A Complex Systems Approach to Infectious Disease Surveillance and Response
Identifieur interne : 000F45 ( Istex/Corpus ); précédent : 000F44; suivant : 000F46A Complex Systems Approach to Infectious Disease Surveillance and Response
Auteurs : Benyun Shi ; Shang Xia ; Jiming LiuSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: The transmission of infectious diseases can be affected by various interactive factors at or across different scales, such as environmental factors (e.g., temperature) and physiological factors (e.g., immunity). In view of this, to effectively and efficiently monitor and response to an infectious disease, it would be necessary for us to systematically model these factors and their impacts on disease transmission. In this paper, we propose a complex systems approach to infectious disease surveillance and response that puts a special emphasis on complex systems modeling and policy-level decision making with consideration of multi-scale interactive factors and/or surveillance data of disease prevalence. We demonstrate the implementation of our approach by presenting two real-world studies, one on the air-borne influenza epidemic in Hong Kong and the other on the vector-borne malaria endemic in Yunnan, China.
Url:
DOI: 10.1007/978-3-319-02753-1_53
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In view of this, to effectively and efficiently monitor and response to an infectious disease, it would be necessary for us to systematically model these factors and their impacts on disease transmission. In this paper, we propose a complex systems approach to infectious disease surveillance and response that puts a special emphasis on complex systems modeling and policy-level decision making with consideration of multi-scale interactive factors and/or surveillance data of disease prevalence. We demonstrate the implementation of our approach by presenting two real-world studies, one on the air-borne influenza epidemic in Hong Kong and the other on the vector-borne malaria endemic in Yunnan, China.</div></front></TEI><istex><corpusName>springer-ebooks</corpusName><author><json:item><name>Benyun Shi</name><affiliations><json:string>Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong</json:string><json:string>E-mail: byshi@comp.hkbu.edu.hk</json:string></affiliations></json:item><json:item><name>Shang Xia</name><affiliations><json:string>Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong</json:string><json:string>E-mail: sxia@comp.hkbu.edu.hk</json:string></affiliations></json:item><json:item><name>Jiming Liu</name><affiliations><json:string>Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong</json:string><json:string>E-mail: jiming@comp.hkbu.edu.hk</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Complex systems modeling</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>data-driven computational intelligence</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>policy-level decision making</value></json:item></subject><arkIstex>ark:/67375/HCB-9VNSDW78-T</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: The transmission of infectious diseases can be affected by various interactive factors at or across different scales, such as environmental factors (e.g., temperature) and physiological factors (e.g., immunity). In view of this, to effectively and efficiently monitor and response to an infectious disease, it would be necessary for us to systematically model these factors and their impacts on disease transmission. In this paper, we propose a complex systems approach to infectious disease surveillance and response that puts a special emphasis on complex systems modeling and policy-level decision making with consideration of multi-scale interactive factors and/or surveillance data of disease prevalence. 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type="first">Doug</forename><surname>Tygar</surname></persName><affiliation><idno type="GRID" subtype="Institution">grid.47840.3f</idno><idno type="ISNI" subtype="Institution">0000000121817878</idno><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><idno type="GRID" subtype="Institution">grid.21940.3e</idno><idno type="ISNI" subtype="Institution"> 0000000419368278</idno><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><idno type="GRID" subtype="Institution">grid.419607.d</idno><idno type="ISNI" subtype="Institution">0000000120969941</idno><orgName type="institution">Max-Planck Institute of Computer Science</orgName><address><settlement>Saarbrücken</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>The transmission of infectious diseases can be affected by various interactive factors at or across different scales, such as environmental factors (e.g., temperature) and physiological factors (e.g., immunity). In view of this, to effectively and efficiently monitor and response to an infectious disease, it would be necessary for us to systematically model these factors and their impacts on disease transmission. In this paper, we propose a complex systems approach to infectious disease surveillance and response that puts a special emphasis on complex systems modeling and policy-level decision making with consideration of multi-scale interactive factors and/or surveillance data of disease prevalence. 