State-level variation of initial COVID-19 dynamics in the United States.
Identifieur interne : 000070 ( Main/Exploration ); précédent : 000069; suivant : 000071State-level variation of initial COVID-19 dynamics in the United States.
Auteurs : Easton R. White [États-Unis] ; Laurent Hébert-Dufresne [États-Unis]Source :
- PloS one [ 1932-6203 ] ; 2020.
Descripteurs français
- KwdFr :
- Betacoronavirus (MeSH), Densité de population (MeSH), Facteurs temps (MeSH), Humains (MeSH), Infections à coronavirus (prévention et contrôle), Infections à coronavirus (transmission), Infections à coronavirus (virologie), Infections à coronavirus (épidémiologie), Modèles linéaires (MeSH), Pandémies (prévention et contrôle), Pneumopathie virale (prévention et contrôle), Pneumopathie virale (transmission), Pneumopathie virale (virologie), Pneumopathie virale (épidémiologie), Population rurale (MeSH), Prévention des infections (méthodes), Revenu (MeSH), Sujet âgé (MeSH), Sujet âgé de 80 ans ou plus (MeSH), Surveillance épidémiologique (MeSH), Vaccination (MeSH), Vaccins antigrippaux (MeSH), États-Unis (épidémiologie).
- MESH :
- méthodes : Prévention des infections.
- prévention et contrôle : Infections à coronavirus, Pandémies, Pneumopathie virale.
- virologie : Infections à coronavirus, Pneumopathie virale.
- épidémiologie : Infections à coronavirus, Pneumopathie virale, États-Unis.
- Betacoronavirus, Densité de population, Facteurs temps, Humains, Modèles linéaires, Population rurale, Revenu, Sujet âgé, Sujet âgé de 80 ans ou plus, Surveillance épidémiologique, Vaccination, Vaccins antigrippaux.
- Wicri :
- geographic : États-Unis.
English descriptors
- KwdEn :
- Aged (MeSH), Aged, 80 and over (MeSH), Betacoronavirus (MeSH), COVID-19 (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Coronavirus Infections (transmission), Coronavirus Infections (virology), Epidemiological Monitoring (MeSH), Humans (MeSH), Income (MeSH), Infection Control (methods), Influenza Vaccines (MeSH), Linear Models (MeSH), Pandemics (prevention & control), Pneumonia, Viral (epidemiology), Pneumonia, Viral (prevention & control), Pneumonia, Viral (transmission), Pneumonia, Viral (virology), Population Density (MeSH), Rural Population (MeSH), SARS-CoV-2 (MeSH), Time Factors (MeSH), United States (epidemiology), Vaccination (MeSH).
- MESH :
- chemical : Influenza Vaccines.
- geographic , epidemiology : United States.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- methods : Infection Control.
- prevention & control : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- virology : Coronavirus Infections, Pneumonia, Viral.
- Aged, Aged, 80 and over, Betacoronavirus, COVID-19, Epidemiological Monitoring, Humans, Income, Linear Models, Population Density, Rural Population, SARS-CoV-2, Time Factors, Vaccination.
Abstract
During an epidemic, metrics such as R0, doubling time, and case fatality rates are important in understanding and predicting the course of an epidemic. However, if collected over country or regional scales, these metrics hide important smaller-scale, local dynamics. We examine how commonly used epidemiological metrics differ for each individual state within the United States during the initial COVID-19 outbreak. We found that the detected case number and trajectory of early detected cases differ considerably between states. We then test for correlations with testing protocols, interventions and population characteristics. We find that epidemic dynamics were most strongly associated with non-pharmaceutical government actions during the early phase of the epidemic. In particular, early social distancing restrictions, particularly on restaurant operations, was correlated with increased doubling times. Interestingly, we also found that states with little tolerance for deviance from enforced rules saw faster early epidemic growth. Together with other correlates such as population density, our results highlight the different factors involved in the heterogeneity in the early spread of COVID-19 throughout the United States. Although individual states are clearly not independent, they can serve as small, natural experiments in how different demographic patterns and government responses can impact the course of an epidemic.
