Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions.
Identifieur interne : 000420 ( Main/Exploration ); précédent : 000419; suivant : 000421Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions.
Auteurs : Kevin Linka [États-Unis] ; Mathias Peirlinck [États-Unis] ; Francisco Sahli Costabal [Chili] ; Ellen Kuhl [États-Unis]Source :
- Computer methods in biomechanics and biomedical engineering [ 1476-8259 ] ; 2020.
Descripteurs français
- KwdFr :
- Betacoronavirus (MeSH), Europe (épidémiologie), Humains (MeSH), Infections à coronavirus (prévention et contrôle), Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Maladie liée aux voyages (MeSH), Pandémies (prévention et contrôle), Pneumopathie virale (prévention et contrôle), Pneumopathie virale (transmission), Pneumopathie virale (épidémiologie), Voyage (MeSH), Épidémies de maladies (MeSH).
- MESH :
- prévention et contrôle : Infections à coronavirus, Pandémies, Pneumopathie virale.
- épidémiologie : Europe, Infections à coronavirus, Pneumopathie virale.
- Betacoronavirus, Humains, Maladie liée aux voyages, Voyage, Épidémies de maladies.
English descriptors
- KwdEn :
- Betacoronavirus (MeSH), COVID-19 (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Coronavirus Infections (transmission), Disease Outbreaks (MeSH), Europe (epidemiology), Humans (MeSH), Pandemics (prevention & control), Pneumonia, Viral (epidemiology), Pneumonia, Viral (prevention & control), Pneumonia, Viral (transmission), SARS-CoV-2 (MeSH), Travel (MeSH), Travel-Related Illness (MeSH).
- MESH :
- geographic , epidemiology : Europe.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- prevention & control : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- Betacoronavirus, COVID-19, Disease Outbreaks, Humans, SARS-CoV-2, Travel, Travel-Related Illness.
Abstract
For the first time in history, on March 17, 2020, the European Union closed all its external borders in an attempt to contain the spreading of the coronavirus 2019, COVID-19. Throughout two past months, governments around the world have implemented massive travel restrictions and border control to mitigate the outbreak of this global pandemic. However, the precise effects of travel restrictions on the outbreak dynamics of COVID-19 remain unknown. Here we combine a global network mobility model with a local epidemiology model to simulate and predict the outbreak dynamics and outbreak control of COVID-19 across Europe. We correlate our mobility model to passenger air travel statistics and calibrate our epidemiology model using the number of reported COVID-19 cases for each country. Our simulations show that mobility networks of air travel can predict the emerging global diffusion pattern of a pandemic at the early stages of the outbreak. Our results suggest that an unconstrained mobility would have significantly accelerated the spreading of COVID-19, especially in Central Europe, Spain, and France. Ultimately, our network epidemiology model can inform political decision making and help identify exit strategies from current travel restrictions and total lockdown.
DOI: 10.1080/10255842.2020.1759560
PubMed: 32367739
PubMed Central: PMC7429245
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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wicri:level="4"><nlm:affiliation>Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, CA</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Peirlinck, Mathias" sort="Peirlinck, Mathias" uniqKey="Peirlinck M" first="Mathias" last="Peirlinck">Mathias Peirlinck</name><affiliation wicri:level="4"><nlm:affiliation>Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, 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(transmission)</term><term>Infections à coronavirus (épidémiologie)</term><term>Maladie liée aux voyages (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>Voyage (MeSH)</term><term>Épidémies de maladies (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Europe</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 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Throughout two past months, governments around the world have implemented massive travel restrictions and border control to mitigate the outbreak of this global pandemic. However, the precise effects of travel restrictions on the outbreak dynamics of COVID-19 remain unknown. Here we combine a global network mobility model with a local epidemiology model to simulate and predict the outbreak dynamics and outbreak control of COVID-19 across Europe. We correlate our mobility model to passenger air travel statistics and calibrate our epidemiology model using the number of reported COVID-19 cases for each country. Our simulations show that mobility networks of air travel can predict the emerging global diffusion pattern of a pandemic at the early stages of the outbreak. Our results suggest that an unconstrained mobility would have significantly accelerated the spreading of COVID-19, especially in Central Europe, Spain, and France. Ultimately, our network epidemiology model can inform political decision making and help identify exit strategies from current travel restrictions and total lockdown.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">32367739</PMID><DateCompleted><Year>2020</Year><Month>08</Month><Day>19</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>18</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1476-8259</ISSN><JournalIssue CitedMedium="Internet"><Volume>23</Volume><Issue>11</Issue><PubDate><Year>2020</Year><Month>Aug</Month></PubDate></JournalIssue><Title>Computer methods in biomechanics and biomedical engineering</Title><ISOAbbreviation>Comput Methods Biomech Biomed Engin</ISOAbbreviation></Journal><ArticleTitle>Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions.</ArticleTitle><Pagination><MedlinePgn>710-717</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1080/10255842.2020.1759560</ELocationID><Abstract><AbstractText>For the first time in history, on March 17, 2020, the European Union closed all its external borders in an attempt to contain the spreading of the coronavirus 2019, COVID-19. Throughout two past months, governments around the world have implemented massive travel restrictions and border control to mitigate the outbreak of this global pandemic. However, the precise effects of travel restrictions on the outbreak dynamics of COVID-19 remain unknown. Here we combine a global network mobility model with a local epidemiology model to simulate and predict the outbreak dynamics and outbreak control of COVID-19 across Europe. We correlate our mobility model to passenger air travel statistics and calibrate our epidemiology model using the number of reported COVID-19 cases for each country. Our simulations show that mobility networks of air travel can predict the emerging global diffusion pattern of a pandemic at the early stages of the outbreak. Our results suggest that an unconstrained mobility would have significantly accelerated the spreading of COVID-19, especially in Central Europe, Spain, and France. Ultimately, our network epidemiology model can inform political decision making and help identify exit strategies from current travel restrictions and total lockdown.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Linka</LastName><ForeName>Kevin</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Peirlinck</LastName><ForeName>Mathias</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sahli Costabal</LastName><ForeName>Francisco</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Department of Mechanical and Metallurgical Engineering, School of Engineering, Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Catolica de Chile, Santiago, Chile.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kuhl</LastName><ForeName>Ellen</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, CA, 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="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>05</Month><Day>05</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Comput Methods Biomech Biomed 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