États-Unis] ; William P. Hanage [États-Unis] ; John S. Brownstein [États-Unis] ; Maylis Layan [France] ; Alessandro Vespignani [États-Unis, Italie] ; Huaiyu Tian [République populaire de Chine] ; Christopher Dye [Royaume-Uni] ; Oliver G. Pybus [Royaume-Uni] ; Samuel V. Scarpino [États-Unis]

Source :

Science (New York, N.Y.) [ 1095-9203 ] ; 2020.

RBID : pubmed:32213647

Descripteurs français

KwdFr :
- Analyse spatiale (MeSH), Betacoronavirus (MeSH), Chine (MeSH), Humains (MeSH), Infections à coronavirus (prévention et contrôle), Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Modèles linéaires (MeSH), Pandémies (prévention et contrôle), Pneumopathie virale (prévention et contrôle), Pneumopathie virale (transmission), Pneumopathie virale (épidémiologie), Répartition par sexe (MeSH), Répartition par âge (MeSH), Surveillance épidémiologique (MeSH), Voyage (statistiques et données numériques).
MESH :
- prévention et contrôle : Infections à coronavirus, Pandémies, Pneumopathie virale.
- statistiques et données numériques : Voyage.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Analyse spatiale, Betacoronavirus, Chine, Humains, Modèles linéaires, Répartition par sexe, Répartition par âge, Surveillance épidémiologique.
Wicri :
- geographic : République populaire de Chine.

English descriptors

KwdEn :
- Age Distribution (MeSH), Betacoronavirus (MeSH), China (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Coronavirus Infections (transmission), Epidemiological Monitoring (MeSH), Humans (MeSH), Linear Models (MeSH), Pandemics (prevention & control), Pneumonia, Viral (epidemiology), Pneumonia, Viral (prevention & control), Pneumonia, Viral (transmission), Sex Distribution (MeSH), Spatial Analysis (MeSH), Travel (statistics & numerical data).
MESH :
- geographic : China.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- prevention & control : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- statistics & numerical data : Travel.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- Age Distribution, Betacoronavirus, Epidemiological Monitoring, Humans, Linear Models, Sex Distribution, Spatial Analysis.

Abstract

The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.

DOI: 10.1126/science.abb4218
PubMed: 32213647
PubMed Central: PMC7146642

Affiliations:

