Mobility network models of COVID-19 explain inequities and inform reopening.
Identifieur interne : 000116 ( Main/Exploration ); précédent : 000115; suivant : 000117Mobility network models of COVID-19 explain inequities and inform reopening.
Auteurs : Serina Chang [États-Unis] ; Emma Pierson [États-Unis] ; Pang Wei Koh [États-Unis] ; Jaline Gerardin [États-Unis] ; Beth Redbird [États-Unis] ; David Grusky [États-Unis] ; Jure Leskovec [États-Unis]Source :
- Nature [ 1476-4687 ] ; 2020.
Abstract
The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.
DOI: 10.1038/s41586-020-2923-3
PubMed: 33171481
Affiliations:
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uniqKey="Gerardin J" first="Jaline" last="Gerardin">Jaline Gerardin</name><affiliation wicri:level="2"><nlm:affiliation>Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Preventive Medicine, Northwestern University, Chicago, IL</wicri:regionArea><placeName><region type="state">Illinois</region></placeName></affiliation></author><author><name sortKey="Redbird, Beth" sort="Redbird, Beth" uniqKey="Redbird B" first="Beth" last="Redbird">Beth Redbird</name><affiliation wicri:level="2"><nlm:affiliation>Department of Sociology, Northwestern University, Evanston, IL, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Sociology, Northwestern University, Evanston, IL</wicri:regionArea><placeName><region type="state">Illinois</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>Institute for Policy 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University, Stanford, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, Stanford University, Stanford, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Pierson, Emma" sort="Pierson, Emma" uniqKey="Pierson E" first="Emma" last="Pierson">Emma Pierson</name><affiliation wicri:level="2"><nlm:affiliation>Department of Computer Science, Stanford University, Stanford, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, Stanford University, Stanford, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>Microsoft Research, Cambridge, MA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Microsoft Research, Cambridge, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author><author><name sortKey="Koh, Pang Wei" sort="Koh, Pang Wei" uniqKey="Koh P" first="Pang Wei" last="Koh">Pang Wei Koh</name><affiliation wicri:level="2"><nlm:affiliation>Department of Computer Science, Stanford University, Stanford, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, Stanford University, Stanford, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Gerardin, Jaline" sort="Gerardin, Jaline" uniqKey="Gerardin J" first="Jaline" last="Gerardin">Jaline Gerardin</name><affiliation wicri:level="2"><nlm:affiliation>Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Preventive Medicine, Northwestern University, Chicago, IL</wicri:regionArea><placeName><region type="state">Illinois</region></placeName></affiliation></author><author><name sortKey="Redbird, Beth" sort="Redbird, Beth" uniqKey="Redbird B" first="Beth" last="Redbird">Beth Redbird</name><affiliation wicri:level="2"><nlm:affiliation>Department of Sociology, Northwestern University, Evanston, IL, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Sociology, Northwestern University, Evanston, IL</wicri:regionArea><placeName><region type="state">Illinois</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>Institute for Policy Research, Northwestern University, Evanston, IL, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Institute for Policy Research, Northwestern University, Evanston, IL</wicri:regionArea><placeName><region type="state">Illinois</region></placeName></affiliation></author><author><name sortKey="Grusky, David" sort="Grusky, David" uniqKey="Grusky D" first="David" last="Grusky">David Grusky</name><affiliation wicri:level="2"><nlm:affiliation>Department of Sociology, Stanford University, Stanford, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Sociology, Stanford University, Stanford, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>Center on Poverty and Inequality, Stanford University, Stanford, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center on Poverty and Inequality, Stanford University, Stanford, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Leskovec, Jure" sort="Leskovec, Jure" uniqKey="Leskovec J" first="Jure" last="Leskovec">Jure Leskovec</name><affiliation wicri:level="2"><nlm:affiliation>Department of Computer Science, Stanford University, Stanford, CA, USA. jure@cs.stanford.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, Stanford University, Stanford, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>Chan Zuckerberg Biohub, San Francisco, CA, USA. jure@cs.stanford.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Chan Zuckerberg Biohub, San Francisco, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author></analytic><series><title level="j">Nature</title><idno type="eISSN">1476-4687</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">The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)<sup>1</sup>. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups<sup>2-8</sup> solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">33171481</PMID><DateRevised><Year>2020</Year><Month>12</Month><Day>03</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1476-4687</ISSN><JournalIssue CitedMedium="Internet"><PubDate><Year>2020</Year><Month>Nov</Month><Day>10</Day></PubDate></JournalIssue><Title>Nature</Title><ISOAbbreviation>Nature</ISOAbbreviation></Journal><ArticleTitle>Mobility network models of COVID-19 explain inequities and inform reopening.</ArticleTitle><ELocationID EIdType="doi" ValidYN="Y">10.1038/s41586-020-2923-3</ELocationID><Abstract><AbstractText>The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)<sup>1</sup>. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups<sup>2-8</sup> solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Chang</LastName><ForeName>Serina</ForeName><Initials>S</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-4253-1016</Identifier><AffiliationInfo><Affiliation>Department of Computer Science, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pierson</LastName><ForeName>Emma</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Department of Computer Science, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Microsoft Research, Cambridge, MA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Koh</LastName><ForeName>Pang Wei</ForeName><Initials>PW</Initials><AffiliationInfo><Affiliation>Department of Computer Science, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gerardin</LastName><ForeName>Jaline</ForeName><Initials>J</Initials><Identifier Source="ORCID">http://orcid.org/0000-0001-8071-9928</Identifier><AffiliationInfo><Affiliation>Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Redbird</LastName><ForeName>Beth</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Department of Sociology, Northwestern University, Evanston, IL, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Institute for Policy Research, Northwestern University, Evanston, IL, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Grusky</LastName><ForeName>David</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Sociology, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center on Poverty and Inequality, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Leskovec</LastName><ForeName>Jure</ForeName><Initials>J</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-5411-923X</Identifier><AffiliationInfo><Affiliation>Department of Computer Science, Stanford University, Stanford, CA, USA. jure@cs.stanford.edu.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Chan Zuckerberg Biohub, San Francisco, CA, USA. jure@cs.stanford.edu.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>11</Month><Day>10</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Nature</MedlineTA><NlmUniqueID>0410462</NlmUniqueID><ISSNLinking>0028-0836</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>06</Month><Day>15</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>10</Month><Day>21</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>11</Month><Day>11</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>11</Month><Day>11</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>11</Month><Day>10</Day><Hour>20</Hour><Minute>15</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33171481</ArticleId><ArticleId IdType="doi">10.1038/s41586-020-2923-3</ArticleId><ArticleId IdType="pii">10.1038/s41586-020-2923-3</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Californie</li><li>Illinois</li><li>Massachusetts</li></region></list><tree><country name="États-Unis"><region name="Californie"><name sortKey="Chang, Serina" sort="Chang, Serina" uniqKey="Chang S" first="Serina" last="Chang">Serina Chang</name></region><name sortKey="Gerardin, Jaline" sort="Gerardin, Jaline" uniqKey="Gerardin J" first="Jaline" last="Gerardin">Jaline Gerardin</name><name sortKey="Grusky, David" sort="Grusky, David" uniqKey="Grusky D" first="David" last="Grusky">David Grusky</name><name sortKey="Grusky, David" sort="Grusky, David" uniqKey="Grusky D" first="David" last="Grusky">David Grusky</name><name sortKey="Koh, Pang Wei" sort="Koh, Pang Wei" uniqKey="Koh P" first="Pang Wei" last="Koh">Pang Wei Koh</name><name sortKey="Leskovec, Jure" sort="Leskovec, Jure" uniqKey="Leskovec J" first="Jure" last="Leskovec">Jure Leskovec</name><name sortKey="Leskovec, Jure" sort="Leskovec, Jure" uniqKey="Leskovec J" first="Jure" last="Leskovec">Jure Leskovec</name><name sortKey="Pierson, Emma" sort="Pierson, Emma" uniqKey="Pierson E" first="Emma" last="Pierson">Emma Pierson</name><name sortKey="Pierson, Emma" sort="Pierson, Emma" uniqKey="Pierson E" first="Emma" last="Pierson">Emma Pierson</name><name sortKey="Redbird, Beth" sort="Redbird, Beth" uniqKey="Redbird B" first="Beth" last="Redbird">Beth Redbird</name><name sortKey="Redbird, Beth" sort="Redbird, Beth" uniqKey="Redbird B" first="Beth" last="Redbird">Beth Redbird</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix 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