Outbreak dynamics of COVID-19 in China and the United States.
Identifieur interne : 000421 ( Main/Exploration ); précédent : 000420; suivant : 000422Outbreak dynamics of COVID-19 in China and the United States.
Auteurs : Mathias Peirlinck [États-Unis] ; Kevin Linka [États-Unis] ; Francisco Sahli Costabal [Chili] ; Ellen Kuhl [États-Unis]Source :
- Biomechanics and modeling in mechanobiology [ 1617-7940 ] ; 2020.
Descripteurs français
- KwdFr :
- Betacoronavirus (MeSH), Chine (épidémiologie), Géographie (MeSH), Humains (MeSH), Infections à coronavirus (prévention et contrôle), Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Modèles théoriques (MeSH), Pandémies (MeSH), Pneumopathie virale (transmission), Pneumopathie virale (épidémiologie), Taux de reproduction de base (MeSH), Vaccins antiviraux (MeSH), États-Unis (épidémiologie).
- MESH :
- prévention et contrôle : Infections à coronavirus.
- épidémiologie : Chine, Infections à coronavirus, Pneumopathie virale, États-Unis.
- Betacoronavirus, Géographie, Humains, Modèles théoriques, Pandémies, Taux de reproduction de base, Vaccins antiviraux.
- Wicri :
- geographic : République populaire de Chine, États-Unis.
English descriptors
- KwdEn :
- Basic Reproduction Number (MeSH), Betacoronavirus (MeSH), COVID-19 (MeSH), COVID-19 Vaccines (MeSH), China (epidemiology), Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Coronavirus Infections (transmission), Geography (MeSH), Humans (MeSH), Models, Theoretical (MeSH), Pandemics (MeSH), Pneumonia, Viral (epidemiology), Pneumonia, Viral (transmission), SARS-CoV-2 (MeSH), United States (epidemiology), Viral Vaccines (MeSH).
- MESH :
- chemical : COVID-19 Vaccines, Viral Vaccines.
- geographic , epidemiology : China, United States.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- prevention & control : Coronavirus Infections.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- Basic Reproduction Number, Betacoronavirus, COVID-19, Geography, Humans, Models, Theoretical, Pandemics, SARS-CoV-2.
Abstract
On March 11, 2020, the World Health Organization declared the coronavirus disease 2019, COVID-19, a global pandemic. In an unprecedented collective effort, massive amounts of data are now being collected worldwide to estimate the immediate and long-term impact of this pandemic on the health system and the global economy. However, the precise timeline of the disease, its transmissibility, and the effect of mitigation strategies remain incompletely understood. Here we integrate a global network model with a local epidemic SEIR model to quantify the outbreak dynamics of COVID-19 in China and the United States. For the outbreak in China, in [Formula: see text] provinces, we found a latent period of 2.56 ± 0.72 days, a contact period of 1.47 ± 0.32 days, and an infectious period of 17.82 ± 2.95 days. We postulate that the latent and infectious periods are disease-specific, whereas the contact period is behavior-specific and can vary between different provinces, states, or countries. For the early stages of the outbreak in the United States, in [Formula: see text] states, we adopted the disease-specific values from China and found a contact period of 3.38 ± 0.69 days. Our network model predicts that-without the massive political mitigation strategies that are in place today-the United States would have faced a basic reproduction number of 5.30 ± 0.95 and a nationwide peak of the outbreak on May 10, 2020 with 3 million infections. Our results demonstrate how mathematical modeling can help estimate outbreak dynamics and provide decision guidelines for successful outbreak control. We anticipate that our model will become a valuable tool to estimate the potential of vaccination and quantify the effect of relaxing political measures including total lockdown, shelter in place, and travel restrictions for low-risk subgroups of the population or for the population as a whole.
