Using routine surveillance data to estimate the epidemic potential of emerging zoonoses: application to the emergence of US swine origin influenza A H3N2v virus.
Identifieur interne : 001244 ( PubMed/Corpus ); précédent : 001243; suivant : 001245Using routine surveillance data to estimate the epidemic potential of emerging zoonoses: application to the emergence of US swine origin influenza A H3N2v virus.
Auteurs : Simon Cauchemez ; Scott Epperson ; Matthew Biggerstaff ; David Swerdlow ; Lyn Finelli ; Neil M. FergusonSource :
- PLoS medicine [ 1549-1676 ] ; 2013.
English descriptors
- KwdEn :
- Animals, Basic Reproduction Number, Epidemics (statistics & numerical data), Humans, Influenza A Virus, H3N2 Subtype (physiology), Influenza, Human (epidemiology), Influenza, Human (transmission), Influenza, Human (virology), Orthomyxoviridae Infections (epidemiology), Orthomyxoviridae Infections (transmission), Orthomyxoviridae Infections (virology), Population Surveillance (methods), Probability, Selection Bias, Swine (virology), Uncertainty, United States (epidemiology), Zoonoses (epidemiology), Zoonoses (transmission).
- MESH :
- geographic , epidemiology : United States.
- epidemiology : Influenza, Human, Orthomyxoviridae Infections, Zoonoses.
- methods : Population Surveillance.
- physiology : Influenza A Virus, H3N2 Subtype.
- statistics & numerical data : Epidemics.
- transmission : Influenza, Human, Orthomyxoviridae Infections, Zoonoses.
- virology : Influenza, Human, Orthomyxoviridae Infections, Swine.
- Animals, Basic Reproduction Number, Humans, Probability, Selection Bias, Uncertainty.
Abstract
Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be assessed by monitoring the reproduction number R (average number of persons infected by a human case). However, until now, estimating R required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under-ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations.
DOI: 10.1371/journal.pmed.1001399
PubMed: 23472057
Links to Exploration step
pubmed:23472057Le document en format XML
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Ferguson</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="RBID">pubmed:23472057</idno><idno type="pmid">23472057</idno><idno type="doi">10.1371/journal.pmed.1001399</idno><idno type="wicri:Area/PubMed/Corpus">001244</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001244</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Using routine surveillance data to estimate the epidemic potential of emerging zoonoses: application to the emergence of US swine origin influenza A H3N2v virus.</title><author><name sortKey="Cauchemez, Simon" sort="Cauchemez, Simon" uniqKey="Cauchemez S" first="Simon" last="Cauchemez">Simon Cauchemez</name><affiliation><nlm:affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom. s.cauchemez@imperial.ac.uk</nlm:affiliation></affiliation></author><author><name sortKey="Epperson, Scott" sort="Epperson, Scott" uniqKey="Epperson S" first="Scott" last="Epperson">Scott Epperson</name></author><author><name sortKey="Biggerstaff, Matthew" sort="Biggerstaff, Matthew" uniqKey="Biggerstaff M" first="Matthew" last="Biggerstaff">Matthew Biggerstaff</name></author><author><name sortKey="Swerdlow, David" sort="Swerdlow, David" uniqKey="Swerdlow D" first="David" last="Swerdlow">David Swerdlow</name></author><author><name sortKey="Finelli, Lyn" sort="Finelli, Lyn" uniqKey="Finelli L" first="Lyn" last="Finelli">Lyn Finelli</name></author><author><name sortKey="Ferguson, Neil M" sort="Ferguson, Neil M" uniqKey="Ferguson N" first="Neil M" last="Ferguson">Neil M. Ferguson</name></author></analytic><series><title level="j">PLoS medicine</title><idno type="eISSN">1549-1676</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term><term>Basic Reproduction Number</term><term>Epidemics (statistics & numerical data)</term><term>Humans</term><term>Influenza A Virus, H3N2 Subtype (physiology)</term><term>Influenza, Human (epidemiology)</term><term>Influenza, Human (transmission)</term><term>Influenza, Human (virology)</term><term>Orthomyxoviridae Infections (epidemiology)</term><term>Orthomyxoviridae Infections (transmission)</term><term>Orthomyxoviridae Infections (virology)</term><term>Population Surveillance (methods)</term><term>Probability</term><term>Selection Bias</term><term>Swine (virology)</term><term>Uncertainty</term><term>United States (epidemiology)</term><term>Zoonoses (epidemiology)</term><term>Zoonoses (transmission)</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>Influenza, Human</term><term>Orthomyxoviridae Infections</term><term>Zoonoses</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Population Surveillance</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Influenza A Virus, H3N2 