Estimation of incubation period distribution of COVID-19 using disease onset forward time: A novel cross-sectional and forward follow-up study.
Identifieur interne : 000591 ( Main/Exploration ); précédent : 000590; suivant : 000592Estimation of incubation period distribution of COVID-19 using disease onset forward time: A novel cross-sectional and forward follow-up study.
Auteurs : Jing Qin [États-Unis] ; Chong You [République populaire de Chine] ; Qiushi Lin [République populaire de Chine] ; Taojun Hu [République populaire de Chine] ; Shicheng Yu [République populaire de Chine] ; Xiao-Hua Zhou [République populaire de Chine]Source :
- Science advances [ 2375-2548 ] ; 2020.
Descripteurs français
- KwdFr :
- Adolescent (MeSH), Adulte (MeSH), Adulte d'âge moyen (MeSH), Betacoronavirus (MeSH), Chine (épidémiologie), Enfant (MeSH), Enfant d'âge préscolaire (MeSH), Femelle (MeSH), Humains (MeSH), Infections à coronavirus (transmission), Infections à coronavirus (virologie), Infections à coronavirus (épidémiologie), Jeune adulte (MeSH), Modèles statistiques (MeSH), Mâle (MeSH), Nourrisson (MeSH), Nouveau-né (MeSH), Pandémies (MeSH), Pneumopathie virale (transmission), Pneumopathie virale (virologie), Pneumopathie virale (épidémiologie), Période d'incubation de la maladie infectieuse (MeSH), Sujet âgé (MeSH), Sujet âgé de 80 ans ou plus (MeSH), Études de suivi (MeSH), Études transversales (MeSH).
- MESH :
- virologie : Infections à coronavirus, Pneumopathie virale.
- épidémiologie : Chine, Infections à coronavirus, Pneumopathie virale.
- Adolescent, Adulte, Adulte d'âge moyen, Betacoronavirus, Enfant, Enfant d'âge préscolaire, Femelle, Humains, Jeune adulte, Modèles statistiques, Mâle, Nourrisson, Nouveau-né, Pandémies, Période d'incubation de la maladie infectieuse, Sujet âgé, Sujet âgé de 80 ans ou plus, Études de suivi, Études transversales.
- Wicri :
- geographic : République populaire de Chine.
English descriptors
- KwdEn :
- Adolescent (MeSH), Adult (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Betacoronavirus (MeSH), Child (MeSH), Child, Preschool (MeSH), China (epidemiology), Coronavirus Infections (epidemiology), Coronavirus Infections (transmission), Coronavirus Infections (virology), Cross-Sectional Studies (MeSH), Female (MeSH), Follow-Up Studies (MeSH), Humans (MeSH), Infant (MeSH), Infant, Newborn (MeSH), Infectious Disease Incubation Period (MeSH), Male (MeSH), Middle Aged (MeSH), Models, Statistical (MeSH), Pandemics (MeSH), Pneumonia, Viral (epidemiology), Pneumonia, Viral (transmission), Pneumonia, Viral (virology), Young Adult (MeSH).
- MESH :
- geographic , epidemiology : China.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- virology : Coronavirus Infections, Pneumonia, Viral.
- Adolescent, Adult, Aged, Aged, 80 and over, Betacoronavirus, Child, Child, Preschool, Cross-Sectional Studies, Female, Follow-Up Studies, Humans, Infant, Infant, Newborn, Infectious Disease Incubation Period, Male, Middle Aged, Models, Statistical, Pandemics, Young Adult.
Abstract
We have proposed a novel, accurate low-cost method to estimate the incubation-period distribution of COVID-19 by conducting a cross-sectional and forward follow-up study. We identified those presymptomatic individuals at their time of departure from Wuhan and followed them until the development of symptoms. The renewal process was adopted by considering the incubation period as a renewal and the duration between departure and symptoms onset as a forward time. Such a method enhances the accuracy of estimation by reducing recall bias and using the readily available data. The estimated median incubation period was 7.76 days [95% confidence interval (CI): 7.02 to 8.53], and the 90th percentile was 14.28 days (95% CI: 13.64 to 14.90). By including the possibility that a small portion of patients may contract the disease on their way out of Wuhan, the estimated probability that the incubation period is longer than 14 days was between 5 and 10%.
DOI: 10.1126/sciadv.abc1202
PubMed: 32851189
PubMed Central: PMC7428324
Affiliations:
Links toward previous steps (curation, corpus...)
