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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 : 002627 ( Ncbi/Merge ); précédent : 002626; suivant : 002628

Using 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 [Royaume-Uni] ; Scott Epperson [États-Unis] ; Matthew Biggerstaff [États-Unis] ; David Swerdlow [États-Unis] ; Lyn Finelli [États-Unis] ; Neil M. Ferguson [Royaume-Uni]

Source :

RBID : PMC:3589342

Descripteurs français

English descriptors

Abstract

Using a novel method to assess the risks of outbreaks and epidemics, Simon Cauchemez and colleagues provide insight into a simple tool that allows for more robust monitoring of the epidemic potential of zoonoses.


Url:
DOI: 10.1371/journal.pmed.1001399
PubMed: 23472057
PubMed Central: 3589342

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PMC:3589342

Le document en format XML

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<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 wicri:level="3">
<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>
<country xml:lang="fr">Royaume-Uni</country>
<wicri:regionArea>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London</wicri:regionArea>
<placeName>
<settlement type="city">Londres</settlement>
<region type="country">Angleterre</region>
<region type="région" nuts="1">Grand Londres</region>
</placeName>
</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>
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<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="KwdFr" xml:lang="fr">
<term>Animaux</term>
<term>Biais de sélection</term>
<term>Grippe humaine (transmission)</term>
<term>Grippe humaine (virologie)</term>
<term>Grippe humaine (épidémiologie)</term>
<term>Humains</term>
<term>Incertitude</term>
<term>Infections à Orthomyxoviridae (transmission)</term>
<term>Infections à Orthomyxoviridae (virologie)</term>
<term>Infections à Orthomyxoviridae (épidémiologie)</term>
<term>Nombre de reproduction de base</term>
<term>Probabilité</term>
<term>Sous-type H3N2 du virus de la grippe A (physiologie)</term>
<term>Suidae (virologie)</term>
<term>Surveillance de la population ()</term>
<term>Zoonoses (transmission)</term>
<term>Zoonoses (épidémiologie)</term>
<term>Épidémies ()</term>
<term>États-Unis d'Amérique (épidémiologie)</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="physiologie" xml:lang="fr">
<term>Sous-type H3N2 du virus de la grippe A</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="virologie" xml:lang="fr">
<term>Grippe humaine</term>
<term>Infections à Orthomyxoviridae</term>
<term>Suidae</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" qualifier="épidémiologie" xml:lang="fr">
<term>Grippe humaine</term>
<term>Infections à Orthomyxoviridae</term>
<term>Zoonoses</term>
<term>États-Unis d'Amérique</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>
<keywords scheme="MESH" xml:lang="fr">
<term>Animaux</term>
<term>Biais de sélection</term>
<term>Humains</term>
<term>Incertitude</term>
<term>Nombre de reproduction de base</term>
<term>Probabilité</term>
<term>Surveillance de la population</term>
<term>Épidémies</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">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>
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