DOI: 10.1371/journal.pone.0240648
PubMed: 33048967
PubMed Central: PMC7553297
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">State-level variation of initial COVID-19 dynamics in the United States.</title><author><name sortKey="White, Easton R" sort="White, Easton R" uniqKey="White E" first="Easton R" last="White">Easton R. White</name><affiliation wicri:level="2"><nlm:affiliation>Department of Biology, University of Vermont, Burlington, VT, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biology, University of Vermont, Burlington, VT</wicri:regionArea><placeName><region type="state">Vermont</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>Gund Institute for Environment, University of Vermont, Burlington, VT, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Gund Institute for Environment, University of Vermont, Burlington, VT</wicri:regionArea><placeName><region type="state">Vermont</region></placeName></affiliation></author><author><name sortKey="Hebert Dufresne, Laurent" sort="Hebert Dufresne, Laurent" uniqKey="Hebert Dufresne L" first="Laurent" last="Hébert-Dufresne">Laurent Hébert-Dufresne</name><affiliation wicri:level="2"><nlm:affiliation>Department of Computer Science, University of Vermont, Burlington, VT, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, University of Vermont, Burlington, VT</wicri:regionArea><placeName><region type="state">Vermont</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Vermont Complex Systems Center, University of Vermont, Burlington, VT</wicri:regionArea><placeName><region type="state">Vermont</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:33048967</idno><idno type="pmid">33048967</idno><idno type="doi">10.1371/journal.pone.0240648</idno><idno type="pmc">PMC7553297</idno><idno type="wicri:Area/Main/Corpus">000080</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000080</idno><idno type="wicri:Area/Main/Curation">000080</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000080</idno><idno type="wicri:Area/Main/Exploration">000080</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">State-level variation of initial COVID-19 dynamics in the United States.</title><author><name sortKey="White, Easton R" sort="White, Easton R" uniqKey="White E" first="Easton R" last="White">Easton R. White</name><affiliation wicri:level="2"><nlm:affiliation>Department of Biology, University of Vermont, Burlington, VT, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biology, University of Vermont, Burlington, VT</wicri:regionArea><placeName><region type="state">Vermont</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>Gund Institute for Environment, University of Vermont, Burlington, VT, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Gund Institute for Environment, University of Vermont, Burlington, VT</wicri:regionArea><placeName><region type="state">Vermont</region></placeName></affiliation></author><author><name sortKey="Hebert Dufresne, Laurent" sort="Hebert Dufresne, Laurent" uniqKey="Hebert Dufresne L" first="Laurent" last="Hébert-Dufresne">Laurent Hébert-Dufresne</name><affiliation wicri:level="2"><nlm:affiliation>Department of Computer Science, University of Vermont, Burlington, VT, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, University of Vermont, Burlington, VT</wicri:regionArea><placeName><region type="state">Vermont</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Vermont Complex Systems Center, University of Vermont, Burlington, VT</wicri:regionArea><placeName><region type="state">Vermont</region></placeName></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged (MeSH)</term><term>Aged, 80 and over (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>COVID-19 (MeSH)</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (prevention & control)</term><term>Coronavirus Infections (transmission)</term><term>Coronavirus Infections (virology)</term><term>Epidemiological Monitoring (MeSH)</term><term>Humans (MeSH)</term><term>Income (MeSH)</term><term>Infection Control (methods)</term><term>Influenza Vaccines (MeSH)</term><term>Linear Models (MeSH)</term><term>Pandemics (prevention & control)</term><term>Pneumonia, Viral (epidemiology)</term><term>Pneumonia, Viral (prevention & control)</term><term>Pneumonia, Viral (transmission)</term><term>Pneumonia, Viral (virology)</term><term>Population Density (MeSH)</term><term>Rural Population (MeSH)</term><term>SARS-CoV-2 (MeSH)</term><term>Time Factors (MeSH)</term><term>United States (epidemiology)</term><term>Vaccination (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Betacoronavirus (MeSH)</term><term>Densité de population (MeSH)</term><term>Facteurs temps (MeSH)</term><term>Humains (MeSH)</term><term>Infections à