Is it safe to lift COVID-19 travel bans? The Newfoundland story.
Identifieur interne : 000489 ( Main/Exploration ); précédent : 000488; suivant : 000490Is it safe to lift COVID-19 travel bans? The Newfoundland story.
Auteurs : Kevin Linka [États-Unis] ; Proton Rahman [Canada] ; Alain Goriely [Royaume-Uni] ; Ellen Kuhl [États-Unis]Source :
- Computational mechanics [ 0178-7675 ] ; 2020.
Abstract
A key strategy to prevent a local outbreak during the COVID-19 pandemic is to restrict incoming travel. Once a region has successfully contained the disease, it becomes critical to decide when and how to reopen the borders. Here we explore the impact of border reopening for the example of Newfoundland and Labrador, a Canadian province that has enjoyed no new cases since late April, 2020. We combine a network epidemiology model with machine learning to infer parameters and predict the COVID-19 dynamics upon partial and total airport reopening, with perfect and imperfect quarantine conditions. Our study suggests that upon full reopening, every other day, a new COVID-19 case would enter the province. Under the current conditions, banning air travel from outside Canada is more efficient in managing the pandemic than fully reopening and quarantining 95% of the incoming population. Our study provides quantitative insights of the efficacy of travel restrictions and can inform political decision making in the controversy of reopening.
DOI: 10.1007/s00466-020-01899-x
PubMed: 32904431
PubMed Central: PMC7456209
Affiliations:
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Once a region has successfully contained the disease, it becomes critical to decide when and how to reopen the borders. Here we explore the impact of border reopening for the example of Newfoundland and Labrador, a Canadian province that has enjoyed no new cases since late April, 2020. We combine a network epidemiology model with machine learning to infer parameters and predict the COVID-19 dynamics upon partial and total airport reopening, with perfect and imperfect quarantine conditions. Our study suggests that upon full reopening, every other day, a new COVID-19 case would enter the province. Under the current conditions, banning air travel from outside Canada is more efficient in managing the pandemic than fully reopening and quarantining 95% of the incoming population. Our study provides quantitative insights of the efficacy of travel restrictions and can inform political decision making in the controversy of reopening.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32904431</PMID><DateRevised><Year>2020</Year><Month>12</Month><Day>18</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0178-7675</ISSN><JournalIssue CitedMedium="Print"><PubDate><Year>2020</Year><Month>Aug</Month><Day>29</Day></PubDate></JournalIssue><Title>Computational mechanics</Title><ISOAbbreviation>Comput Mech</ISOAbbreviation></Journal><ArticleTitle>Is it safe to lift COVID-19 travel bans? The Newfoundland story.</ArticleTitle><Pagination><MedlinePgn>1-12</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s00466-020-01899-x</ELocationID><Abstract><AbstractText>A key strategy to prevent a local outbreak during the COVID-19 pandemic is to restrict incoming travel. Once a region has successfully contained the disease, it becomes critical to decide when and how to reopen the borders. Here we explore the impact of border reopening for the example of Newfoundland and Labrador, a Canadian province that has enjoyed no new cases since late April, 2020. We combine a network epidemiology model with machine learning to infer parameters and predict the COVID-19 dynamics upon partial and total airport reopening, with perfect and imperfect quarantine conditions. Our study suggests that upon full reopening, every other day, a new COVID-19 case would enter the province. Under the current conditions, banning air travel from outside Canada is more efficient in managing the pandemic than fully reopening and quarantining 95% of the incoming population. Our study provides quantitative insights of the efficacy of travel restrictions and can inform political decision making in the controversy of reopening.</AbstractText><CopyrightInformation>© Springer-Verlag GmbH Germany, part of Springer Nature 2020.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Linka</LastName><ForeName>Kevin</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Mechanical Engineering, Stanford University, Stanford, CA USA.</Affiliation><Identifier Source="GRID">grid.168010.e</Identifier><Identifier Source="ISNI">0000000419368956</Identifier></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rahman</LastName><ForeName>Proton</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Department of Medicine, Memorial University of Newfoundland, St. John's, Canada.</Affiliation><Identifier Source="GRID">grid.25055.37</Identifier><Identifier Source="ISNI">0000 0000 9130 6822</Identifier></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Goriely</LastName><ForeName>Alain</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Mathematical Institute, University of Oxford, Oxford, UK.</Affiliation><Identifier Source="GRID">grid.4991.5</Identifier><Identifier Source="ISNI">0000 0004 1936 8948</Identifier></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kuhl</LastName><ForeName>Ellen</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Department of Mechanical Engineering, Stanford University, Stanford, CA USA.</Affiliation><Identifier Source="GRID">grid.168010.e</Identifier><Identifier Source="ISNI">0000000419368956</Identifier></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>08</Month><Day>29</Day></ArticleDate></Article><MedlineJournalInfo><Country>Germany</Country><MedlineTA>Comput Mech</MedlineTA><NlmUniqueID>9883520</NlmUniqueID><ISSNLinking>0178-7675</ISSNLinking></MedlineJournalInfo><CommentsCorrectionsList><CommentsCorrections RefType="UpdateOf"><RefSource>medRxiv. 2020 Aug 02;:</RefSource><PMID Version="1">32766595</PMID></CommentsCorrections></CommentsCorrectionsList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Epidemiology</Keyword><Keyword MajorTopicYN="N">Machine learning</Keyword><Keyword MajorTopicYN="N">Reproduction number</Keyword><Keyword MajorTopicYN="N">SEIR model</Keyword></KeywordList><CoiStatement>Conflict of interestThe authors declare that they have no conflict of interest.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>07</Month><Day>16</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>08</Month><Day>03</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>9</Month><Day>9</Day><Hour>18</Hour><Minute>4</Minute></PubMedPubDate><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>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32904431</ArticleId><ArticleId IdType="doi">10.1007/s00466-020-01899-x</ArticleId><ArticleId IdType="pii">1899</ArticleId><ArticleId IdType="pmc">PMC7456209</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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