The reproduction number of COVID-19 and its correlation with public health interventions.
Identifieur interne : 000199 ( Main/Exploration ); précédent : 000198; suivant : 000200The reproduction number of COVID-19 and its correlation with public health interventions.
Auteurs : Kevin Linka [États-Unis] ; Mathias Peirlinck [États-Unis] ; Ellen Kuhl [États-Unis]Source :
- Computational mechanics [ 0178-7675 ] ; 2020.
Abstract
Throughout the past six months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22 ± 1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67 ± 0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24 ± 2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.
DOI: 10.1007/s00466-020-01880-8
PubMed: 32836597
PubMed Central: PMC7385940
Affiliations:
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This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22 ± 1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67 ± 0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24 ± 2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32836597</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>28</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0178-7675</ISSN><JournalIssue CitedMedium="Print"><PubDate><Year>2020</Year><Month>Jul</Month><Day>28</Day></PubDate></JournalIssue><Title>Computational mechanics</Title><ISOAbbreviation>Comput Mech</ISOAbbreviation></Journal><ArticleTitle>The reproduction number of COVID-19 and its correlation with public health interventions.</ArticleTitle><Pagination><MedlinePgn>1-16</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s00466-020-01880-8</ELocationID><Abstract><AbstractText>Throughout the past six months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22 ± 1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67 ± 0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24 ± 2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.</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>Peirlinck</LastName><ForeName>Mathias</ForeName><Initials>M</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>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><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>07</Month><Day>28</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 Jul 07;:</RefSource><PMID Version="1">32676611</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></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>05</Month><Day>04</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>07</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32836597</ArticleId><ArticleId IdType="doi">10.1007/s00466-020-01880-8</ArticleId><ArticleId IdType="pii">1880</ArticleId><ArticleId IdType="pmc">PMC7385940</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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