coronavirus (prévention et contrôle)</term><term>Infections à coronavirus (transmission)</term><term>Infections à coronavirus (virologie)</term><term>Infections à coronavirus (épidémiologie)</term><term>Modèles linéaires (MeSH)</term><term>Pandémies (prévention et contrôle)</term><term>Pneumopathie virale (prévention et contrôle)</term><term>Pneumopathie virale (transmission)</term><term>Pneumopathie virale (virologie)</term><term>Pneumopathie virale (épidémiologie)</term><term>Population rurale (MeSH)</term><term>Prévention des infections (méthodes)</term><term>Revenu (MeSH)</term><term>Sujet âgé (MeSH)</term><term>Sujet âgé de 80 ans ou plus (MeSH)</term><term>Surveillance épidémiologique (MeSH)</term><term>Vaccination (MeSH)</term><term>Vaccins antigrippaux (MeSH)</term><term>États-Unis (épidémiologie)</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Influenza Vaccines</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><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="methods" xml:lang="en"><term>Infection Control</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Prévention des infections</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 scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term><term>États-Unis</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Aged, 80 and over</term><term>Betacoronavirus</term><term>COVID-19</term><term>Epidemiological Monitoring</term><term>Humans</term><term>Income</term><term>Linear Models</term><term>Population Density</term><term>Rural Population</term><term>SARS-CoV-2</term><term>Time Factors</term><term>Vaccination</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Betacoronavirus</term><term>Densité de population</term><term>Facteurs temps</term><term>Humains</term><term>Modèles linéaires</term><term>Population rurale</term><term>Revenu</term><term>Sujet âgé</term><term>Sujet âgé de 80 ans ou plus</term><term>Surveillance épidémiologique</term><term>Vaccination</term><term>Vaccins antigrippaux</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>États-Unis</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">During an epidemic, metrics such as R0, doubling time, and case fatality rates are important in understanding and predicting the course of an epidemic. However, if collected over country or regional scales, these metrics hide important smaller-scale, local dynamics. We examine how commonly used epidemiological metrics differ for each individual state within the United States during the initial COVID-19 outbreak. We found that the detected case number and trajectory of early detected cases differ considerably between states. We then test for correlations with testing protocols, interventions and population characteristics. We find that epidemic dynamics were most strongly associated with non-pharmaceutical government actions during the early phase of the epidemic. In particular, early social distancing restrictions, particularly on restaurant operations, was correlated with increased doubling times. Interestingly, we also found that states with little tolerance for deviance from enforced rules saw faster early epidemic growth. Together with other correlates such as population density, our results highlight the different factors involved in the heterogeneity in the early spread of COVID-19 throughout the United States. Although individual states are clearly not independent, they can serve as small, natural experiments in how different demographic patterns and government responses can impact the course of an epidemic.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">33048967</PMID><DateCompleted><Year>2020</Year><Month>11</Month><Day>02</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>10</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>15</Volume><Issue>10</Issue><PubDate><Year>2020</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS One</ISOAbbreviation></Journal><ArticleTitle>State-level variation of initial COVID-19 dynamics in the United States.</ArticleTitle><Pagination><MedlinePgn>e0240648</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0240648</ELocationID><Abstract><AbstractText>During an epidemic, metrics such as R0, doubling time, and case fatality rates are important in understanding and predicting the course of an epidemic. However, if collected over country or regional scales, these metrics hide important smaller-scale, local dynamics. We examine how commonly used epidemiological metrics differ for each individual state within the United States during the initial COVID-19 outbreak. We found that the detected case number and trajectory of early detected cases differ considerably between states. We then test for correlations with testing protocols, interventions