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first="Qiushi" last="Lin">Qiushi Lin</name><affiliation wicri:level="4"><nlm:affiliation>Beijing International Center for Mathematical Research, Peking University, 100871, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing International Center for Mathematical Research, Peking University, 100871</wicri:regionArea><orgName type="university">Université de Pékin</orgName><placeName><settlement type="city">Pékin</settlement><region type="capitale">Pékin</region></placeName></affiliation></author><author><name sortKey="Hu, Taojun" sort="Hu, Taojun" uniqKey="Hu T" first="Taojun" last="Hu">Taojun Hu</name><affiliation wicri:level="4"><nlm:affiliation>School of Mathematical Sciences, Peking University, 100871, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Mathematical Sciences, Peking University, 100871</wicri:regionArea><orgName type="university">Université de 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(MeSH)</term><term>Female (MeSH)</term><term>Follow-Up Studies (MeSH)</term><term>Humans (MeSH)</term><term>Infant (MeSH)</term><term>Infant, Newborn (MeSH)</term><term>Infectious Disease Incubation Period (MeSH)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Models, Statistical (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (epidemiology)</term><term>Pneumonia, Viral (transmission)</term><term>Pneumonia, Viral (virology)</term><term>Young Adult (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent (MeSH)</term><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Chine (épidémiologie)</term><term>Enfant (MeSH)</term><term>Enfant d'âge préscolaire (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Infections à coronavirus (transmission)</term><term>Infections à coronavirus (virologie)</term><term>Infections à coronavirus (épidémiologie)</term><term>Jeune adulte (MeSH)</term><term>Modèles statistiques (MeSH)</term><term>Mâle (MeSH)</term><term>Nourrisson (MeSH)</term><term>Nouveau-né (MeSH)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (transmission)</term><term>Pneumopathie virale (virologie)</term><term>Pneumopathie virale (épidémiologie)</term><term>Période d'incubation de la maladie infectieuse (MeSH)</term><term>Sujet âgé (MeSH)</term><term>Sujet âgé de 80 ans ou plus (MeSH)</term><term>Études de suivi (MeSH)</term><term>Études transversales (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>China</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" 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We identified those presymptomatic individuals at their time of departure from Wuhan and followed them until the development of symptoms. The renewal process was adopted by considering the incubation period as a renewal and the duration between departure and symptoms onset as a forward time. Such a method enhances the accuracy of estimation by reducing recall bias and using the readily available data. The estimated median incubation period was 7.76 days [95% confidence interval (CI): 7.02 to 8.53], and the 90th percentile was 14.28 days (95% CI: 13.64 to 14.90). By including the possibility that a small portion of patients may contract the disease on their way out of Wuhan, the estimated probability that the incubation period is longer than 14 days was between 5 and 10%.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32851189</PMID><DateCompleted><Year>2020</Year><Month>09</Month><Day>21</Day></DateCompleted><DateRevised><Year>2020</Year><Month>09</Month><Day>21</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">2375-2548</ISSN><JournalIssue CitedMedium="Internet"><Volume>6</Volume><Issue>33</Issue><PubDate><Year>2020</Year><Month>08</Month></PubDate></JournalIssue><Title>Science advances</Title><ISOAbbreviation>Sci Adv</ISOAbbreviation></Journal><ArticleTitle>Estimation of incubation period distribution of COVID-19 using disease onset forward time: A novel cross-sectional and forward follow-up study.</ArticleTitle><Pagination><MedlinePgn>eabc1202</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1126/sciadv.abc1202</ELocationID><Abstract><AbstractText>We have proposed a novel, accurate low-cost method to estimate the incubation-period distribution of COVID-19 by conducting a cross-sectional and forward follow-up study. We identified those presymptomatic individuals at their time of departure from Wuhan and followed them until the development of symptoms. The renewal process was adopted by considering the incubation period as a renewal and the duration between departure and symptoms onset as a forward time. Such a method enhances the accuracy of estimation by reducing recall bias and using the readily available data. The estimated median incubation period was 7.76 days [95% confidence interval (CI): 7.02 to 8.53], and the 90th percentile was 14.28 days (95% CI: 13.64 to 14.90). By including the possibility that a small portion of patients may contract the disease on their way out of Wuhan, the estimated probability that the incubation period is longer than 14 days was between 5 and 10%.</AbstractText><CopyrightInformation>Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Qin</LastName><ForeName>Jing</ForeName><Initials>J</Initials><Identifier Source="ORCID">0000-0003-2817-6326</Identifier><AffiliationInfo><Affiliation>Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>You</LastName><ForeName>Chong</ForeName><Initials>C</Initials><Identifier Source="ORCID">0000-0003-1309-9620</Identifier><AffiliationInfo><Affiliation>Beijing International Center for Mathematical Research, Peking University, 100871, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lin</LastName><ForeName>Qiushi</ForeName><Initials>Q</Initials><AffiliationInfo><Affiliation>Beijing International Center for Mathematical Research, Peking University, 100871, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hu</LastName><ForeName>Taojun</ForeName><Initials>T</Initials><Identifier Source="ORCID">0000-0003-4699-121X</Identifier><AffiliationInfo><Affiliation>School of Mathematical Sciences, Peking University, 100871, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yu</LastName><ForeName>Shicheng</ForeName><Initials>S</Initials><Identifier Source="ORCID">0000-0003-4617-3065</Identifier><AffiliationInfo><Affiliation>Office for Epidemiology, Chinese Center for Disease Control and Prevention, 102206, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhou</LastName><ForeName>Xiao-Hua</ForeName><Initials>XH</Initials><Identifier Source="ORCID">0000-0001-7935-1222</Identifier><AffiliationInfo><Affiliation>Beijing International Center for Mathematical Research, Peking University, 100871, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biostatistics, School of Public Health, Peking University, 100871, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Statistical Science, Peking University, 100871, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>08</Month><Day>14</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Sci Adv</MedlineTA><NlmUniqueID>101653440</NlmUniqueID><ISSNLinking>2375-2548</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName><SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome 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sortKey="Hu, Taojun" sort="Hu, Taojun" uniqKey="Hu T" first="Taojun" last="Hu">Taojun Hu</name><name sortKey="Lin, Qiushi" sort="Lin, Qiushi" uniqKey="Lin Q" first="Qiushi" last="Lin">Qiushi Lin</name><name sortKey="Yu, Shicheng" sort="Yu, Shicheng" uniqKey="Yu S" first="Shicheng" last="Yu">Shicheng Yu</name><name sortKey="Zhou, Xiao Hua" sort="Zhou, Xiao Hua" uniqKey="Zhou X" first="Xiao-Hua" last="Zhou">Xiao-Hua Zhou</name><name sortKey="Zhou, Xiao Hua" sort="Zhou, Xiao Hua" uniqKey="Zhou X" first="Xiao-Hua" last="Zhou">Xiao-Hua Zhou</name><name sortKey="Zhou, Xiao Hua" sort="Zhou, Xiao Hua" uniqKey="Zhou X" first="Xiao-Hua" last="Zhou">Xiao-Hua Zhou</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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