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

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Le document en format XML

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<author><name sortKey="Hanage, William P" sort="Hanage, William P" uniqKey="Hanage W" first="William P" last="Hanage">William P. Hanage</name>
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<settlement type="city">Cambridge (Massachusetts)</settlement>
</placeName>
<orgName type="university">Université Harvard</orgName>
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<author><name sortKey="Layan, Maylis" sort="Layan, Maylis" uniqKey="Layan M" first="Maylis" last="Layan">Maylis Layan</name>
<affiliation wicri:level="3"><nlm:affiliation>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.</nlm:affiliation>
<country xml:lang="fr">France</country>
<wicri:regionArea>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris</wicri:regionArea>
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<region type="old region">Île-de-France</region>
<settlement type="city">Paris</settlement>
</placeName>
</affiliation>
<affiliation wicri:level="3"><nlm:affiliation>Sorbonne Université, Paris, France.</nlm:affiliation>
<country xml:lang="fr">France</country>
<wicri:regionArea>Sorbonne Université, Paris</wicri:regionArea>
<placeName><region type="region">Île-de-France</region>
<region type="old region">Île-de-France</region>
<settlement type="city">Paris</settlement>
</placeName>
</affiliation>
</author>
<author><name sortKey="Vespignani, Alessandro" sort="Vespignani, Alessandro" uniqKey="Vespignani A" first="Alessandro" last="Vespignani">Alessandro Vespignani</name>
<affiliation wicri:level="2"><nlm:affiliation>Network Science Institute, Northeastern University, Boston, MA, USA.</nlm:affiliation>
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<wicri:regionArea>Network Science Institute, Northeastern University, Boston, MA</wicri:regionArea>
<placeName><region type="state">Massachusetts</region>
</placeName>
</affiliation>
<affiliation wicri:level="3"><nlm:affiliation>ISI Foundation, Turin, Italy.</nlm:affiliation>
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<wicri:regionArea>ISI Foundation, Turin</wicri:regionArea>
<placeName><settlement type="city">Turin</settlement>
<region type="région" nuts="2">Piémont</region>
</placeName>
</affiliation>
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<author><name sortKey="Tian, Huaiyu" sort="Tian, Huaiyu" uniqKey="Tian H" first="Huaiyu" last="Tian">Huaiyu Tian</name>
<affiliation wicri:level="3"><nlm:affiliation>State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.</nlm:affiliation>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing</wicri:regionArea>
<placeName><settlement type="city">Pékin</settlement>
</placeName>
</affiliation>
</author>
<author><name sortKey="Dye, Christopher" sort="Dye, Christopher" uniqKey="Dye C" first="Christopher" last="Dye">Christopher Dye</name>
<affiliation wicri:level="4"><nlm:affiliation>Department of Zoology, University of Oxford, Oxford, UK.</nlm:affiliation>
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<region type="comté" nuts="2">Oxfordshire</region>
</placeName>
<orgName type="university">Université d'Oxford</orgName>
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<author><name sortKey="Pybus, Oliver G" sort="Pybus, Oliver G" uniqKey="Pybus O" first="Oliver G" last="Pybus">Oliver G. Pybus</name>
<affiliation wicri:level="4"><nlm:affiliation>Department of Zoology, University of Oxford, Oxford, UK. s.scarpino@northeastern.edu oliver.pybus@zoo.ox.ac.uk moritz.kraemer@zoo.ox.ac.uk.</nlm:affiliation>
<country xml:lang="fr">Royaume-Uni</country>
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<region type="comté" nuts="2">Oxfordshire</region>
</placeName>
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</affiliation>
<affiliation wicri:level="3"><nlm:affiliation>Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK.</nlm:affiliation>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>Department of Pathobiology and Population Sciences, The Royal Veterinary College, London</wicri:regionArea>
<placeName><settlement type="city">Londres</settlement>
<region type="country">Angleterre</region>
<region type="région" nuts="1">Grand Londres</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Scarpino, Samuel V" sort="Scarpino, Samuel V" uniqKey="Scarpino S" first="Samuel V" last="Scarpino">Samuel V. Scarpino</name>
<affiliation wicri:level="2"><nlm:affiliation>Network Science Institute, Northeastern University, Boston, MA, USA. s.scarpino@northeastern.edu oliver.pybus@zoo.ox.ac.uk moritz.kraemer@zoo.ox.ac.uk.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Network Science Institute, Northeastern University, Boston, MA</wicri:regionArea>
<placeName><region type="state">Massachusetts</region>
</placeName>