and population characteristics. We find that epidemic dynamics were most strongly associated with non-pharmaceutical government actions during the early phase of the epidemic. In particular, early social distancing restrictions, particularly on restaurant operations, was correlated with increased doubling times. Interestingly, we also found that states with little tolerance for deviance from enforced rules saw faster early epidemic growth. Together with other correlates such as population density, our results highlight the different factors involved in the heterogeneity in the early spread of COVID-19 throughout the United States. Although individual states are clearly not independent, they can serve as small, natural experiments in how different demographic patterns and government responses can impact the course of an epidemic.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>White</LastName><ForeName>Easton R</ForeName><Initials>ER</Initials><Identifier Source="ORCID">0000-0002-0768-9555</Identifier><AffiliationInfo><Affiliation>Department of Biology, University of Vermont, Burlington, VT, United States of America.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Gund Institute for Environment, University of Vermont, Burlington, VT, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hébert-Dufresne</LastName><ForeName>Laurent</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Computer Science, University of Vermont, Burlington, VT, United States of America.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States of America.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>1P20 GM125498-01</GrantID><Acronym>NH</Acronym><Agency>NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>10</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D007252">Influenza Vaccines</NameOfSubstance></Chemical></ChemicalList><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName><SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName></SupplMeshList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="Y">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName><QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062665" MajorTopicYN="Y">Epidemiological Monitoring</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007182" MajorTopicYN="N">Income</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017053" MajorTopicYN="N">Infection Control</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007252" MajorTopicYN="N">Influenza Vaccines</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016014" MajorTopicYN="N">Linear Models</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName><QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011156" MajorTopicYN="N">Population Density</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012424" MajorTopicYN="N">Rural Population</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086402" MajorTopicYN="N">SARS-CoV-2</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013997" MajorTopicYN="N">Time Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014481" MajorTopicYN="N" Type="Geographic">United States</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014611" MajorTopicYN="N">Vaccination</DescriptorName></MeshHeading></MeshHeadingList><CoiStatement>The authors have declared that no competing interests exist.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>05</Month><Day>04</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>09</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>10</Month><Day>13</Day><Hour>17</Hour><Minute>12</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>10</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>11</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33048967</ArticleId><ArticleId IdType="doi">10.1371/journal.pone.0240648</ArticleId><ArticleId IdType="pii">PONE-D-20-13138</ArticleId><ArticleId IdType="pmc">PMC7553297</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Proc Natl Acad Sci U S A. 2017 May 30;114(22):E4334-E4343</Citation><ArticleIdList><ArticleId IdType="pubmed">28442561</ArticleId></ArticleIdList></Reference><Reference><Citation>JAMA. 2020 Sep 1;324(9):859-870</Citation><ArticleIdList><ArticleId IdType="pubmed">32745200</ArticleId></ArticleIdList></Reference><Reference><Citation>Emerg Infect Dis. 2020 Sep;26(9):</Citation><ArticleIdList><ArticleId IdType="pubmed">32516108</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Natl Acad Sci U S A. 2020 May 5;117(18):9696-9698</Citation><ArticleIdList><ArticleId IdType="pubmed">32300018</ArticleId></ArticleIdList></Reference><Reference><Citation>Theor Biol Med Model. 