Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies.
Identifieur interne : 000E33 ( Main/Exploration ); précédent : 000E32; suivant : 000E34Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies.
Auteurs : Qinxia Wang [États-Unis] ; Shanghong Xie [États-Unis] ; Yuanjia Wang [États-Unis] ; Donglin Zeng [États-Unis]Source :
- Frontiers in public health [ 2296-2565 ] ; 2020.
Abstract
Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict the COVID-19 disease course and compare the effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (Rt ) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the transmission rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (2-3 weeks after the outbreak). A fast rate of decline in Rt was observed, and adopting mitigation strategies early in the epidemic was effective in reducing the transmission rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the transmission rate decreases. In the United States, Rt significantly decreased during a 2-week period after the declaration of national emergency, but it declined at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (mid-November with fewer than 100 daily cases) and a total of more than 2 million cases.
DOI: 10.3389/fpubh.2020.00325
PubMed: 32719764
PubMed Central: PMC7347904
Affiliations:
- États-Unis
- Caroline du Nord, État de New York
- Chapel Hill (Caroline du Nord), New York
- Université Columbia, Université de Caroline du Nord à Chapel Hill
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Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region><settlement type="city">New York</settlement></placeName><orgName type="university">Université Columbia</orgName></affiliation></author><author><name sortKey="Xie, Shanghong" sort="Xie, Shanghong" uniqKey="Xie S" first="Shanghong" last="Xie">Shanghong Xie</name><affiliation wicri:level="4"><nlm:affiliation>Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region><settlement type="city">New York</settlement></placeName><orgName type="university">Université Columbia</orgName></affiliation></author><author><name sortKey="Wang, Yuanjia" sort="Wang, Yuanjia" uniqKey="Wang Y" first="Yuanjia" last="Wang">Yuanjia Wang</name><affiliation wicri:level="4"><nlm:affiliation>Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY</wicri:regionArea><placeName><region type="state">État de New York</region><settlement type="city">New York</settlement></placeName><orgName type="university">Université Columbia</orgName></affiliation></author><author><name sortKey="Zeng, Donglin" sort="Zeng, Donglin" uniqKey="Zeng D" first="Donglin" last="Zeng">Donglin Zeng</name><affiliation wicri:level="4"><nlm:affiliation>Department of Biostatistics, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC</wicri:regionArea><placeName><region type="state">Caroline du Nord</region><settlement type="city">Chapel Hill (Caroline du Nord)</settlement></placeName><orgName type="university">Université de Caroline du Nord à Chapel Hill</orgName></affiliation></author></analytic><series><title level="j">Frontiers in public health</title><idno type="ISSN">2296-2565</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict the COVID-19 disease course and compare the effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (<i>R</i><sub><i>t</i></sub> ) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the transmission rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (2-3 weeks after the outbreak). A fast rate of decline in <i>R</i><sub><i>t</i></sub> was observed, and adopting mitigation strategies early in the epidemic was effective in reducing the transmission rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the transmission rate decreases. In the United States, <i>R</i><sub><i>t</i></sub> significantly decreased during a 2-week period after the declaration of national emergency, but it declined at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (mid-November with fewer than 100 daily cases) and a total of more than 2 million cases.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32719764</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>28</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">2296-2565</ISSN><JournalIssue CitedMedium="Print"><Volume>8</Volume><PubDate><Year>2020</Year></PubDate></JournalIssue><Title>Frontiers in public health</Title><ISOAbbreviation>Front Public Health</ISOAbbreviation></Journal><ArticleTitle>Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies.</ArticleTitle><Pagination><MedlinePgn>325</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3389/fpubh.2020.00325</ELocationID><Abstract><AbstractText>Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict the COVID-19 disease course and compare the effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (<i>R</i><sub><i>t</i></sub> ) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the transmission rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (2-3 weeks after the outbreak). A fast rate of decline in <i>R</i><sub><i>t</i></sub> was observed, and adopting mitigation strategies early in the epidemic was effective in reducing the transmission rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the transmission rate decreases. In the United States, <i>R</i><sub><i>t</i></sub> significantly decreased during a 2-week period after the declaration of national emergency, but it declined at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (mid-November with fewer than 100 daily cases) and a total of more than 2 million cases.</AbstractText><CopyrightInformation>Copyright © 2020 Wang, Xie, Wang and Zeng.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Qinxia</ForeName><Initials>Q</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xie</LastName><ForeName>Shanghong</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Yuanjia</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zeng</LastName><ForeName>Donglin</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 GM124104</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R01 NS073671</GrantID><Acronym>NS</Acronym><Agency>NINDS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R21 MH117458</GrantID><Acronym>MH</Acronym><Agency>NIMH NIH HHS</Agency><Country>United 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Y" first="Yuanjia" last="Wang">Yuanjia Wang</name><name sortKey="Xie, Shanghong" sort="Xie, Shanghong" uniqKey="Xie S" first="Shanghong" last="Xie">Shanghong Xie</name><name sortKey="Zeng, Donglin" sort="Zeng, Donglin" uniqKey="Zeng D" first="Donglin" last="Zeng">Donglin Zeng</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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