Measuring mobility to monitor travel and physical distancing interventions: a common framework for mobile phone data analysis.
Identifieur interne : 000462 ( Main/Exploration ); précédent : 000461; suivant : 000463Measuring mobility to monitor travel and physical distancing interventions: a common framework for mobile phone data analysis.
Auteurs : Nishant Kishore [États-Unis] ; Mathew V. Kiang [États-Unis] ; Kenth Eng Monsen [Norvège] ; Navin Vembar [États-Unis] ; Andrew Schroeder [États-Unis] ; Satchit Balsari [États-Unis] ; Caroline O. Buckee [États-Unis]Source :
- The Lancet. Digital health [ 2589-7500 ] ; 2020.
Descripteurs français
- KwdFr :
- MESH :
- statistiques et données numériques : Téléphones portables, Voyage.
- Analyse spatio-temporelle, Diffusion de l'information, Humains, Modèles statistiques, Surveillance épidémiologique, Systèmes d'information géographique.
English descriptors
- KwdEn :
- COVID-19 (epidemiology), COVID-19 (prevention & control), Cell Phone (statistics & numerical data), Epidemiological Monitoring (MeSH), Geographic Information Systems (MeSH), Humans (MeSH), Information Dissemination (MeSH), Models, Statistical (MeSH), Physical Distancing (MeSH), Spatio-Temporal Analysis (MeSH), Travel (statistics & numerical data).
- MESH :
- epidemiology : COVID-19.
- prevention & control : COVID-19.
- statistics & numerical data : Cell Phone, Travel.
- Epidemiological Monitoring, Geographic Information Systems, Humans, Information Dissemination, Models, Statistical, Physical Distancing, Spatio-Temporal Analysis.
Abstract
A surge of interest has been noted in the use of mobility data from mobile phones to monitor physical distancing and model the spread of severe acute respiratory syndrome coronavirus 2, the virus that causes COVID-19. Despite several years of research in this area, standard frameworks for aggregating and making use of different data streams from mobile phones are scarce and difficult to generalise across data providers. Here, we examine aggregation principles and procedures for different mobile phone data streams and describe a common syntax for how aggregated data are used in research and policy. We argue that the principles of privacy and data protection are vital in assessing more technical aspects of aggregation and should be an important central feature to guide partnerships with governments who make use of research products.
DOI: 10.1016/S2589-7500(20)30193-X
PubMed: 32905027
PubMed Central: PMC7462565
Affiliations:
- Norvège, États-Unis
- Californie, District de Columbia, Massachusetts, Østlandet
- Cambridge (Massachusetts), Oslo
- Université Harvard
Links toward previous steps (curation, corpus...)
Le document en format XML
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Kiang</name><affiliation wicri:level="2"><nlm:affiliation>Center for Population Health Sciences, Stanford University School of Medicine, Stanford, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Population Health Sciences, Stanford University School of Medicine, Stanford, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Eng Monsen, Kenth" sort="Eng Monsen, Kenth" uniqKey="Eng Monsen K" first="Kenth" last="Eng Monsen">Kenth Eng Monsen</name><affiliation wicri:level="3"><nlm:affiliation>Telenor Research, Oslo, Norway.</nlm:affiliation><country xml:lang="fr">Norvège</country><wicri:regionArea>Telenor Research, Oslo</wicri:regionArea><placeName><settlement type="city">Oslo</settlement><region nuts="2">Østlandet</region></placeName></affiliation></author><author><name sortKey="Vembar, Navin" sort="Vembar, Navin" uniqKey="Vembar N" first="Navin" last="Vembar">Navin Vembar</name><affiliation wicri:level="2"><nlm:affiliation>Camber Systems, Washington, DC, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Camber Systems, Washington, DC</wicri:regionArea><placeName><region type="state">District de Columbia</region></placeName></affiliation></author><author><name sortKey="Schroeder, Andrew" sort="Schroeder, Andrew" uniqKey="Schroeder A" first="Andrew" last="Schroeder">Andrew Schroeder</name><affiliation wicri:level="2"><nlm:affiliation>Direct Relief, Santa Barbara, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Direct Relief, Santa Barbara, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Balsari, Satchit" sort="Balsari, Satchit" uniqKey="Balsari S" first="Satchit" last="Balsari">Satchit Balsari</name><affiliation wicri:level="2"><nlm:affiliation>Department of Emergency Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Emergency Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation><affiliation wicri:level="4"><nlm:affiliation>India Digital Health Network, Lakshmi Mittal and Family South Asia Institute, Harvard University, Cambridge, MA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>India Digital Health Network, Lakshmi Mittal and Family South Asia Institute, Harvard University, Cambridge, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation></author><author><name sortKey="Buckee, Caroline O" sort="Buckee, Caroline O" uniqKey="Buckee C" first="Caroline O" last="Buckee">Caroline O. Buckee</name><affiliation wicri:level="2"><nlm:affiliation>Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author></analytic><series><title level="j">The Lancet. Digital health</title><idno type="eISSN">2589-7500</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>COVID-19 (epidemiology)</term><term>COVID-19 (prevention & control)</term><term>Cell Phone (statistics & numerical data)</term><term>Epidemiological Monitoring (MeSH)</term><term>Geographic Information Systems (MeSH)</term><term>Humans (MeSH)</term><term>Information Dissemination (MeSH)</term><term>Models, Statistical (MeSH)</term><term>Physical Distancing (MeSH)</term><term>Spatio-Temporal Analysis (MeSH)</term><term>Travel (statistics & numerical data)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse spatio-temporelle (MeSH)</term><term>Diffusion de l'information (MeSH)</term><term>Humains (MeSH)</term><term>Modèles statistiques (MeSH)</term><term>Surveillance épidémiologique (MeSH)</term><term>Systèmes d'information géographique (MeSH)</term><term>Téléphones portables (statistiques et