Effectiveness of Vaccination Strategies for Infectious Diseases According to Human Contact Networks
Identifieur interne : 000696 ( Istex/Corpus ); précédent : 000695; suivant : 000697Effectiveness of Vaccination Strategies for Infectious Diseases According to Human Contact Networks
Auteurs : Fumihiko Takeuchi ; Kenji YamamotoSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: A ‘contact network’ modeling infection transmission comprises of nodes (or individuals) that are linked when they are in contact that possibly transmits an infection. We here studied infection transmission on contact networks of various degree distributions—scale-free, exponential and constant—under SIRV model assuming susceptible, infected, removed and vaccinated statuses of nodes. Aiming for infectious disease containment within the very early stage of spreading, we computed the minimum transmissibility at which an infectious disease epidemic begins to emerge, and its change according to mass preventive and ring post-outbreak vaccination. In the most degree-heterogeneous scale-free network, the ‘super-spreading’ by the hubs, or high-degree nodes, allowed epidemics even for low transmissibility. In compensation, vaccination was much more efficient for the scale-free network. We also found that basic reproductive number R 0 defines a measurement of epidemic emergence universally applicable to networks of various degree distributions. These results are significant for public health design.
Url:
DOI: 10.1007/11428831_119
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ISTEX:7FA951059FA0A066496F9AF347EB5EAF65BB0B4ALe document en format XML
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We here studied infection transmission on contact networks of various degree distributions—scale-free, exponential and constant—under SIRV model assuming susceptible, infected, removed and vaccinated statuses of nodes. Aiming for infectious disease containment within the very early stage of spreading, we computed the minimum transmissibility at which an infectious disease epidemic begins to emerge, and its change according to mass preventive and ring post-outbreak vaccination. In the most degree-heterogeneous scale-free network, the ‘super-spreading’ by the hubs, or high-degree nodes, allowed epidemics even for low transmissibility. In compensation, vaccination was much more efficient for the scale-free network. We also found that basic reproductive number R 0 defines a measurement of epidemic emergence universally applicable to networks of various degree distributions. These results are significant for public health design.</div></front></TEI><istex><corpusName>springer-ebooks</corpusName><author><json:item><name>Fumihiko Takeuchi</name><affiliations><json:string>Department of Infection Control Science, Juntendo University, 113-8421, Tokyo, Japan</json:string><json:string>International Medical Center of Japan, Research Institute, 162-8655, Tokyo, Japan</json:string><json:string>E-mail: fumihiko@takeuchi.name</json:string></affiliations></json:item><json:item><name>Kenji Yamamoto</name><affiliations><json:string>Department of Infection Control Science, Juntendo University, 113-8421, Tokyo, Japan</json:string><json:string>International Medical Center of Japan, Research Institute, 162-8655, Tokyo, Japan</json:string></affiliations></json:item></author><arkIstex>ark:/67375/HCB-RH923RD6-6</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: A ‘contact network’ modeling infection transmission comprises of nodes (or individuals) that are linked when they are in contact that possibly transmits an infection. We here studied infection transmission on contact networks of various degree distributions—scale-free, exponential and constant—under SIRV model assuming susceptible, infected, removed and vaccinated statuses of nodes. Aiming for infectious disease containment within the very early stage of spreading, we computed the minimum transmissibility at which an infectious disease epidemic begins to emerge, and its change according to mass preventive and ring post-outbreak vaccination. In the most degree-heterogeneous scale-free network, the ‘super-spreading’ by the hubs, or high-degree nodes, allowed epidemics even for low transmissibility. In compensation, vaccination was much more efficient for the scale-free network. We also found that basic reproductive number R 0 defines a measurement of epidemic emergence universally applicable to networks of various degree distributions. 