Networks and Models with Heterogeneous Population Structure in Epidemiology
Identifieur interne : 000C09 ( Pmc/Checkpoint ); précédent : 000C08; suivant : 000C10Networks and Models with Heterogeneous Population Structure in Epidemiology
Auteurs :Source :
- Network Science ; 2010.
Abstract
Heterogeneous population structure can have a profound effect on infectious disease dynamics, and is particularly important when investigating “tactical” disease control questions. At times, the nature of the network involved in the transmission of the pathogen (bacteria, virus, macro-parasite, etc.) appears to be clear; however, the nature of the network involved is dependent on the scale (e.g. within-host, between-host, or between-population), the nature of the contact, which ranges from the highly specific (e.g. sexual acts or needle sharing at the person-to-person level) to almost completely non-specific (e.g. aerosol transmission, often over long distances as can occur with the highly infectious livestock pathogen foot-and-mouth disease virus—FMDv—at the farm-to-farm level, e.g. Schley et al. in J. R. Soc. Interface 6:455–462,
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DOI: 10.1007/978-1-84996-396-1_4
PubMed: NONE
PubMed Central: 7123232
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Networks and Models with Heterogeneous Population Structure in Epidemiology</title></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmc">7123232</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7123232</idno><idno type="RBID">PMC:7123232</idno><idno type="doi">10.1007/978-1-84996-396-1_4</idno><idno type="pmid">NONE</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">000108</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000108</idno><idno type="wicri:Area/Pmc/Curation">000108</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000108</idno><idno type="wicri:Area/Pmc/Checkpoint">000C09</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000C09</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Networks and Models with Heterogeneous Population Structure in Epidemiology</title></analytic><series><title level="j">Network Science</title><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Heterogeneous population structure can have a profound effect on infectious disease dynamics, and is particularly important when investigating “tactical” disease control questions. At times, the nature of the network involved in the transmission of the pathogen (bacteria, virus, macro-parasite, etc.) appears to be clear; however, the nature of the network involved is dependent on the scale (e.g. within-host, between-host, or between-population), the nature of the contact, which ranges from the highly specific (e.g. sexual acts or needle sharing at the person-to-person level) to almost completely non-specific (e.g. aerosol transmission, often over long distances as can occur with the highly infectious livestock pathogen foot-and-mouth disease virus—FMDv—at the farm-to-farm level, e.g. Schley et al. in J. R. Soc. Interface 6:455–462, <xref ref-type="bibr" rid="CR63">2008</xref>), and the timescale of interest (e.g. at the scale of the individual, the typical infectious period of the host). Theoretical approaches to examining the implications of particular network structures on disease transmission have provided critical insight; however, a greater challenge is the integration of network approaches with data on real population structures. In this chapter, some concepts in disease modelling will be introduced, the relevance of selected network phenomena discussed, and then results from real data and their relationship to network analyses summarised. These include examinations of the patterns of air traffic and its relation to the spread of SARS in 2003 (Colizza et al. in BMC Med., <xref ref-type="bibr" rid="CR9">2007</xref>; Hufnagel et al. in Proc. Natl. Acad. Sci. USA 101:15124–15129, <xref ref-type="bibr" rid="CR35">2004</xref>), the use of the extensively documented Great Britain livestock movements network (Green et al. in J. Theor. Biol. 239:289–297, <xref ref-type="bibr" rid="CR27">2008</xref>; Robinson et al. in J. R. Soc. Interface 4:669–674, <xref ref-type="bibr" rid="CR61">2007</xref>; Vernon and Keeling in Proc. R. Soc. Lond. B, Biol. Sci. 276:469–476, <xref ref-type="bibr" rid="CR67">2009</xref>) and the growing interest in combining contact structure data with phylogenetics to identify real contact patterns as they directly relate to diseases of interest (Cottam et al. in PLoS Pathogens 4:1000050, <xref ref-type="bibr" rid="CR11">2007</xref>; Hughes et al. in PLoS Pathogens 5:1000590, <xref ref-type="bibr" rid="CR36">2009</xref>).</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Akaike, H" uniqKey="Akaike H">H. Akaike</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Anderson, R M" uniqKey="Anderson R">R.M. Anderson</name></author><author><name sortKey="May, R M" uniqKey="May R">R.M. May</name></author><author><name sortKey="Anderson, B" uniqKey="Anderson B">B. 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<front><journal-meta><journal-id journal-id-type="publisher-id">978-1-84996-396-1</journal-id><journal-id journal-id-type="doi">10.1007/978-1-84996-396-1</journal-id><journal-id journal-id-type="nlm-ta">Network Science</journal-id><journal-title-group><journal-title>Network Science</journal-title><journal-subtitle>Complexity in Nature and Technology</journal-subtitle></journal-title-group><isbn publication-format="print">978-1-84996-395-4</isbn><isbn publication-format="electronic">978-1-84996-396-1</isbn></journal-meta><article-meta><article-id pub-id-type="pmc">7123232</article-id><article-id pub-id-type="publisher-id">4</article-id><article-id pub-id-type="doi">10.1007/978-1-84996-396-1_4</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Networks and Models with Heterogeneous Population Structure in Epidemiology</article-title></title-group><contrib-group content-type="book editors"><contrib contrib-type="editor"><name><surname>Estrada</surname><given-names>Ernesto</given-names></name><address><phone>141548 3657</phone><fax>141548 3345</fax><email>ernesto.estrada@strath.ac.uk</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="editor"><name><surname>Fox</surname><given-names>Maria</given-names></name><address><phone>141548 4527</phone><email>maria@maths.strath.ac.uk</email></address><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="editor"><name><surname>Higham</surname><given-names>Desmond J.