Global and local mobility as a barometer for COVID-19 dynamics.
Identifieur interne : 000556 ( Main/Exploration ); précédent : 000555; suivant : 000557Global and local mobility as a barometer for COVID-19 dynamics.
Auteurs : Kevin Linka [États-Unis] ; Alain Goriely [Royaume-Uni] ; Ellen Kuhl [États-Unis]Source :
- medRxiv : the preprint server for health sciences ; 2020.
Abstract
. The spreading of infectious diseases including COVID-19 depends on human interactions. In an environment where behavioral patterns and physical contacts are constantly evolving according to new governmental regulations, measuring these interactions is a major challenge. Mobility has emerged as an indicator for human activity and, implicitly, for human interactions. Here we study the coupling between mobility and COVID-19 dynamics and show that variations in global air traffic and local driving mobility can be used to stratify different disease phases. For ten European countries, our study shows maximal correlation between driving mobility and disease dynamics with a time lag of 14.6 +/- 5.6 days. Our findings suggests that local mobility can serve as a quantitative metric to forecast future reproduction numbers and identify the stages of the pandemic when mobility and reproduction become decorrelated.
DOI: 10.1101/2020.06.13.20130658
PubMed: 32817955
PubMed Central: PMC7430597
Affiliations:
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The spreading of infectious diseases including COVID-19 depends on human interactions. In an environment where behavioral patterns and physical contacts are constantly evolving according to new governmental regulations, measuring these interactions is a major challenge. Mobility has emerged as an indicator for human activity and, implicitly, for human interactions. Here we study the coupling between mobility and COVID-19 dynamics and show that variations in global air traffic and local driving mobility can be used to stratify different disease phases. For ten European countries, our study shows maximal correlation between driving mobility and disease dynamics with a time lag of 14.6 +/- 5.6 days. Our findings suggests that local mobility can serve as a quantitative metric to forecast future reproduction numbers and identify the stages of the pandemic when mobility and reproduction become decorrelated.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32817955</PMID><DateRevised><Year>2021</Year><Month>01</Month><Day>23</Day></DateRevised><Article PubModel="Electronic"><Journal><JournalIssue CitedMedium="Internet"><PubDate><Year>2020</Year><Month>Aug</Month><Day>11</Day></PubDate></JournalIssue><Title>medRxiv : the preprint server for health sciences</Title><ISOAbbreviation>medRxiv</ISOAbbreviation></Journal><ArticleTitle>Global and local mobility as a barometer for COVID-19 dynamics.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">2020.06.13.20130658</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1101/2020.06.13.20130658</ELocationID><Abstract><AbstractText>. The spreading of infectious diseases including COVID-19 depends on human interactions. In an environment where behavioral patterns and physical contacts are constantly evolving according to new governmental regulations, measuring these interactions is a major challenge. Mobility has emerged as an indicator for human activity and, implicitly, for human interactions. Here we study the coupling between mobility and COVID-19 dynamics and show that variations in global air traffic and local driving mobility can be used to stratify different disease phases. For ten European countries, our study shows maximal correlation between driving mobility and disease dynamics with a time lag of 14.6 +/- 5.6 days. Our findings suggests that local mobility can serve as a quantitative metric to forecast future reproduction numbers and identify the stages of the pandemic when mobility and reproduction become decorrelated.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Linka</LastName><ForeName>Kevin</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Mechanical Engineering, Stanford University, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Goriely</LastName><ForeName>Alain</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Mathematical Institute, Oxford University, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kuhl</LastName><ForeName>Ellen</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Mechanical Engineering, Stanford University, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>U01 HL119578</GrantID><Acronym>HL</Acronym><Agency>NHLBI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D000076942">Preprint</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>08</Month><Day>11</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>medRxiv</MedlineTA><NlmUniqueID>101767986</NlmUniqueID></MedlineJournalInfo><CommentsCorrectionsList><CommentsCorrections RefType="UpdateIn"><RefSource>Biomech Model Mechanobiol. 2021 Jan 15;:</RefSource><PMID Version="1">33449276</PMID></CommentsCorrections></CommentsCorrectionsList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>21</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>21</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>21</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32817955</ArticleId><ArticleId IdType="doi">10.1101/2020.06.13.20130658</ArticleId><ArticleId IdType="pmc">PMC7430597</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Royaume-Uni</li><li>États-Unis</li></country><region><li>Californie</li></region><settlement><li>Stanford (Californie)</li></settlement><orgName><li>Université Stanford</li></orgName></list><tree><country name="États-Unis"><region name="Californie"><name sortKey="Linka, Kevin" sort="Linka, Kevin" uniqKey="Linka K" first="Kevin" last="Linka">Kevin Linka</name></region><name sortKey="Kuhl, Ellen" sort="Kuhl, Ellen" uniqKey="Kuhl E" first="Ellen" last="Kuhl">Ellen Kuhl</name></country><country name="Royaume-Uni"><noRegion><name sortKey="Goriely, Alain" sort="Goriely, Alain" uniqKey="Goriely A" first="Alain" last="Goriely">Alain Goriely</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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