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<series><title level="j">Science (New York, N.Y.)</title>
<idno type="eISSN">1095-9203</idno>
<imprint><date when="2020" type="published">2020</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Age Distribution (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>China (MeSH)</term>
<term>Coronavirus Infections (epidemiology)</term>
<term>Coronavirus Infections (prevention & control)</term>
<term>Coronavirus Infections (transmission)</term>
<term>Epidemiological Monitoring (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Linear Models (MeSH)</term>
<term>Pandemics (prevention & control)</term>
<term>Pneumonia, Viral (epidemiology)</term>
<term>Pneumonia, Viral (prevention & control)</term>
<term>Pneumonia, Viral (transmission)</term>
<term>Sex Distribution (MeSH)</term>
<term>Spatial Analysis (MeSH)</term>
<term>Travel (statistics & numerical data)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Analyse spatiale (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>Chine (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Infections à coronavirus (prévention et contrôle)</term>
<term>Infections à coronavirus (transmission)</term>
<term>Infections à coronavirus (épidémiologie)</term>
<term>Modèles linéaires (MeSH)</term>
<term>Pandémies (prévention et contrôle)</term>
<term>Pneumopathie virale (prévention et contrôle)</term>
<term>Pneumopathie virale (transmission)</term>
<term>Pneumopathie virale (épidémiologie)</term>
<term>Répartition par sexe (MeSH)</term>
<term>Répartition par âge (MeSH)</term>
<term>Surveillance épidémiologique (MeSH)</term>
<term>Voyage (statistiques et données numériques)</term>
</keywords>
<keywords scheme="MESH" type="geographic" xml:lang="en"><term>China</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pandemics</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="prévention et contrôle" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pandémies</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Travel</term>
</keywords>
<keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Voyage</term>
</keywords>
<keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Age Distribution</term>
<term>Betacoronavirus</term>
<term>Epidemiological Monitoring</term>
<term>Humans</term>
<term>Linear Models</term>
<term>Sex Distribution</term>
<term>Spatial Analysis</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Analyse spatiale</term>
<term>Betacoronavirus</term>
<term>Chine</term>
<term>Humains</term>
<term>Modèles linéaires</term>
<term>Répartition par sexe</term>
<term>Répartition par âge</term>
<term>Surveillance épidémiologique</term>
</keywords>
<keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>République populaire de Chine</term>
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<front><div type="abstract" xml:lang="en">The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.</div>
</front>
</TEI>
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<DateCompleted><Year>2020</Year>
<Month>05</Month>
<Day>05</Day>
</DateCompleted>
<DateRevised><Year>2020</Year>
<Month>05</Month>
<Day>15</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1095-9203</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>368</Volume>
<Issue>6490</Issue>
<PubDate><Year>2020</Year>
<Month>05</Month>
<Day>01</Day>
</PubDate>
</JournalIssue>
<Title>Science (New York, N.Y.)</Title>
<ISOAbbreviation>Science</ISOAbbreviation>
</Journal>
<ArticleTitle>The effect of human mobility and control measures on the COVID-19 epidemic in China.</ArticleTitle>
<Pagination><MedlinePgn>493-497</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1126/science.abb4218</ELocationID>
<Abstract><AbstractText>The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.</AbstractText>
<CopyrightInformation>Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Kraemer</LastName>
<ForeName>Moritz U G</ForeName>
<Initials>MUG</Initials>
<Identifier Source="ORCID">0000-0001-8838-7147</Identifier>
<AffiliationInfo><Affiliation>Department of Zoology, University of Oxford, Oxford, UK. s.scarpino@northeastern.edu oliver.pybus@zoo.ox.ac.uk moritz.kraemer@zoo.ox.ac.uk.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Harvard Medical School, Harvard University, Boston, MA, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Boston Children's Hospital, Boston, MA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Yang</LastName>
<ForeName>Chia-Hung</ForeName>
<Initials>CH</Initials>
<AffiliationInfo><Affiliation>Network Science Institute, Northeastern University, Boston, MA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Gutierrez</LastName>
<ForeName>Bernardo</ForeName>
<Initials>B</Initials>
<Identifier Source="ORCID">0000-0002-9220-2739</Identifier>
<AffiliationInfo><Affiliation>Department of Zoology, University of Oxford, Oxford, UK.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>School of Biological and Environmental Sciences, Universidad San Francisco de Quito USFQ, Quito, Ecuador.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Wu</LastName>
