LockdownV1, Main, Exploration, bibRecord, 002889

Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data.

Identifieur interne : 002889 ( Main/Exploration ); précédent : 002888; suivant : 002890

Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data.

Auteurs : Corey M. Peak [États-Unis, Suède] ; Amy Wesolowski [États-Unis] ; Elisabeth Zu Erbach-Schoenberg [Suède, Royaume-Uni] ; Andrew J. Tatem [Suède, Royaume-Uni] ; Erik Wetter [Suède, Philippines] ; Xin Lu [Suède, République populaire de Chine] ; Daniel Power [Suède] ; Elaine Weidman-Grunewald [Suède] ; Sergio Ramos [Suède] ; Simon Moritz [Suède] ; Caroline O. Buckee [États-Unis] ; Linus Bengtsson [Suède]

Source :

International journal of epidemiology [ 1464-3685 ] ; 2018.

RBID : pubmed:29947788

Descripteurs français

KwdFr :
- Dynamique des populations (MeSH), Fièvre hémorragique à virus Ebola (transmission), Fièvre hémorragique à virus Ebola (épidémiologie), Humains (MeSH), Incidence (MeSH), Prévention des infections (méthodes), Sierra Leone (épidémiologie), Téléphones portables (statistiques et données numériques), Voyage (législation et jurisprudence), Voyage (statistiques et données numériques), Épidémies (MeSH), Études rétrospectives (MeSH).
MESH :
- législation et jurisprudence : Voyage.
- méthodes : Prévention des infections.
- statistiques et données numériques : Téléphones portables, Voyage.
- épidémiologie : Fièvre hémorragique à virus Ebola, Sierra Leone.
- Dynamique des populations, Humains, Incidence, Épidémies, Études rétrospectives.
Wicri :
- geographic : Sierra Leone.

English descriptors

KwdEn :
- Cell Phone (statistics & numerical data), Epidemics (MeSH), Hemorrhagic Fever, Ebola (epidemiology), Hemorrhagic Fever, Ebola (transmission), Humans (MeSH), Incidence (MeSH), Infection Control (methods), Population Dynamics (MeSH), Retrospective Studies (MeSH), Sierra Leone (epidemiology), Travel (legislation & jurisprudence), Travel (statistics & numerical data).
MESH :
- geographic , epidemiology : Sierra Leone.
- epidemiology : Hemorrhagic Fever, Ebola.
- legislation & jurisprudence : Travel.
- methods : Infection Control.
- statistics & numerical data : Cell Phone, Travel.
- transmission : Hemorrhagic Fever, Ebola.
- Epidemics, Humans, Incidence, Population Dynamics, Retrospective Studies.

Abstract

Background

Travel restrictions were implemented on an unprecedented scale in 2015 in Sierra Leone to contain and eliminate Ebola virus disease. However, the impact of epidemic travel restrictions on mobility itself remains difficult to measure with traditional methods. New 'big data' approaches using mobile phone data can provide, in near real-time, the type of information needed to guide and evaluate control measures.

Methods

We analysed anonymous mobile phone call detail records (CDRs) from a leading operator in Sierra Leone between 20 March and 1 July in 2015. We used an anomaly detection algorithm to assess changes in travel during a national 'stay at home' lockdown from 27 to 29 March. To measure the magnitude of these changes and to assess effect modification by region and historical Ebola burden, we performed a time series analysis and a crossover analysis.

Results

Routinely collected mobile phone data revealed a dramatic reduction in human mobility during a 3-day lockdown in Sierra Leone. The number of individuals relocating between chiefdoms decreased by 31% within 15 km, by 46% for 15-30 km and by 76% for distances greater than 30 km. This effect was highly heterogeneous in space, with higher impact in regions with higher Ebola incidence. Travel quickly returned to normal patterns after the restrictions were lifted.

Conclusions

The effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymization protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment.

DOI: 10.1093/ije/dyy095
PubMed: 29947788
PubMed Central: PMC6208277

Affiliations:

