Congruent epidemic models for unstructured and structured populations: Analytical reconstruction of a 2003 SARS outbreak
Identifieur interne : 001151 ( Pmc/Checkpoint ); précédent : 001150; suivant : 001152Congruent epidemic models for unstructured and structured populations: Analytical reconstruction of a 2003 SARS outbreak
Auteurs : John N. BombardtSource :
- Mathematical Biosciences [ 0025-5564 ] ; 2006.
Abstract
Both the threat of bioterrorism and the natural emergence of contagious diseases underscore the importance of quantitatively understanding disease transmission in structured human populations. Over the last few years, researchers have advanced the mathematical theory of scale-free networks and used such theoretical advancements in pilot epidemic models. Scale-free contact networks are particularly interesting in the realm of mathematical epidemiology, primarily because these networks may allow meaningfully structured populations to be incorporated in epidemic models at moderate or intermediate levels of complexity. Moreover, a scale-free contact network with node degree correlation is in accord with the well-known preferred mixing concept. The present author describes a semi-empirical and deterministic epidemic modeling approach that (a) focuses on time-varying rates of disease transmission in both unstructured and structured populations and (b) employs probability density functions to characterize disease progression and outbreak controls. Given an epidemic curve for a historical outbreak, this modeling approach calls for Monte Carlo calculations (that define the average new infection rate) and solutions to integro-differential equations (that describe outbreak dynamics in an aggregate population or across all network connectivity classes). Numerical results are obtained for the 2003 SARS outbreak in Taiwan and the dynamical implications of time-varying transmission rates and scale-free contact networks are discussed in some detail.
Url:
DOI: 10.1016/j.mbs.2006.05.004
PubMed: 16904134
PubMed Central: 7094332
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Congruent epidemic models for unstructured and structured populations: Analytical reconstruction of a 2003 SARS outbreak</title><author><name sortKey="Bombardt, John N" sort="Bombardt, John N" uniqKey="Bombardt J" first="John N." last="Bombardt">John N. Bombardt</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">16904134</idno><idno type="pmc">7094332</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7094332</idno><idno type="RBID">PMC:7094332</idno><idno type="doi">10.1016/j.mbs.2006.05.004</idno><date when="2006">2006</date><idno type="wicri:Area/Pmc/Corpus">001268</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001268</idno><idno type="wicri:Area/Pmc/Curation">001268</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">001268</idno><idno type="wicri:Area/Pmc/Checkpoint">001151</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">001151</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Congruent epidemic models for unstructured and structured populations: Analytical reconstruction of a 2003 SARS outbreak</title><author><name sortKey="Bombardt, John N" sort="Bombardt, John N" uniqKey="Bombardt J" first="John N." last="Bombardt">John N. Bombardt</name></author></analytic><series><title level="j">Mathematical Biosciences</title><idno type="ISSN">0025-5564</idno><idno type="eISSN">1879-3134</idno><imprint><date when="2006">2006</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Both the threat of bioterrorism and the natural emergence of contagious diseases underscore the importance of quantitatively understanding disease transmission in structured human populations. Over the last few years, researchers have advanced the mathematical theory of scale-free networks and used such theoretical advancements in pilot epidemic models. Scale-free contact networks are particularly interesting in the realm of mathematical epidemiology, primarily because these networks may allow meaningfully structured populations to be incorporated in epidemic models at moderate or intermediate levels of complexity. Moreover, a scale-free contact network with node degree correlation is in accord with the well-known preferred mixing concept. The present author describes a semi-empirical and deterministic epidemic modeling approach that (a) focuses on time-varying rates of disease transmission in both unstructured and structured populations and (b) employs probability density functions to characterize disease progression and outbreak controls. Given an epidemic curve for a historical outbreak, this modeling approach calls for Monte Carlo calculations (that define the average new infection rate) and solutions to integro-differential equations (that describe outbreak dynamics in an aggregate population or across all network connectivity classes). Numerical results are obtained for the 2003 SARS outbreak in Taiwan and the dynamical implications of time-varying transmission rates and scale-free contact networks are discussed in some detail.