<ForeName>Chieh-Hsi</ForeName>
<Initials>CH</Initials>
<Identifier Source="ORCID">0000-0001-9386-725X</Identifier>
<AffiliationInfo><Affiliation>Mathematical Sciences, University of Southampton, Southampton, UK.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Klein</LastName>
<ForeName>Brennan</ForeName>
<Initials>B</Initials>
<Identifier Source="ORCID">0000-0001-8326-5044</Identifier>
<AffiliationInfo><Affiliation>Network Science Institute, Northeastern University, Boston, MA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Pigott</LastName>
<ForeName>David M</ForeName>
<Initials>DM</Initials>
<Identifier Source="ORCID">0000-0002-6731-4034</Identifier>
<AffiliationInfo><Affiliation>Institute for Health Metrics and Evaluation, Department of Health Metrics, University of Washington, Seattle, WA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><CollectiveName>Open COVID-19 Data Working Group</CollectiveName>
</Author>
<Author ValidYN="Y"><LastName>du Plessis</LastName>
<ForeName>Louis</ForeName>
<Initials>L</Initials>
<Identifier Source="ORCID">0000-0003-0352-6289</Identifier>
<AffiliationInfo><Affiliation>Department of Zoology, University of Oxford, Oxford, UK.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Faria</LastName>
<ForeName>Nuno R</ForeName>
<Initials>NR</Initials>
<Identifier Source="ORCID">0000-0001-8839-2798</Identifier>
<AffiliationInfo><Affiliation>Department of Zoology, University of Oxford, Oxford, UK.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Li</LastName>
<ForeName>Ruoran</ForeName>
<Initials>R</Initials>
<Identifier Source="ORCID">0000-0001-7575-2758</Identifier>
<AffiliationInfo><Affiliation>Harvard T.H. Chan School of Public Health, Boston, MA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Hanage</LastName>
<ForeName>William P</ForeName>
<Initials>WP</Initials>
<Identifier Source="ORCID">0000-0002-6319-7336</Identifier>
<AffiliationInfo><Affiliation>Harvard T.H. Chan School of Public Health, Boston, MA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Brownstein</LastName>
<ForeName>John S</ForeName>
<Initials>JS</Initials>
<AffiliationInfo><Affiliation>Harvard Medical School, Harvard University, Boston, MA, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Boston Children's Hospital, Boston, MA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Layan</LastName>
<ForeName>Maylis</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Sorbonne Université, Paris, France.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Vespignani</LastName>
<ForeName>Alessandro</ForeName>
<Initials>A</Initials>
<Identifier Source="ORCID">0000-0003-3419-4205</Identifier>
<AffiliationInfo><Affiliation>Network Science Institute, Northeastern University, Boston, MA, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>ISI Foundation, Turin, Italy.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Tian</LastName>
<ForeName>Huaiyu</ForeName>
<Initials>H</Initials>
<AffiliationInfo><Affiliation>State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Dye</LastName>
<ForeName>Christopher</ForeName>
<Initials>C</Initials>
<Identifier Source="ORCID">0000-0002-2957-1793</Identifier>
<AffiliationInfo><Affiliation>Department of Zoology, University of Oxford, Oxford, UK.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Pybus</LastName>
<ForeName>Oliver G</ForeName>
<Initials>OG</Initials>
<Identifier Source="ORCID">0000-0002-8797-2667</Identifier>
<AffiliationInfo><Affiliation>Department of Zoology, University of Oxford, Oxford, UK. s.scarpino@northeastern.edu oliver.pybus@zoo.ox.ac.uk moritz.kraemer@zoo.ox.ac.uk.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Scarpino</LastName>
<ForeName>Samuel V</ForeName>
<Initials>SV</Initials>
<Identifier Source="ORCID">0000-0001-5716-2770</Identifier>
<AffiliationInfo><Affiliation>Network Science Institute, Northeastern University, Boston, MA, USA. s.scarpino@northeastern.edu oliver.pybus@zoo.ox.ac.uk moritz.kraemer@zoo.ox.ac.uk.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<GrantList CompleteYN="Y"><Grant><GrantID>U54 GM088558</GrantID>
<Acronym>GM</Acronym>
<Agency>NIGMS NIH HHS</Agency>
<Country>United States</Country>
</Grant>
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<PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
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<ArticleDate DateType="Electronic"><Year>2020</Year>
<Month>03</Month>
<Day>25</Day>
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	Serveur d'exploration sur la COVID en France
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The effect of human mobility and control measures on the COVID-19 epidemic in China.

The effect of human mobility and control measures on the COVID-19 epidemic in China.

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