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type="first">Demetri</forename><surname>Terzopoulos</surname></persName><affiliation><orgName type="institution">New York University</orgName><address><settlement>NY</settlement><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Dough</forename><surname>Tygar</surname></persName><affiliation><orgName type="institution">University of California</orgName><address><settlement>Berkeley</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation><orgName type="institution">Rice University</orgName><address><settlement>Houston</settlement><region>TX</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation><orgName type="institution">Max-Planck Institute of Computer Science</orgName><address><settlement>Saarbruecken</settlement><country key="DE">GERMANY</country></address></affiliation></editor><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="seriesID">558</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><abstract xml:lang="en"><head>Abstract</head><p>A ‘contact network’ modeling infection transmission comprises of nodes (or individuals) that are linked when they are in contact that possibly transmits an infection. We here studied infection transmission on contact networks of various degree distributions—scale-free, exponential and constant—under SIRV model assuming susceptible, infected, removed and vaccinated statuses of nodes. Aiming for infectious disease containment within the very early stage of spreading, we computed the minimum transmissibility at which an infectious disease epidemic begins to emerge, and its change according to mass preventive and ring post-outbreak vaccination. In the most degree-heterogeneous scale-free network, the ‘super-spreading’ by the hubs, or high-degree nodes, allowed epidemics even for low transmissibility. In compensation, vaccination was much more efficient for the scale-free network. We also found that basic reproductive number <hi rend="italic">R</hi><hi rend="subscript">0</hi> defines a measurement of epidemic emergence universally applicable to networks of various degree distributions. 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DisplayOrder="Western"><GivenName>Madhu</GivenName><FamilyName>Sudan</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff12"><EditorName DisplayOrder="Western"><GivenName>Demetri</GivenName><FamilyName>Terzopoulos</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff13"><EditorName DisplayOrder="Western"><GivenName>Dough</GivenName><FamilyName>Tygar</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff14"><EditorName DisplayOrder="Western"><GivenName>Moshe</GivenName><GivenName>Y.</GivenName><FamilyName>Vardi</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff15"><EditorName DisplayOrder="Western"><GivenName>Gerhard</GivenName><FamilyName>Weikum</FamilyName></EditorName></Editor><Affiliation ID="Aff1"><OrgName>Lancaster University</OrgName><OrgAddress><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgName>ETH Zurich</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>CA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>New York University</OrgName><OrgAddress><City>NY</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-32111-8</BookID><BookTitle>Computational Science – ICCS 2005</BookTitle><BookSubTitle>5th International Conference, Atlanta, GA, USA, May 22-25, 2005. Proceedings, Part I</BookSubTitle><BookVolumeNumber>3514</BookVolumeNumber><BookSequenceNumber>3514</BookSequenceNumber><BookDOI>10.1007/b136570</BookDOI><BookTitleID>118306</BookTitleID><BookPrintISBN>978-3-540-26032-5</BookPrintISBN><BookElectronicISBN>978-3-540-32111-8</BookElectronicISBN><BookChapterCount>133</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I14002" Priority="1" Type="Secondary">Software Engineering/Programming and Operating Systems</BookSubject><BookSubject Code="I16005" Priority="2" Type="Secondary">Theory of Computation</BookSubject><BookSubject Code="I1701X" Priority="3" Type="Secondary">Numeric Computing</BookSubject><BookSubject Code="I18008" Priority="4" Type="Secondary">Information Systems and Communication Service</BookSubject><BookSubject Code="I22005" Priority="5" Type="Secondary">Computer Imaging, Vision, Pattern Recognition and Graphics</BookSubject><BookSubject Code="I21025" Priority="6" Type="Secondary">Simulation and Modeling</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Vaidy</GivenName><GivenName>S.</GivenName><FamilyName>Sunderam</FamilyName></EditorName><Contact><Email>vss@emory.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Geert</GivenName><GivenName>Dick</GivenName><Particle>van</Particle><FamilyName>Albada</FamilyName></EditorName><Contact><Email>dick@science.uva.nl</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Peter</GivenName><GivenName>M.