</given-names></name><address><phone>141 548 3716</phone><fax>141 548 3345</fax><email>djh@maths.strath.ac.uk</email></address><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="editor"><name><surname>Oppo</surname><given-names>Gian-Luca</given-names></name><address><phone>141 548 3761</phone><fax>141 552 2891</fax><email>gianluca@phys.strath.ac.uk</email></address><xref ref-type="aff" rid="Aff4">4</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="GRID">grid.11984.35</institution-id><institution-id institution-id-type="ISNI">0000000121138138</institution-id><institution>, Dept. Mathematics,</institution><institution>University of Strathclyde,</institution></institution-wrap>Glasgow, G1 1XH United Kingdom</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="GRID">grid.11984.35</institution-id><institution-id institution-id-type="ISNI">0000000121138138</institution-id><institution>, Dept. Computer & Information Sciences,</institution><institution>University of Strathclyde,</institution></institution-wrap>Richmond Street 26, Glasgow, G1 1XH United Kingdom</aff><aff id="Aff3"><label>3</label><institution-wrap><institution-id institution-id-type="GRID">grid.11984.35</institution-id><institution-id institution-id-type="ISNI">0000000121138138</institution-id><institution>Dept. Mathematics,</institution><institution>University of Strathclyde,</institution></institution-wrap>Glasgow, G1 1XH United Kingdom</aff><aff id="Aff4"><label>4</label><institution-wrap><institution-id institution-id-type="GRID">grid.11984.35</institution-id><institution-id institution-id-type="ISNI">0000000121138138</institution-id><institution>, Dept. Physics & Applied Physics,</institution><institution>University of Strathclyde,</institution></institution-wrap>Rottenrow 107, Glasgow, G4 0NG United Kingdom</aff></contrib-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Kao</surname><given-names>R. R.</given-names></name><address><email>r.kao@vet.gla.ac.uk</email></address><xref ref-type="aff" rid="Aff1"></xref></contrib><aff id="Aff5"><institution-wrap><institution-id institution-id-type="GRID">grid.8756.c</institution-id><institution-id institution-id-type="ISNI">000000012193314X</institution-id><institution>Faculty of Veterinary Medicine,</institution><institution>University of Glasgow,</institution></institution-wrap>Glasgow, UK</aff></contrib-group><pub-date pub-type="epub"><day>2</day><month>6</month><year>2010</year></pub-date><fpage>51</fpage><lpage>84</lpage><permissions><copyright-statement>© Springer-Verlag London Limited 2010</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"><p>Heterogeneous population structure can have a profound effect on infectious disease dynamics, and is particularly important when investigating “tactical” disease control questions. At times, the nature of the network involved in the transmission of the pathogen (bacteria, virus, macro-parasite, etc.) appears to be clear; however, the nature of the network involved is dependent on the scale (e.g. within-host, between-host, or between-population), the nature of the contact, which ranges from the highly specific (e.g. sexual acts or needle sharing at the person-to-person level) to almost completely non-specific (e.g. aerosol transmission, often over long distances as can occur with the highly infectious livestock pathogen foot-and-mouth disease virus—FMDv—at the farm-to-farm level, e.g. Schley et al. in J. R. Soc. Interface 6:455–462, <xref ref-type="bibr" rid="CR63">2008</xref>), and the timescale of interest (e.g. at the scale of the individual, the typical infectious period of the host). Theoretical approaches to examining the implications of particular network structures on disease transmission have provided critical insight; however, a greater challenge is the integration of network approaches with data on real population structures. In this chapter, some concepts in disease modelling will be introduced, the relevance of selected network phenomena discussed, and then results from real data and their relationship to network analyses summarised. These include examinations of the patterns of air traffic and its relation to the spread of SARS in 2003 (Colizza et al. in BMC Med., <xref ref-type="bibr" rid="CR9">2007</xref>; Hufnagel et al. in Proc. Natl. Acad. Sci. USA 101:15124–15129, <xref ref-type="bibr" rid="CR35">2004</xref>), the use of the extensively documented Great Britain livestock movements network (Green et al. in J. Theor. Biol. 239:289–297, <xref ref-type="bibr" rid="CR27">2008</xref>; Robinson et al. in J. R. Soc. Interface 4:669–674, <xref ref-type="bibr" rid="CR61">2007</xref>; Vernon and Keeling in Proc. R. Soc. Lond. B, Biol. Sci. 276:469–476, <xref ref-type="bibr" rid="CR67">2009</xref>) and the growing interest in combining contact structure data with phylogenetics to identify real contact patterns as they directly relate to diseases of interest (Cottam et al. in PLoS Pathogens 4:1000050, <xref ref-type="bibr" rid="CR11">2007</xref>; Hughes et al. in PLoS Pathogens 5:1000590, <xref ref-type="bibr" rid="CR36">2009</xref>).</p></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Human Immunodeficiency Virus</kwd><kwd>Acquire Immune Deficiency Syndrome</kwd><kwd>Transmission Network</kwd><kwd>Average Path Length</kwd><kwd>Basic Reproduction Number</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© Springer-Verlag London Limited 2010</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc><affiliations><list></list><tree></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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