Real‐time epidemiology
Identifieur interne : 001477 ( Pmc/Curation ); précédent : 001476; suivant : 001478Real‐time epidemiology
Auteurs : Christl Donnelly ; Azra GhaniSource :
- Significance (Oxford, England) [ 1740-9705 ] ; 2004.
Abstract
Fitting transmission models to outbreak data allows key epidemiological parameters to be estimated and the impact of control measures to be evaluated.
Url:
DOI: 10.1111/j.1740-9713.2004.00066.x
PubMed: NONE
PubMed Central: 7169536
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<front><journal-meta><journal-id journal-id-type="nlm-ta">Signif (Oxf)</journal-id><journal-id journal-id-type="iso-abbrev">Signif (Oxf)</journal-id><journal-id journal-id-type="doi">10.1111/(ISSN)1740-9713</journal-id><journal-id journal-id-type="publisher-id">SIGN</journal-id><journal-title-group><journal-title>Significance (Oxford, England)</journal-title></journal-title-group><issn pub-type="ppub">1740-9705</issn><issn pub-type="epub">1740-9713</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmc">7169536</article-id><article-id pub-id-type="doi">10.1111/j.1740-9713.2004.00066.x</article-id><article-id pub-id-type="publisher-id">SIGN066</article-id><article-categories><subj-group subj-group-type="overline"><subject>Case Study</subject></subj-group><subj-group subj-group-type="heading"><subject>Case Study</subject></subj-group></article-categories><title-group><article-title>Real‐time epidemiology</article-title><subtitle>Understanding the spread of SARS</subtitle></title-group><contrib-group><contrib id="sign066-cr-0001" contrib-type="author"><name><surname>Donnelly</surname><given-names>Christl</given-names></name><xref ref-type="aff" rid="sign066-aff-0001"><sup>1</sup></xref></contrib><contrib id="sign066-cr-0002" contrib-type="author"><name><surname>Ghani</surname><given-names>Azra</given-names></name><xref ref-type="aff" rid="sign066-aff-0001"><sup>1</sup></xref></contrib></contrib-group><aff id="sign066-aff-0001"><label><sup>1</sup></label><named-content content-type="organisation-division">Imperial College London, Faculty of Medicine</named-content></aff><pub-date pub-type="ppub"><month>12</month><year>2004</year></pub-date><pub-date pub-type="epub"><day>13</day><month>12</month><year>2004</year></pub-date><volume>1</volume><issue>4</issue><issue-id pub-id-type="doi">10.1111/sign.2004.1.issue-4</issue-id><fpage>176</fpage><lpage>179</lpage><permissions><pmc-comment> © 2004 The Royal Statistical Society </pmc-comment>
<copyright-statement content-type="article-copyright">© 2004 The Royal Statistical Society</copyright-statement><license><license-p>This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="file:SIGN-1-176.pdf"></self-uri><abstract><p>Fitting transmission models to outbreak data allows key epidemiological parameters to be estimated and the impact of control measures to be evaluated. <bold>Christl Donnelly</bold> and <bold>Azra Ghani</bold> describe the use of such models in the 2003 SARS epidemic in Hong Kong.</p></abstract><counts><page-count count="4"></page-count></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>December 2004</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.0 mode:remove_FC converted:15.04.2020</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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