DOI: 10.1007/s10237-020-01332-5
PubMed: 32342242
PubMed Central: PMC7185268
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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(Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Sahli Costabal, Francisco" sort="Sahli Costabal, Francisco" uniqKey="Sahli Costabal F" first="Francisco" last="Sahli Costabal">Francisco Sahli Costabal</name><affiliation wicri:level="1"><nlm:affiliation>Department of Mechanical and Metallurgical Engineering, School of Engineering and Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Catolica de Chile, Santiago, Chile.</nlm:affiliation><country xml:lang="fr">Chili</country><wicri:regionArea>Department of Mechanical and Metallurgical Engineering, School of Engineering and Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Catolica de Chile, 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type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Basic Reproduction Number (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>COVID-19 (MeSH)</term><term>COVID-19 Vaccines (MeSH)</term><term>China (epidemiology)</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (prevention & control)</term><term>Coronavirus Infections (transmission)</term><term>Geography (MeSH)</term><term>Humans (MeSH)</term><term>Models, Theoretical (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (epidemiology)</term><term>Pneumonia, Viral (transmission)</term><term>SARS-CoV-2 (MeSH)</term><term>United States (epidemiology)</term><term>Viral Vaccines (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Betacoronavirus (MeSH)</term><term>Chine (épidémiologie)</term><term>Géographie (MeSH)</term><term>Humains (MeSH)</term><term>Infections à coronavirus (prévention et contrôle)</term><term>Infections à coronavirus (transmission)</term><term>Infections à coronavirus (épidémiologie)</term><term>Modèles théoriques (MeSH)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (transmission)</term><term>Pneumopathie virale (épidémiologie)</term><term>Taux de reproduction de base (MeSH)</term><term>Vaccins antiviraux (MeSH)</term><term>États-Unis (épidémiologie)</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>COVID-19 Vaccines</term><term>Viral Vaccines</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>China</term><term>United States</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></keywords><keywords scheme="MESH" 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populaire de Chine</term><term>États-Unis</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">On March 11, 2020, the World Health Organization declared the coronavirus disease 2019, COVID-19, a global pandemic. In an unprecedented collective effort, massive amounts of data are now being collected worldwide to estimate the immediate and long-term impact of this pandemic on the health system and the global economy. However, the precise timeline of the disease, its transmissibility, and the effect of mitigation strategies remain incompletely understood. Here we integrate a global network model with a local epidemic SEIR model to quantify the outbreak dynamics of COVID-19 in China and the United States. For the outbreak in China, in [Formula: see text] provinces, we found a latent period of 2.56 ± 0.72 days, a contact period of 1.47 ± 0.32 days, and an infectious period of 17.82 ± 2.95 days. We postulate that the latent and infectious periods are disease-specific, whereas the contact period is behavior-specific and can vary between different provinces, states, or countries. For the early stages of the outbreak in the United States, in [Formula: see text] states, we adopted the disease-specific values from China and found a contact period of 3.38 ± 0.69 days. Our network model predicts that-without the massive political mitigation strategies that are in place today-the United States would have faced a basic reproduction number of 5.30 ± 0.95 and a nationwide peak of the outbreak on May 10, 2020 with 3 million infections. Our results demonstrate how mathematical modeling can help estimate outbreak dynamics and provide decision guidelines for successful outbreak control. We anticipate that our model will become a valuable tool to estimate the potential of vaccination and quantify the effect of relaxing political measures including total lockdown, shelter in place, and travel restrictions for low-risk subgroups of the population or for the population as a whole.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32342242</PMID><DateCompleted><Year>2020</Year><Month>11</Month><Day>16</Day></DateCompleted><DateRevised><Year>2021</Year><Month>01</Month><Day>10</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1617-7940</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>6</Issue><PubDate><Year>2020</Year><Month>Dec</Month></PubDate></JournalIssue><Title>Biomechanics and modeling in mechanobiology</Title><ISOAbbreviation>Biomech Model Mechanobiol</ISOAbbreviation></Journal><ArticleTitle>Outbreak dynamics of COVID-19 in China and the United States.