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Lloyd Smith, J O" uniqKey="Lloyd Smith J">J.O. Lloyd-Smith</name></author><author><name sortKey="Galvani, A P" uniqKey="Galvani A">A.P. Galvani</name></author><author><name sortKey="Getz, W M" uniqKey="Getz W">W.M. Getz</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Riley, S" uniqKey="Riley S">S. Riley</name></author><author><name sortKey="Fraser, C" uniqKey="Fraser C">C. Fraser</name></author><author><name sortKey="Donnelly, C A" uniqKey="Donnelly C">C.A. Donnelly</name></author><author><name sortKey="Ghani, A C" uniqKey="Ghani A">A.C. Ghani</name></author><author><name sortKey="Abu Raddad, L J" uniqKey="Abu Raddad L">L.J. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Math Biosci</journal-id><journal-id journal-id-type="iso-abbrev">Math Biosci</journal-id><journal-title-group><journal-title>Mathematical Biosciences</journal-title></journal-title-group><issn pub-type="ppub">0025-5564</issn><issn pub-type="epub">1879-3134</issn><publisher><publisher-name>Elsevier Inc.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">16904134</article-id><article-id pub-id-type="pmc">7094332</article-id><article-id pub-id-type="publisher-id">S0025-5564(06)00084-8</article-id><article-id pub-id-type="doi">10.1016/j.mbs.2006.05.004</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Congruent epidemic models for unstructured and structured populations: Analytical reconstruction of a 2003 SARS outbreak</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Bombardt</surname><given-names>John N.</given-names></name><email>jbombard@ida.org</email><xref rid="cor1" ref-type="corresp">⁎</xref></contrib></contrib-group><aff>Institute for Defense Analyses, 4850 Mark Center Drive, Alexandria, VA 22311-1882, United States</aff><author-notes><corresp id="cor1"><label>⁎</label>Tel.: +1 703 845 2204; fax: +1 703 845 2255. <email>jbombard@ida.org</email></corresp></author-notes><pub-date pub-type="pmc-release"><day>9</day><month>6</month><year>2006</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="ppub"><month>10</month><year>2006</year></pub-date><pub-date pub-type="epub"><day>9</day><month>6</month><year>2006</year></pub-date><volume>203</volume><issue>2</issue><fpage>171</fpage><lpage>203</lpage><history><date date-type="received"><day>16</day><month>8</month><year>2005</year></date><date date-type="rev-recd"><day>5</day><month>5</month><year>2006</year></date><date date-type="accepted"><day>9</day><month>5</month><year>2006</year></date></history><permissions><copyright-statement>Copyright © 2006 Elsevier Inc. All rights reserved.</copyright-statement><copyright-year>2006</copyright-year><copyright-holder>Elsevier Inc.</copyright-holder><license><license-p>Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</license-p></license></permissions><abstract><p>Both the threat of bioterrorism and the natural emergence of contagious diseases underscore the importance of quantitatively understanding disease transmission in structured human populations. Over the last few years, researchers have advanced the mathematical theory of scale-free networks and used such theoretical advancements in pilot epidemic models. Scale-free contact networks are particularly interesting in the realm of mathematical epidemiology, primarily because these networks may allow meaningfully structured populations to be incorporated in epidemic models at moderate or intermediate levels of complexity. Moreover, a scale-free contact network with node degree correlation is in accord with the well-known preferred mixing concept. The present author describes a semi-empirical and deterministic epidemic modeling approach that (a) focuses on time-varying rates of disease transmission in both unstructured and structured populations and (b) employs probability density functions to characterize disease progression and outbreak controls. Given an epidemic curve for a historical outbreak, this modeling approach calls for Monte Carlo calculations (that define the average new infection rate) and solutions to integro-differential equations (that describe outbreak dynamics in an aggregate population or across all network connectivity classes). Numerical results are obtained for the 2003 SARS outbreak in Taiwan and the dynamical implications of time-varying transmission rates and scale-free contact networks are discussed in some detail.</p></abstract><kwd-group><title>Keywords</title><kwd>Deterministic epidemic model</kwd><kwd>Scale-free network</kwd><kwd>Preferred mixing</kwd><kwd>Time-dependent transmission rate</kwd><kwd>SARS</kwd></kwd-group></article-meta></front></pmc><affiliations><list></list><tree><noCountry><name sortKey="Bombardt, John N" sort="Bombardt, John N" uniqKey="Bombardt J" first="John N." last="Bombardt">John N. Bombardt</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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