New approaches to quantifying the spread of infection
Identifieur interne : 001040 ( Ncbi/Merge ); précédent : 001039; suivant : 001041New approaches to quantifying the spread of infection
Auteurs : Louise Matthews ; Mark WoolhouseSource :
- Nature Reviews. Microbiology [ 1740-1526 ] ; 2005.
Abstract
The last decade has seen considerable advances in statistical, mathematical and computational techniques that are available for the analysis of outbreak data. These advances have greatly increased our capacity to generate meaningful epidemiological information from relatively small numbers of cases of an infection by better representation of the stochastic nature of the outbreak events and improved methods for estimating parameters from such data. This review focuses on the application of such models, which capture the highly variable dynamics of infection spread amongst small numbers of individuals, in the following areas: quantifying the basic reproduction ratio An important development is the integration of clinical, surveillance and contact-tracing data into the modelling process. This leads to models that are better able to capture the underlying variability in the transmission dynamics and can do so with minimal assumptions. In particular, heterogeneities in the number of secondary infections generated by an infected case (as exemplified by the super-spreaders of the SARS outbreak) mean that contact tracing is essential for a proper quantification of uncertainty in the reproduction ratio. Advances in the analysis of outbreak data in the near future will probably come from the further development of molecular techniques (to assist contact tracing) and from the better integration of disease data with demographic and environmental information.
Url:
DOI: 10.1038/nrmicro1178
PubMed: 15995653
PubMed Central: 7096817
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">New approaches to quantifying the spread of infection</title><author><name sortKey="Matthews, Louise" sort="Matthews, Louise" uniqKey="Matthews L" first="Louise" last="Matthews">Louise Matthews</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Woolhouse, Mark" sort="Woolhouse, Mark" uniqKey="Woolhouse M" first="Mark" last="Woolhouse">Mark Woolhouse</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">15995653</idno><idno type="pmc">7096817</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7096817</idno><idno type="RBID">PMC:7096817</idno><idno type="doi">10.1038/nrmicro1178</idno><date when="2005">2005</date><idno type="wicri:Area/Pmc/Corpus">000565</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000565</idno><idno type="wicri:Area/Pmc/Curation">000565</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000565</idno><idno type="wicri:Area/Pmc/Checkpoint">001287</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">001287</idno><idno type="wicri:Area/Ncbi/Merge">001040</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">New approaches to quantifying the spread of infection</title><author><name sortKey="Matthews, Louise" sort="Matthews, Louise" uniqKey="Matthews L" first="Louise" last="Matthews">Louise Matthews</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Woolhouse, Mark" sort="Woolhouse, Mark" uniqKey="Woolhouse M" first="Mark" last="Woolhouse">Mark Woolhouse</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">Nature Reviews. Microbiology</title><idno type="ISSN">1740-1526</idno><idno type="eISSN">1740-1534</idno><imprint><date when="2005">2005</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Key Points</title><p id="Par3"><list list-type="bullet"><list-item><p id="Par4">The last decade has seen considerable advances in statistical, mathematical and computational techniques that are available for the analysis of outbreak data. These advances have greatly increased our capacity to generate meaningful epidemiological information from relatively small numbers of cases of an infection by better representation of the stochastic nature of the outbreak events and improved methods for estimating parameters from such data.</p></list-item><list-item><p id="Par5">This review focuses on the application of such models, which capture the highly variable dynamics of infection spread amongst small numbers of individuals, in the following areas: quantifying the basic reproduction ratio <italic>R</italic><sub>0</sub> in the early stages of an outbreak (for foot-and-mouth disease and severe acute respiratory syndrome (SARS)); short-term predictions of outbreak progress (foot-and-mouth disease); trends towards disease emergence or re-emergence (measles in the UK); as an early warning system when there is the threat of a major outbreak (avian influenza); and capturing transmission dynamics within small populations (antibiotic-resistant nosocomial infections).</p></list-item><list-item><p id="Par6">An important development is the integration of clinical, surveillance and contact-tracing data into the modelling process. This leads to models that are better able to capture the underlying variability in the transmission dynamics and can do so with minimal assumptions. In particular, heterogeneities in the number of secondary infections generated by an infected case (as exemplified by the super-spreaders of the SARS outbreak) mean that contact tracing is essential for a proper quantification of uncertainty in the reproduction ratio.</p></list-item><list-item><p id="Par7">Advances in the analysis of outbreak data in the near future will probably come from the further development of molecular techniques (to assist contact tracing) and from the better integration of disease data with demographic and environmental information.