Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19.
Identifieur interne : 000759 ( PubMed/Corpus ); précédent : 000758; suivant : 000760Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19.
Auteurs : Katelyn Gostic ; Ana Cr Gomez ; Riley O. Mummah ; Adam J. Kucharski ; James O. Lloyd-SmithSource :
- eLife [ 2050-084X ] ; 2020.
English descriptors
- KwdEn :
- Asymptomatic Infections, Betacoronavirus (isolation & purification), Coronavirus Infections (diagnosis), Coronavirus Infections (epidemiology), Coronavirus Infections (transmission), Disease Outbreaks, Humans, Infection Control, Mass Screening (methods), Mass Screening (standards), Pneumonia, Viral (diagnosis), Pneumonia, Viral (epidemiology), Pneumonia, Viral (transmission), Risk Assessment, Travel.
- MESH :
- diagnosis : Coronavirus Infections, Pneumonia, Viral.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- isolation & purification : Betacoronavirus.
- methods : Mass Screening.
- standards : Mass Screening.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- Asymptomatic Infections, Disease Outbreaks, Humans, Infection Control, Risk Assessment, Travel.
Abstract
Traveller screening is being used to limit further spread of COVID-19 following its recent emergence, and symptom screening has become a ubiquitous tool in the global response. Previously, we developed a mathematical model to understand factors governing the effectiveness of traveller screening to prevent spread of emerging pathogens (Gostic et al., 2015). Here, we estimate the impact of different screening programs given current knowledge of key COVID-19 life history and epidemiological parameters. Even under best-case assumptions, we estimate that screening will miss more than half of infected people. Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed. Our work underscores the need for measures to limit transmission by individuals who become ill after being missed by a screening program. These findings can support evidence-based policy to combat the spread of COVID-19, and prospective planning to mitigate future emerging pathogens.
DOI: 10.7554/eLife.55570
PubMed: 32091395
Links to Exploration step
pubmed:32091395Le document en format XML
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Mummah</name><affiliation><nlm:affiliation>Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States.</nlm:affiliation></affiliation></author><author><name sortKey="Kucharski, Adam J" sort="Kucharski, Adam J" uniqKey="Kucharski A" first="Adam J" last="Kucharski">Adam J. Kucharski</name><affiliation><nlm:affiliation>Department of Infectious Disease Epidemiology, London School of Tropical Hygiene and Medicine, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Lloyd Smith, James O" sort="Lloyd Smith, James O" uniqKey="Lloyd Smith J" first="James O" last="Lloyd-Smith">James O. Lloyd-Smith</name><affiliation><nlm:affiliation>Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32091395</idno><idno type="pmid">32091395</idno><idno type="doi">10.7554/eLife.55570</idno><idno type="wicri:Area/PubMed/Corpus">000759</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000759</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19.</title><author><name sortKey="Gostic, Katelyn" sort="Gostic, Katelyn" uniqKey="Gostic K" first="Katelyn" last="Gostic">Katelyn Gostic</name><affiliation><nlm:affiliation>Department of Ecology and Evolution, University of Chicago, Chicago, United States.</nlm:affiliation></affiliation></author><author><name sortKey="Gomez, Ana Cr" sort="Gomez, Ana Cr" uniqKey="Gomez A" first="Ana Cr" last="Gomez">Ana Cr Gomez</name><affiliation><nlm:affiliation>Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States.</nlm:affiliation></affiliation></author><author><name sortKey="Mummah, Riley O" sort="Mummah, Riley O" uniqKey="Mummah R" first="Riley O" last="Mummah">Riley O. Mummah</name><affiliation><nlm:affiliation>Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States.</nlm:affiliation></affiliation></author><author><name sortKey="Kucharski, Adam J" sort="Kucharski, Adam J" uniqKey="Kucharski A" first="Adam J" last="Kucharski">Adam J. Kucharski</name><affiliation><nlm:affiliation>Department of Infectious Disease Epidemiology, London School of Tropical Hygiene and Medicine, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Lloyd Smith, James O" sort="Lloyd Smith, James O" uniqKey="Lloyd Smith J" first="James O" last="Lloyd-Smith">James O. Lloyd-Smith</name><affiliation><nlm:affiliation>Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States.</nlm:affiliation></affiliation></author></analytic><series><title level="j">eLife</title><idno type="eISSN">2050-084X</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Asymptomatic Infections</term><term>Betacoronavirus (isolation & purification)</term><term>Coronavirus Infections (diagnosis)</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (transmission)</term><term>Disease Outbreaks</term><term>Humans</term><term>Infection Control</term><term>Mass Screening (methods)</term><term>Mass Screening (standards)</term><term>Pneumonia, Viral (diagnosis)</term><term>Pneumonia, Viral (epidemiology)</term><term>Pneumonia, Viral (transmission)</term><term>Risk Assessment</term><term>Travel</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Betacoronavirus</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Mass Screening</term></keywords><keywords scheme="MESH" qualifier="standards" xml:lang="en"><term>Mass Screening</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Asymptomatic Infections</term><term>Disease Outbreaks</term><term>Humans</term><term>Infection Control</term><term>Risk Assessment</term><term>Travel</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Traveller screening is being used to limit further spread of COVID-19 following its recent emergence, and symptom screening has become a ubiquitous tool in the global response. Previously, we developed a mathematical model to understand factors governing the effectiveness of traveller screening to prevent spread of emerging pathogens (Gostic et al., 2015). Here, we estimate the impact of different screening programs given current knowledge of key COVID-19 life history and epidemiological parameters. Even under best-case assumptions, we estimate that screening will miss more than half of infected people. Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed. Our work underscores the need for measures to limit transmission by individuals who become ill after being missed by a screening program. These findings can support evidence-based policy to combat the spread of COVID-19, and prospective planning to mitigate future emerging pathogens.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32091395</PMID><DateCompleted><Year>2020</Year><Month>03</Month><Day>18</Day></DateCompleted><DateRevised><Year>2020</Year><Month>04</Month><Day>17</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2050-084X</ISSN><JournalIssue CitedMedium="Internet"><Volume>9</Volume><PubDate><Year>2020</Year><Month>02</Month><Day>24</Day></PubDate></JournalIssue><Title>eLife</Title><ISOAbbreviation>Elife</ISOAbbreviation></Journal><ArticleTitle>Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19.</ArticleTitle><ELocationID EIdType="doi" ValidYN="Y">10.7554/eLife.55570</ELocationID><ELocationID EIdType="pii" ValidYN="Y">e55570</ELocationID><Abstract><AbstractText>Traveller screening is being used to limit further spread of COVID-19 following its recent emergence, and symptom screening has become a ubiquitous tool in the global response. Previously, we developed a mathematical model to understand factors governing the effectiveness of traveller screening to prevent spread of emerging pathogens (Gostic et al., 2015). Here, we estimate the impact of different screening programs given current knowledge of key COVID-19 life history and epidemiological parameters. Even under best-case assumptions, we estimate that screening will miss more than half of infected people. Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed. Our work underscores the need for measures to limit transmission by individuals who become ill after being missed by a screening program. These findings can support evidence-based policy to combat the spread of COVID-19, and prospective planning to mitigate future emerging pathogens.</AbstractText><CopyrightInformation>© 2020, Gostic et al.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Gostic</LastName><ForeName>Katelyn</ForeName><Initials>K</Initials><Identifier Source="ORCID">0000-0002-9369-6371</Identifier><AffiliationInfo><Affiliation>Department of Ecology and Evolution, University of Chicago, Chicago, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gomez</LastName><ForeName>Ana Cr</ForeName><Initials>AC</Initials><AffiliationInfo><Affiliation>Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mummah</LastName><ForeName>Riley O</ForeName><Initials>RO</Initials><AffiliationInfo><Affiliation>Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kucharski</LastName><ForeName>Adam J</ForeName><Initials>AJ</Initials><Identifier Source="ORCID">0000-0001-8814-9421</Identifier><AffiliationInfo><Affiliation>Department of Infectious Disease Epidemiology, London School of Tropical Hygiene and Medicine, London, United Kingdom.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lloyd-Smith</LastName><ForeName>James O</ForeName><Initials>JO</Initials><Identifier Source="ORCID">0000-0001-7941-502X</Identifier><AffiliationInfo><Affiliation>Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Fogarty International Center, National Institutes of Health, Bethesda, United States.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>Postdoctoral fellowship in dynamic and multiscale systems</GrantID><Agency>James S. McDonnell Foundation</Agency><Country>International</Country></Grant><Grant><GrantID>206250/Z/17/Z</GrantID><Agency>Wellcome</Agency><Country>International</Country></Grant><Grant><GrantID>Science without borders fellowship</GrantID><Agency>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior</Agency><Country>International</Country></Grant><Grant><GrantID>DEB-1557022</GrantID><Agency>National Science Foundation</Agency><Country>International</Country></Grant><Grant><GrantID>PREEMPT D18AC00031</GrantID><Agency>Defense Advanced Research Projects Agency</Agency><Country>International</Country></Grant><Grant><GrantID>RC-2635</GrantID><Agency>Strategic Environmental Research and Development Program</Agency><Country>International</Country></Grant><Grant><GrantID>RC-2635</GrantID><Agency>Strategic Environmental Reserach and Development Program</Agency><Country>International</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>02</Month><Day>24</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Elife</MedlineTA><NlmUniqueID>101579614</NlmUniqueID><ISSNLinking>2050-084X</ISSNLinking></MedlineJournalInfo><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="D058345" MajorTopicYN="Y">Asymptomatic Infections</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="Y">Betacoronavirus</DescriptorName><QualifierName UI="Q000302" MajorTopicYN="N">isolation & purification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus 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