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Le document en format XML

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<affiliation wicri:level="1"><nlm:affiliation>Asian Institute of Management, University in Makati, Metro Manila, Philippines.</nlm:affiliation>
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<author><name sortKey="Lu, Xin" sort="Lu, Xin" uniqKey="Lu X" first="Xin" last="Lu">Xin Lu</name>
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<author><name sortKey="Power, Daniel" sort="Power, Daniel" uniqKey="Power D" first="Daniel" last="Power">Daniel Power</name>
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<author><name sortKey="Ramos, Sergio" sort="Ramos, Sergio" uniqKey="Ramos S" first="Sergio" last="Ramos">Sergio Ramos</name>
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<author><name sortKey="Buckee, Caroline O" sort="Buckee, Caroline O" uniqKey="Buckee C" first="Caroline O" last="Buckee">Caroline O. Buckee</name>
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</placeName>
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<author><name sortKey="Bengtsson, Linus" sort="Bengtsson, Linus" uniqKey="Bengtsson L" first="Linus" last="Bengtsson">Linus Bengtsson</name>
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</placeName>
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<affiliation wicri:level="3"><nlm:affiliation>Department of Epidemiology and Public Health Sciences, Karolinska Institute, Stockholm, Sweden.</nlm:affiliation>
<country xml:lang="fr">Suède</country>
<wicri:regionArea>Department of Epidemiology and Public Health Sciences, Karolinska Institute, Stockholm</wicri:regionArea>
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<series><title level="j">International journal of epidemiology</title>
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<imprint><date when="2018" type="published">2018</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Cell Phone (statistics & numerical data)</term>
<term>Epidemics (MeSH)</term>
<term>Hemorrhagic Fever, Ebola (epidemiology)</term>
<term>Hemorrhagic Fever, Ebola (transmission)</term>
<term>Humans (MeSH)</term>
<term>Incidence (MeSH)</term>
<term>Infection Control (methods)</term>
<term>Population Dynamics (MeSH)</term>
<term>Retrospective Studies (MeSH)</term>
<term>Sierra Leone (epidemiology)</term>
<term>Travel (legislation & jurisprudence)</term>
<term>Travel (statistics & numerical data)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Dynamique des populations (MeSH)</term>
<term>Fièvre hémorragique à virus Ebola (transmission)</term>
<term>Fièvre hémorragique à virus Ebola (épidémiologie)</term>
<term>Humains (MeSH)</term>
<term>Incidence (MeSH)</term>
<term>Prévention des infections (méthodes)</term>
<term>Sierra Leone (épidémiologie)</term>
<term>Téléphones portables (statistiques et données numériques)</term>
<term>Voyage (législation et jurisprudence)</term>
<term>Voyage (statistiques et données numériques)</term>
<term>Épidémies (MeSH)</term>
<term>Études rétrospectives (MeSH)</term>
</keywords>
<keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Sierra Leone</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Hemorrhagic Fever, Ebola</term>
</keywords>
<keywords scheme="MESH" qualifier="legislation & jurisprudence" xml:lang="en"><term>Travel</term>
</keywords>
<keywords scheme="MESH" qualifier="législation et jurisprudence" xml:lang="fr"><term>Voyage</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Infection Control</term>
</keywords>
<keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Prévention des infections</term>
</keywords>
<keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Cell Phone</term>
<term>Travel</term>
</keywords>
<keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Téléphones portables</term>
<term>Voyage</term>
</keywords>
<keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Hemorrhagic Fever, Ebola</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Fièvre hémorragique à virus Ebola</term>
<term>Sierra Leone</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Epidemics</term>
<term>Humans</term>
<term>Incidence</term>
<term>Population Dynamics</term>
<term>Retrospective Studies</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Dynamique des populations</term>
<term>Humains</term>
<term>Incidence</term>
<term>Épidémies</term>
<term>Études rétrospectives</term>
</keywords>
<keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Sierra Leone</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><p><b>Background</b>
</p>
<p>Travel restrictions were implemented on an unprecedented scale in 2015 in Sierra Leone to contain and eliminate Ebola virus disease. However, the impact of epidemic travel restrictions on mobility itself remains difficult to measure with traditional methods. New 'big data' approaches using mobile phone data can provide, in near real-time, the type of information needed to guide and evaluate control measures.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>Methods</b>
</p>
<p>We analysed anonymous mobile phone call detail records (CDRs) from a leading operator in Sierra Leone between 20 March and 1 July in 2015. We used an anomaly detection algorithm to assess changes in travel during a national 'stay at home' lockdown from 27 to 29 March. To measure the magnitude of these changes and to assess effect modification by region and historical Ebola burden, we performed a time series analysis and a crossover analysis.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>Results</b>
</p>
<p>Routinely collected mobile phone data revealed a dramatic reduction in human mobility during a 3-day lockdown in Sierra Leone. The number of individuals relocating between chiefdoms decreased by 31% within 15 km, by 46% for 15-30 km and by 76% for distances greater than 30 km. This effect was highly heterogeneous in space, with higher impact in regions with higher Ebola incidence. Travel quickly returned to normal patterns after the restrictions were lifted.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>Conclusions</b>
</p>
<p>The effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymization protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment.</p>
</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29947788</PMID>
<DateCompleted><Year>2019</Year>
<Month>05</Month>
<Day>29</Day>
</DateCompleted>
<DateRevised><Year>2019</Year>
<Month>05</Month>
<Day>29</Day>
</DateRevised>
<Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1464-3685</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>47</Volume>
<Issue>5</Issue>
<PubDate><Year>2018</Year>
<Month>10</Month>
<Day>01</Day>
</PubDate>
</JournalIssue>
<Title>International journal of epidemiology</Title>
<ISOAbbreviation>Int J Epidemiol</ISOAbbreviation>
</Journal>
<ArticleTitle>Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data.</ArticleTitle>