</p></list-item></list></p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Vinten Johansen, P" uniqKey="Vinten Johansen P">P Vinten-Johansen</name></author><author><name sortKey="Brody, H" uniqKey="Brody H">H Brody</name></author><author><name sortKey="Paneth, N" uniqKey="Paneth N">N Paneth</name></author><author><name sortKey="Rachman, S" uniqKey="Rachman S">S 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Nat Rev Microbiol</journal-id><journal-id journal-id-type="iso-abbrev">Nat. Rev. Microbiol</journal-id><journal-title-group><journal-title>Nature Reviews. Microbiology</journal-title></journal-title-group><issn pub-type="ppub">1740-1526</issn><issn pub-type="epub">1740-1534</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">15995653</article-id><article-id pub-id-type="pmc">7096817</article-id><article-id pub-id-type="publisher-id">BFnrmicro1178</article-id><article-id pub-id-type="doi">10.1038/nrmicro1178</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>New approaches to quantifying the spread of infection</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Matthews</surname><given-names>Louise</given-names></name><address><email>Louise.Matthews@ed.ac.uk</email></address><xref ref-type="aff" rid="Aff1"></xref><bio><p id="Par1">Louise Matthews has worked as a post-doctoral researcher, and currently as a research fellow, at the University of Edinburgh since 1997. Her research interests lie in the application of mathematical models to infectious-disease data to investigate within-host dynamics of disease pathogenesis, metapopulation dynamics and persistence of rare pathogens, control strategies and their optimal implementation, and identification of heterogeneities such as super-spreaders, which drive the spread of disease.</p></bio></contrib><contrib contrib-type="author"><name><surname>Woolhouse</surname><given-names>Mark</given-names></name><xref ref-type="aff" rid="Aff1"></xref><bio><p id="Par2">Mark Woolhouse has held the chair of Veterinary Public Health and Quantitative Epidemiology at the University of Edinburgh since 1997. His research interests concern the epidemiology and pathogenesis of animal and human infectious diseases. He works on a variety of infectious disease systems, ranging from prion diseases to viruses, bacteria, protozoa and helminths. The common theme is the development of a formal, quantitative understanding of the dynamics of parasites and pathogens within their host populations, with particular emphasis on informing the design of disease control programmes.</p></bio></contrib><aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.4305.2</institution-id><institution-id institution-id-type="ISNI">0000 0004 1936 7988</institution-id><institution>Veterinary Epidemiology Group, Centre for Tropical Veterinary Medicine, University of Edinburgh, Easter Bush Veterinary Centre,</institution></institution-wrap>Roslin, EH25 9RG Midlothian Scotland</aff></contrib-group><pub-date pub-type="ppub"><year>2005</year></pub-date><volume>3</volume><issue>7</issue><fpage>529</fpage><lpage>536</lpage><permissions><copyright-statement>© Nature Publishing Group 2005</copyright-statement><license><license-p>This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.</license-p></license></permissions><abstract id="Abs1" abstract-type="KeyPoints"><title>Key Points</title><p id="Par3"><list list-type="bullet"><list-item><p id="Par4">The last decade has seen considerable advances in statistical, mathematical and computational techniques that are available for the analysis of outbreak data. These advances have greatly increased our capacity to generate meaningful epidemiological information from relatively small numbers of cases of an infection by better representation of the stochastic nature of the outbreak events and improved methods for estimating parameters from such data.</p></list-item><list-item><p id="Par5">This review focuses on the application of such models, which capture the highly variable dynamics of infection spread amongst small numbers of individuals, in the following areas: quantifying the basic reproduction ratio <italic>R</italic><sub>0</sub> in the early stages of an outbreak (for foot-and-mouth disease and severe acute respiratory syndrome (SARS)); short-term predictions of outbreak progress (foot-and-mouth disease); trends towards disease emergence or re-emergence (measles in the UK); as an early warning system when there is the threat of a major outbreak (avian influenza); and capturing transmission dynamics within small populations (antibiotic-resistant nosocomial infections).</p></list-item><list-item><p id="Par6">An important development is the integration of clinical, surveillance and contact-tracing data into the modelling process. This leads to models that are better able to capture the underlying variability in the transmission dynamics and can do so with minimal assumptions. In particular, heterogeneities in the number of secondary infections generated by an infected case (as exemplified by the super-spreaders of the SARS outbreak) mean that contact tracing is essential for a proper quantification of uncertainty in the reproduction ratio.</p></list-item><list-item><p id="Par7">Advances in the analysis of outbreak data in the near future will probably come from the further development of molecular techniques (to assist contact tracing) and from the better integration of disease data with demographic and environmental information.</p></list-item></list></p></abstract><abstract id="Abs2"><p id="Par8">Recent major disease outbreaks, such as severe acute respiratory syndrome and foot-and-mouth disease in the UK, coupled with fears of emergence of human-to-human transmissible variants of avian influenza, have highlighted the importance of accurate quantification of disease threat when relatively few cases have occurred. Traditional approaches to mathematical modelling of infectious diseases deal most effectively with large outbreaks in large populations. The desire to elucidate the highly variable dynamics of disease spread amongst small numbers of individuals has fuelled the development of models that depend more directly on surveillance and contact-tracing data. This signals a move towards a closer interplay between epidemiological modelling, surveillance and disease-management strategies.</p></abstract><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© Springer Nature Limited 2005</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Matthews, Louise" sort="Matthews, Louise" uniqKey="Matthews L" first="Louise" last="Matthews">Louise Matthews</name><name sortKey="Woolhouse, Mark" sort="Woolhouse, Mark" uniqKey="Woolhouse M" first="Mark" last="Woolhouse">Mark Woolhouse</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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