</GivenName><GivenName>A.</GivenName><FamilyName>Sloot</FamilyName></EditorName><Contact><Email>sloot@science.uva.nl</Email></Contact></Editor><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Jack</GivenName><GivenName>J.</GivenName><FamilyName>Dongarra</FamilyName></EditorName><Contact><Email>dongarra@eecs.utk.edu</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Department of Mathematics and Computer Science</OrgDivision><OrgName>Emory University</OrgName><OrgAddress><City>Atlanta</City><State>Georgia</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>Department of Mathematics and Computer Science</OrgDivision><OrgName>University of Amsterdam</OrgName><OrgAddress><Street>Kruislaan 403</Street><Postcode>1098</Postcode><City>Amsterdam</City><State>SJ</State><Country>The Netherlands</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>Faculty of Sciences, Section of Computational Science</OrgDivision><OrgName>University of Amsterdam</OrgName><OrgAddress><Street>Kruislaan 403</Street><Postcode>1098</Postcode><City>Amsterdam</City><State>SJ</State><Country>The Netherlands</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgDivision>Computer Science Department</OrgDivision><OrgName>University of Tennessee</OrgName><OrgAddress><Postcode>37996-3450</Postcode><City>Knoxville</City><State>TN</State><Country>USA</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part20"><PartInfo TocLevels="0"><PartID>20</PartID><PartSequenceNumber>20</PartSequenceNumber><PartTitle>Biomedical Applications</PartTitle><PartChapterCount>5</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Computational Science – ICCS 2005</BookTitle></PartContext></PartInfo><Chapter ID="Chap119" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>119</ChapterID><ChapterDOI>10.1007/11428831_119</ChapterDOI><ChapterSequenceNumber>119</ChapterSequenceNumber><ChapterTitle Language="En">Effectiveness of Vaccination Strategies for Infectious Diseases According to Human Contact Networks</ChapterTitle><ChapterFirstPage>956</ChapterFirstPage><ChapterLastPage>962</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>20</PartID><BookID>978-3-540-32111-8</BookID><BookTitle>Computational Science – ICCS 2005</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff20 Aff21"><AuthorName DisplayOrder="Western"><GivenName>Fumihiko</GivenName><FamilyName>Takeuchi</FamilyName></AuthorName><Contact><Email>fumihiko@takeuchi.name</Email></Contact></Author><Author AffiliationIDS="Aff20 Aff21"><AuthorName DisplayOrder="Western"><GivenName>Kenji</GivenName><FamilyName>Yamamoto</FamilyName></AuthorName></Author><Affiliation ID="Aff20"><OrgDivision>Department of Infection Control Science</OrgDivision><OrgName>Juntendo University</OrgName><OrgAddress><Postcode>113-8421</Postcode><City>Tokyo</City><Country>Japan</Country></OrgAddress></Affiliation><Affiliation ID="Aff21"><OrgDivision>International Medical Center of Japan</OrgDivision><OrgName>Research Institute</OrgName><OrgAddress><Postcode>162-8655</Postcode><City>Tokyo</City><Country>Japan</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>A ‘contact network’ modeling infection transmission comprises of nodes (or individuals) that are linked when they are in contact that possibly transmits an infection. We here studied infection transmission on contact networks of various degree distributions—scale-free, exponential and constant—under SIRV model assuming susceptible, infected, removed and vaccinated statuses of nodes. Aiming for infectious disease containment within the very early stage of spreading, we computed the minimum transmissibility at which an infectious disease epidemic begins to emerge, and its change according to mass preventive and ring post-outbreak vaccination. In the most degree-heterogeneous scale-free network, the ‘super-spreading’ by the hubs, or high-degree nodes, allowed epidemics even for low transmissibility. In compensation, vaccination was much more efficient for the scale-free network. We also found that basic reproductive number <Emphasis Type="Italic">R</Emphasis><Subscript>0</Subscript> defines a measurement of epidemic emergence universally applicable to networks of various degree distributions. These results are significant for public health design.