<Pagination><MedlinePgn>1562-1570</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1093/ije/dyy095</ELocationID>
<Abstract><AbstractText Label="Background">Travel restrictions were implemented on an unprecedented scale in 2015 in Sierra Leone to contain and eliminate Ebola virus disease. However, the impact of epidemic travel restrictions on mobility itself remains difficult to measure with traditional methods. New 'big data' approaches using mobile phone data can provide, in near real-time, the type of information needed to guide and evaluate control measures.</AbstractText>
<AbstractText Label="Methods">We analysed anonymous mobile phone call detail records (CDRs) from a leading operator in Sierra Leone between 20 March and 1 July in 2015. We used an anomaly detection algorithm to assess changes in travel during a national 'stay at home' lockdown from 27 to 29 March. To measure the magnitude of these changes and to assess effect modification by region and historical Ebola burden, we performed a time series analysis and a crossover analysis.</AbstractText>
<AbstractText Label="Results">Routinely collected mobile phone data revealed a dramatic reduction in human mobility during a 3-day lockdown in Sierra Leone. The number of individuals relocating between chiefdoms decreased by 31% within 15 km, by 46% for 15-30 km and by 76% for distances greater than 30 km. This effect was highly heterogeneous in space, with higher impact in regions with higher Ebola incidence. Travel quickly returned to normal patterns after the restrictions were lifted.</AbstractText>
<AbstractText Label="Conclusions">The effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymization protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Peak</LastName>
<ForeName>Corey M</ForeName>
<Initials>CM</Initials>
<AffiliationInfo><Affiliation>Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Flowminder Foundation, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Wesolowski</LastName>
<ForeName>Amy</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Zu Erbach-Schoenberg</LastName>
<ForeName>Elisabeth</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>Flowminder Foundation, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Tatem</LastName>
<ForeName>Andrew J</ForeName>
<Initials>AJ</Initials>
<AffiliationInfo><Affiliation>Flowminder Foundation, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Wetter</LastName>
<ForeName>Erik</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>Flowminder Foundation, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Misum, Stockholm School of Economics, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Asian Institute of Management, University in Makati, Metro Manila, Philippines.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Lu</LastName>
<ForeName>Xin</ForeName>
<Initials>X</Initials>
<AffiliationInfo><Affiliation>Flowminder Foundation, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>School of Business, Central South University, Changsha, China.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>College of Information System and Management, National University of Defense Technology, Changsha, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Power</LastName>
<ForeName>Daniel</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Flowminder Foundation, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Weidman-Grunewald</LastName>
<ForeName>Elaine</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>Erisson Technology and Emerging Business, Ericsson, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ramos</LastName>
<ForeName>Sergio</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Erisson Technology and Emerging Business, Ericsson, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Moritz</LastName>
<ForeName>Simon</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Erisson Technology and Emerging Business, Ericsson, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Buckee</LastName>
<ForeName>Caroline O</ForeName>
<Initials>CO</Initials>
<AffiliationInfo><Affiliation>Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Bengtsson</LastName>
<ForeName>Linus</ForeName>
<Initials>L</Initials>
<AffiliationInfo><Affiliation>Flowminder Foundation, Stockholm, Sweden.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Epidemiology and Public Health Sciences, Karolinska Institute, Stockholm, Sweden.</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>
<Grant><GrantID>T32 AI007535</GrantID>
<Acronym>AI</Acronym>
<Agency>NIAID NIH HHS</Agency>
<Country>United States</Country>
</Grant>
<Grant><GrantID>106866/Z/15/Z</GrantID>
<Agency>Wellcome Trust</Agency>
<Country>United Kingdom</Country>
</Grant>
</GrantList>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
</PublicationTypeList>
</Article>
<MedlineJournalInfo><Country>England</Country>
<MedlineTA>Int J Epidemiol</MedlineTA>
<NlmUniqueID>7802871</NlmUniqueID>
<ISSNLinking>0300-5771</ISSNLinking>
</MedlineJournalInfo>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D040421" MajorTopicYN="N">Cell Phone</DescriptorName>
<QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D058872" MajorTopicYN="Y">Epidemics</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D019142" MajorTopicYN="N">Hemorrhagic Fever, Ebola</DescriptorName>
<QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName>
<QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D015994" MajorTopicYN="N">Incidence</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D017053" MajorTopicYN="N">Infection Control</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011157" MajorTopicYN="N">Population Dynamics</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012189" MajorTopicYN="N">Retrospective Studies</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012807" MajorTopicYN="N" Type="Geographic">Sierra Leone</DescriptorName>
<QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014195" MajorTopicYN="N">Travel</DescriptorName>
<QualifierName UI="Q000331" MajorTopicYN="Y">legislation & jurisprudence</QualifierName>
<QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName>
</MeshHeading>
</MeshHeadingList>
</MedlineCitation>
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<Month>05</Month>
<Day>10</Day>
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<Month>6</Month>
<Day>28</Day>
<Hour>6</Hour>
<Minute>0</Minute>
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<affiliations><list><country><li>Philippines</li>
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<li>États-Unis</li>
</country>
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	Serveur d'exploration sur le confinement (PubMed)
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Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data.

Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data.

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