</ArticleTitle><Pagination><MedlinePgn>2179-2193</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s10237-020-01332-5</ELocationID><Abstract><AbstractText>On March 11, 2020, the World Health Organization declared the coronavirus disease 2019, COVID-19, a global pandemic. In an unprecedented collective effort, massive amounts of data are now being collected worldwide to estimate the immediate and long-term impact of this pandemic on the health system and the global economy. However, the precise timeline of the disease, its transmissibility, and the effect of mitigation strategies remain incompletely understood. Here we integrate a global network model with a local epidemic SEIR model to quantify the outbreak dynamics of COVID-19 in China and the United States. For the outbreak in China, in [Formula: see text] provinces, we found a latent period of 2.56 ± 0.72 days, a contact period of 1.47 ± 0.32 days, and an infectious period of 17.82 ± 2.95 days. We postulate that the latent and infectious periods are disease-specific, whereas the contact period is behavior-specific and can vary between different provinces, states, or countries. For the early stages of the outbreak in the United States, in [Formula: see text] states, we adopted the disease-specific values from China and found a contact period of 3.38 ± 0.69 days. Our network model predicts that-without the massive political mitigation strategies that are in place today-the United States would have faced a basic reproduction number of 5.30 ± 0.95 and a nationwide peak of the outbreak on May 10, 2020 with 3 million infections. Our results demonstrate how mathematical modeling can help estimate outbreak dynamics and provide decision guidelines for successful outbreak control. We anticipate that our model will become a valuable tool to estimate the potential of vaccination and quantify the effect of relaxing political measures including total lockdown, shelter in place, and travel restrictions for low-risk subgroups of the population or for the population as a whole.</AbstractText></Abstract><AuthorList CompleteYN="Y"><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>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>Sahli Costabal</LastName><ForeName>Francisco</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Department of Mechanical and Metallurgical Engineering, School of Engineering and 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><Identifier Source="ORCID">http://orcid.org/0000-0002-6283-935X</Identifier><AffiliationInfo><Affiliation>Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, CA, USA. ekuhl@stanford.edu.</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>04</Month><Day>27</Day></ArticleDate></Article><MedlineJournalInfo><Country>Germany</Country><MedlineTA>Biomech Model Mechanobiol</MedlineTA><NlmUniqueID>101135325</NlmUniqueID><ISSNLinking>1617-7940</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000086663">COVID-19 Vaccines</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D014765">Viral Vaccines</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><CitationSubset>S</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D050936" MajorTopicYN="N">Basic Reproduction Number</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086663" MajorTopicYN="N">COVID-19 Vaccines</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName><QualifierName UI="Q000635" MajorTopicYN="Y">transmission</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005843" MajorTopicYN="N">Geography</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="N">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000635" MajorTopicYN="Y">transmission</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000086402" MajorTopicYN="N">SARS-CoV-2</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014481" MajorTopicYN="N" Type="Geographic">United States</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014765" MajorTopicYN="N">Viral Vaccines</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Coronavirus</Keyword><Keyword MajorTopicYN="N">Epidemiology modeling</Keyword><Keyword 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Stanford</li></orgName></list><tree><country name="États-Unis"><region name="Californie"><name sortKey="Peirlinck, Mathias" sort="Peirlinck, Mathias" uniqKey="Peirlinck M" first="Mathias" last="Peirlinck">Mathias Peirlinck</name></region><name sortKey="Kuhl, Ellen" sort="Kuhl, Ellen" uniqKey="Kuhl E" first="Ellen" last="Kuhl">Ellen Kuhl</name><name sortKey="Linka, Kevin" sort="Linka, Kevin" uniqKey="Linka K" first="Kevin" last="Linka">Kevin Linka</name></country><country name="Chili"><noRegion><name sortKey="Sahli Costabal, Francisco" sort="Sahli Costabal, Francisco" uniqKey="Sahli Costabal F" first="Francisco" last="Sahli Costabal">Francisco Sahli Costabal</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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