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Effectiveness of Vaccination Strategies for Infectious Diseases According to Human Contact Networks</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Effectiveness of Vaccination Strategies for Infectious Diseases According to Human Contact Networks</title></titleInfo><name type="personal"><namePart type="given">Fumihiko</namePart><namePart type="family">Takeuchi</namePart><affiliation>Department of Infection Control Science, Juntendo University, 113-8421, Tokyo, Japan</affiliation><affiliation>International Medical Center of Japan, Research Institute, 162-8655, Tokyo, Japan</affiliation><affiliation>E-mail: fumihiko@takeuchi.name</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Kenji</namePart><namePart type="family">Yamamoto</namePart><affiliation>Department of Infection Control Science, Juntendo University, 113-8421, Tokyo, Japan</affiliation><affiliation>International Medical Center of Japan, Research Institute, 162-8655, Tokyo, Japan</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2005</dateIssued><copyrightDate encoding="w3cdtf">2005</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: A ‘contact network’ modeling infection transmission comprises of nodes (or individuals) that are linked when they are in contact that possibly transmits an infection. We here studied infection transmission on contact networks of various degree distributions—scale-free, exponential and constant—under SIRV model assuming susceptible, infected, removed and vaccinated statuses of nodes. Aiming for infectious disease containment within the very early stage of spreading, we computed the minimum transmissibility at which an infectious disease epidemic begins to emerge, and its change according to mass preventive and ring post-outbreak vaccination. In the most degree-heterogeneous scale-free network, the ‘super-spreading’ by the hubs, or high-degree nodes, allowed epidemics even for low transmissibility. In compensation, vaccination was much more efficient for the scale-free network. We also found that basic reproductive number R 0 defines a measurement of epidemic emergence universally applicable to networks of various degree distributions. These results are significant for public health design.</abstract><relatedItem type="host"><titleInfo><title>Computational Science – ICCS 2005</title><subTitle>5th International Conference, Atlanta, GA, USA, May 22-25, 2005. Proceedings, Part I</subTitle></titleInfo><name type="personal"><namePart type="given">Vaidy</namePart><namePart type="given">S.</namePart><namePart type="family">Sunderam</namePart><affiliation>Department of Mathematics and Computer Science, Emory University, Atlanta, Georgia, USA</affiliation><affiliation>E-mail: vss@emory.edu</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Geert</namePart><namePart type="given">Dick</namePart><namePart type="family">van Albada</namePart><affiliation>Department of Mathematics and Computer Science, University of Amsterdam, Kruislaan 403, 1098, Amsterdam, SJ, The Netherlands</affiliation><affiliation>E-mail: dick@science.uva.nl</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Peter</namePart><namePart type="given">M.</namePart><namePart type="given">A.</namePart><namePart type="family">Sloot</namePart><affiliation>Faculty of Sciences, Section of Computational Science, University of Amsterdam, Kruislaan 403, 1098, Amsterdam, SJ, The Netherlands</affiliation><affiliation>E-mail: sloot@science.uva.nl</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jack</namePart><namePart type="given">J.</namePart><namePart type="family">Dongarra</namePart><affiliation>Computer Science Department, University of Tennessee, 37996-3450, Knoxville, TN, USA</affiliation><affiliation>E-mail: dongarra@eecs.utk.edu</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate 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Modeling</topic></subject><identifier type="DOI">10.1007/b136570</identifier><identifier type="ISBN">978-3-540-26032-5</identifier><identifier type="eISBN">978-3-540-32111-8</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">118306</identifier><identifier type="BookID">978-3-540-32111-8</identifier><identifier type="BookChapterCount">133</identifier><identifier type="BookVolumeNumber">3514</identifier><identifier type="BookSequenceNumber">3514</identifier><identifier type="PartChapterCount">5</identifier><part><date>2005</date><detail type="part"><title>Biomedical Applications</title></detail><detail type="volume"><number>3514</number><caption>vol.</caption></detail><extent unit="pages"><start>956</start><end>962</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2005</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in 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