Subtype</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Epidemics</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Influenza, Human</term><term>Orthomyxoviridae Infections</term><term>Zoonoses</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Influenza, Human</term><term>Orthomyxoviridae Infections</term><term>Swine</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Basic Reproduction Number</term><term>Humans</term><term>Probability</term><term>Selection Bias</term><term>Uncertainty</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be assessed by monitoring the reproduction number R (average number of persons infected by a human case). However, until now, estimating R required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under-ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">23472057</PMID><DateCompleted><Year>2013</Year><Month>09</Month><Day>26</Day></DateCompleted><DateRevised><Year>2019</Year><Month>10</Month><Day>08</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1549-1676</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>3</Issue><PubDate><Year>2013</Year></PubDate></JournalIssue><Title>PLoS medicine</Title><ISOAbbreviation>PLoS Med.</ISOAbbreviation></Journal><ArticleTitle>Using routine surveillance data to estimate the epidemic potential of emerging zoonoses: application to the emergence of US swine origin influenza A H3N2v virus.</ArticleTitle><Pagination><MedlinePgn>e1001399</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pmed.1001399</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be assessed by monitoring the reproduction number R (average number of persons infected by a human case). However, until now, estimating R required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under-ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations.</AbstractText><AbstractText Label="METHODS AND FINDINGS" NlmCategory="RESULTS">Our approach is based on a parsimonious mathematical model of disease transmission and only requires data collected through routine surveillance and standard case investigations. We apply it to assess the transmissibility of swine-origin influenza A H3N2v-M virus in the US, Nipah virus in Malaysia and Bangladesh, and also present a non-zoonotic example (cholera in the Dominican Republic). Estimation is based on two simple summary statistics, the proportion infected by the natural reservoir among detected cases (G) and among the subset of the first detected cases in each cluster (F). If detection of a case does not affect detection of other cases from the same cluster, we find that R can be estimated by 1-G; otherwise R can be estimated by 1-F when the case detection rate is low. In more general cases, bounds on R can still be derived.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">We have developed a simple approach with limited data requirements that enables robust assessment of the risks posed by emerging zoonoses. We illustrate this by deriving transmissibility estimates for the H3N2v-M virus, an important step in evaluating the possible pandemic threat posed by this virus. Please see later in the article for the Editors' Summary.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Cauchemez</LastName><ForeName>Simon</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom. s.cauchemez@imperial.ac.uk</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Epperson</LastName><ForeName>Scott</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Biggerstaff</LastName><ForeName>Matthew</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Swerdlow</LastName><ForeName>David</ForeName><Initials>D</Initials></Author><Author ValidYN="Y"><LastName>Finelli</LastName><ForeName>Lyn</ForeName><Initials>L</Initials></Author><Author ValidYN="Y"><LastName>Ferguson</LastName><ForeName>Neil M</ForeName><Initials>NM</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>MR/K010174/1</GrantID><Agency>Medical Research Council</Agency><Country>United Kingdom</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>03</Month><Day>05</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS Med</MedlineTA><NlmUniqueID>101231360</NlmUniqueID><ISSNLinking>1549-1277</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D050936" MajorTopicYN="N">Basic Reproduction Number</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058872" MajorTopicYN="N">Epidemics</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D053122" MajorTopicYN="N">Influenza A Virus, H3N2 Subtype</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName><QualifierName UI="Q000821" MajorTopicYN="Y">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D009976" MajorTopicYN="N">Orthomyxoviridae Infections</DescriptorName><QualifierName UI="Q000453" 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