2014 Jan 13;11:3</Citation><ArticleIdList><ArticleId IdType="pubmed">24418011</ArticleId></ArticleIdList></Reference><Reference><Citation>JAMA. 2020 Apr 28;323(16):1547-1548</Citation><ArticleIdList><ArticleId IdType="pubmed">32186661</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2018 Oct 5;362(6410):75-79</Citation><ArticleIdList><ArticleId IdType="pubmed">30287659</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Natl Acad Sci U S A. 2014 Jun 3;111(22):7990-5</Citation><ArticleIdList><ArticleId IdType="pubmed">24843116</ArticleId></ArticleIdList></Reference><Reference><Citation>Front Med (Lausanne). 2020 Jul 03;7:384</Citation><ArticleIdList><ArticleId IdType="pubmed">32719807</ArticleId></ArticleIdList></Reference><Reference><Citation>Eur Respir J. 2020 Jul 23;56(1):</Citation><ArticleIdList><ArticleId IdType="pubmed">32482785</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2020 Sep 18;369(6510):1465-1470</Citation><ArticleIdList><ArticleId IdType="pubmed">32680881</ArticleId></ArticleIdList></Reference><Reference><Citation>J Health Polit Policy Law. 2020 Sep 16;:</Citation><ArticleIdList><ArticleId IdType="pubmed">32955556</ArticleId></ArticleIdList></Reference><Reference><Citation>BMJ Open. 2020 Aug 26;10(8):e040413</Citation><ArticleIdList><ArticleId IdType="pubmed">32847926</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2020 Mar 21;395(10228):931-934</Citation><ArticleIdList><ArticleId IdType="pubmed">32164834</ArticleId></ArticleIdList></Reference><Reference><Citation>Epidemiology (Sunnyvale). 2016 Apr;6(2):</Citation><ArticleIdList><ArticleId IdType="pubmed">27274911</ArticleId></ArticleIdList></Reference><Reference><Citation>Ecol Evol. 2012 Jul;2(7):1398-407</Citation><ArticleIdList><ArticleId IdType="pubmed">22957148</ArticleId></ArticleIdList></Reference><Reference><Citation>Naturwissenschaften. 2001 Sep;88(9):372-81</Citation><ArticleIdList><ArticleId IdType="pubmed">11688412</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet Public Health. 2020 May;5(5):e289-e296</Citation><ArticleIdList><ArticleId IdType="pubmed">32330458</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet Infect Dis. 2020 May;20(5):533-534</Citation><ArticleIdList><ArticleId IdType="pubmed">32087114</ArticleId></ArticleIdList></Reference><Reference><Citation>Front Med (Lausanne). 2020 May 22;7:247</Citation><ArticleIdList><ArticleId IdType="pubmed">32574335</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Biol Sci. 2010 Jun 22;277(1689):1907-14</Citation><ArticleIdList><ArticleId IdType="pubmed">20164099</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2001 Oct 26;294(5543):813-7</Citation><ArticleIdList><ArticleId IdType="pubmed">11679661</ArticleId></ArticleIdList></Reference><Reference><Citation>Wellcome Open Res. 2020 Apr 27;5:78</Citation><ArticleIdList><ArticleId IdType="pubmed">32518842</ArticleId></ArticleIdList></Reference><Reference><Citation>Health Aff (Millwood). 2020 Jul;39(7):1237-1246</Citation><ArticleIdList><ArticleId IdType="pubmed">32407171</ArticleId></ArticleIdList></Reference><Reference><Citation>Sci Data. 2020 Mar 24;7(1):106</Citation><ArticleIdList><ArticleId IdType="pubmed">32210236</ArticleId></ArticleIdList></Reference><Reference><Citation>Am J Trop Med Hyg. 2020 Jul;103(1):25-27</Citation><ArticleIdList><ArticleId IdType="pubmed">32383432</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2020 Apr 24;368(6489):395-400</Citation><ArticleIdList><ArticleId IdType="pubmed">32144116</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Vermont</li></region></list><tree><country name="États-Unis"><region name="Vermont"><name sortKey="White, Easton R" sort="White, Easton R" uniqKey="White E" first="Easton R" last="White">Easton R. White</name></region><name sortKey="Hebert Dufresne, Laurent" sort="Hebert Dufresne, Laurent" uniqKey="Hebert Dufresne L" first="Laurent" last="Hébert-Dufresne">Laurent Hébert-Dufresne</name><name sortKey="Hebert Dufresne, Laurent" sort="Hebert Dufresne, Laurent" uniqKey="Hebert Dufresne L" first="Laurent" last="Hébert-Dufresne">Laurent Hébert-Dufresne</name><name sortKey="White, Easton R" sort="White, Easton R" uniqKey="White E" first="Easton R" last="White">Easton R. White</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Wicri/explor/CovidPublicV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000070 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000070 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Wicri
|area= CovidPublicV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= pubmed:33048967
|texte= State-level variation of initial COVID-19 dynamics in the United States.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:33048967" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \
| NlmPubMed2Wicri -a CovidPublicV1
| This area was generated with Dilib version V0.6.38. |  |