données numériques)</term><term>Voyage (statistiques et données numériques)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>COVID-19</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>COVID-19</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" xml:lang="en"><term>Epidemiological Monitoring</term><term>Geographic Information Systems</term><term>Humans</term><term>Information Dissemination</term><term>Models, Statistical</term><term>Physical Distancing</term><term>Spatio-Temporal Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse spatio-temporelle</term><term>Diffusion de l'information</term><term>Humains</term><term>Modèles statistiques</term><term>Surveillance épidémiologique</term><term>Systèmes d'information géographique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A surge of interest has been noted in the use of mobility data from mobile phones to monitor physical distancing and model the spread of severe acute respiratory syndrome coronavirus 2, the virus that causes COVID-19. Despite several years of research in this area, standard frameworks for aggregating and making use of different data streams from mobile phones are scarce and difficult to generalise across data providers. Here, we examine aggregation principles and procedures for different mobile phone data streams and describe a common syntax for how aggregated data are used in research and policy. We argue that the principles of privacy and data protection are vital in assessing more technical aspects of aggregation and should be an important central feature to guide partnerships with governments who make use of research products.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32905027</PMID><DateCompleted><Year>2020</Year><Month>12</Month><Day>18</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>18</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2589-7500</ISSN><JournalIssue CitedMedium="Internet"><Volume>2</Volume><Issue>11</Issue><PubDate><Year>2020</Year><Month>11</Month></PubDate></JournalIssue><Title>The Lancet. Digital health</Title><ISOAbbreviation>Lancet Digit Health</ISOAbbreviation></Journal><ArticleTitle>Measuring mobility to monitor travel and physical distancing interventions: a common framework for mobile phone data analysis.</ArticleTitle><Pagination><MedlinePgn>e622-e628</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/S2589-7500(20)30193-X</ELocationID><Abstract><AbstractText>A surge of interest has been noted in the use of mobility data from mobile phones to monitor physical distancing and model the spread of severe acute respiratory syndrome coronavirus 2, the virus that causes COVID-19. Despite several years of research in this area, standard frameworks for aggregating and making use of different data streams from mobile phones are scarce and difficult to generalise across data providers. Here, we examine aggregation principles and procedures for different mobile phone data streams and describe a common syntax for how aggregated data are used in research and policy. We argue that the principles of privacy and data protection are vital in assessing more technical aspects of aggregation and should be an important central feature to guide partnerships with governments who make use of research products.</AbstractText><CopyrightInformation>© 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Kishore</LastName><ForeName>Nishant</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kiang</LastName><ForeName>Mathew V</ForeName><Initials>MV</Initials><AffiliationInfo><Affiliation>Center for Population Health Sciences, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Engø-Monsen</LastName><ForeName>Kenth</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Telenor Research, Oslo, Norway.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Vembar</LastName><ForeName>Navin</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>Camber Systems, Washington, DC, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schroeder</LastName><ForeName>Andrew</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Direct Relief, Santa Barbara, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Balsari</LastName><ForeName>Satchit</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Emergency Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>India Digital Health Network, Lakshmi Mittal and Family South Asia Institute, Harvard University, Cambridge, MA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Buckee</LastName><ForeName>Caroline O</ForeName><Initials>CO</Initials><AffiliationInfo><Affiliation>Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>09</Month><Day>01</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Lancet Digit Health</MedlineTA><NlmUniqueID>101751302</NlmUniqueID><ISSNLinking>2589-7500</ISSNLinking></MedlineJournalInfo><MeshHeadingList><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D040421" MajorTopicYN="N">Cell Phone</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062665" MajorTopicYN="Y">Epidemiological Monitoring</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D040362" MajorTopicYN="N">Geographic Information Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D033181" MajorTopicYN="N">Information Dissemination</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="N">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000085762" MajorTopicYN="Y">Physical Distancing</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D062211" MajorTopicYN="N">Spatio-Temporal Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014195" MajorTopicYN="N">Travel</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>9</Month><Day>10</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>9</Month><Day>10</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>9</Month><Day>9</Day><Hour>18</Hour><Minute>10</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32905027</ArticleId><ArticleId IdType="doi">10.1016/S2589-7500(20)30193-X</ArticleId><ArticleId IdType="pii">S2589-7500(20)30193-X</ArticleId><ArticleId IdType="pmc">PMC7462565</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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