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TocLevels="0"><ChapterID>53</ChapterID><ChapterDOI>10.1007/978-3-319-02753-1_53</ChapterDOI><ChapterSequenceNumber>53</ChapterSequenceNumber><ChapterTitle Language="En">A Complex Systems Approach to Infectious Disease Surveillance and Response</ChapterTitle><ChapterFirstPage>524</ChapterFirstPage><ChapterLastPage>535</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer International Publishing Switzerland</CopyrightHolderName><CopyrightYear>2013</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>10</PartID><BookID>978-3-319-02753-1</BookID><BookTitle>Brain and Health Informatics</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff25"><AuthorName DisplayOrder="Western"><GivenName>Benyun</GivenName><FamilyName>Shi</FamilyName></AuthorName><Contact><Email>byshi@comp.hkbu.edu.hk</Email></Contact></Author><Author AffiliationIDS="Aff25"><AuthorName DisplayOrder="Western"><GivenName>Shang</GivenName><FamilyName>Xia</FamilyName></AuthorName><Contact><Email>sxia@comp.hkbu.edu.hk</Email></Contact></Author><Author AffiliationIDS="Aff25"><AuthorName DisplayOrder="Western"><GivenName>Jiming</GivenName><FamilyName>Liu</FamilyName></AuthorName><Contact><Email>jiming@comp.hkbu.edu.hk</Email></Contact></Author><Affiliation ID="Aff25"><OrgID Level="Institution" Type="GRID">grid.221309.b</OrgID><OrgID Level="Institution" Type="ISNI">0000000417645980</OrgID><OrgDivision>Department of Computer Science</OrgDivision><OrgName>Hong Kong Baptist University</OrgName><OrgAddress><City>Kowloon Tong</City><Country>Hong Kong</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>The transmission of infectious diseases can be affected by various interactive factors at or across different scales, such as environmental factors (e.g., temperature) and physiological factors (e.g., immunity). In view of this, to effectively and efficiently monitor and response to an infectious disease, it would be necessary for us to systematically model these factors and their impacts on disease transmission. In this paper, we propose a complex systems approach to infectious disease surveillance and response that puts a special emphasis on complex systems modeling and policy-level decision making with consideration of multi-scale interactive factors and/or surveillance data of disease prevalence. We demonstrate the implementation of our approach by presenting two real-world studies, one on the air-borne influenza epidemic in Hong Kong and the other on the vector-borne malaria endemic in Yunnan, China.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Complex systems modeling</Keyword><Keyword>data-driven computational intelligence</Keyword><Keyword>policy-level decision making</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher><!-- Converted from LaTeX with LaTeX2A++ V3.1.49 --></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A Complex Systems Approach to Infectious Disease Surveillance and Response</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>A Complex Systems Approach to Infectious Disease Surveillance and Response</title></titleInfo><name type="personal"><namePart type="given">Benyun</namePart><namePart type="family">Shi</namePart><affiliation>Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong</affiliation><affiliation>E-mail: byshi@comp.hkbu.edu.hk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Shang</namePart><namePart type="family">Xia</namePart><affiliation>Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong</affiliation><affiliation>E-mail: sxia@comp.hkbu.edu.hk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jiming</namePart><namePart type="family">Liu</namePart><affiliation>Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong</affiliation><affiliation>E-mail: jiming@comp.hkbu.edu.hk</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 International Publishing</publisher><place><placeTerm type="text">Cham</placeTerm></place><dateIssued encoding="w3cdtf">2013</dateIssued><copyrightDate encoding="w3cdtf">2013</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: The transmission of infectious diseases can be affected by various interactive factors at or across different scales, such as environmental factors (e.g., temperature) and physiological factors (e.g., immunity). In view of this, to effectively and efficiently monitor and response to an infectious disease, it would be necessary for us to systematically model these factors and their impacts on disease transmission. In this paper, we propose a complex systems approach to infectious disease surveillance and response that puts a special emphasis on complex systems modeling and policy-level decision making with consideration of multi-scale interactive factors and/or surveillance data of disease prevalence. We demonstrate the implementation of our approach by presenting two real-world studies, one on the air-borne influenza epidemic in Hong Kong and the other on the vector-borne malaria endemic in Yunnan, China.</abstract><subject lang="en"><genre>Keywords</genre><topic>Complex systems modeling</topic><topic>data-driven computational intelligence</topic><topic>policy-level decision making</topic></subject><relatedItem type="host"><titleInfo><title>Brain and Health Informatics</title><subTitle>International Conference, BHI 2013, Maebashi, Japan, October 29-31, 2013. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Kazayuki</namePart><namePart type="family">Imamura</namePart><affiliation>Department of Systems Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadori-cho, 371-0816, Maebashi, Gunma, Japan</affiliation><affiliation>E-mail: imamurak@maebashi-it.ac.jp</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Shiro</namePart><namePart type="family">Usui</namePart><affiliation>Electronics-Inspired Interdisciplinary Research Institute, Toyohashi University of Technology, 1-1 Hibarigaoka Tenpaku-cho, 441-8580, Toyohashi, Aichi, Japan</affiliation><affiliation>E-mail: usui@eiiris.tut.ac.jp</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Tomoaki</namePart><namePart type="family">Shirao</namePart><affiliation>Department of Neurobiology and Behavior, Gunma University Graduate School of Medicine, 39-22 Showamachi 3-chome, 371-8511, Maebashi, Gunma, Japan</affiliation><affiliation>E-mail: tshirao@med.gunma-u.ac.jp</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takuji</namePart><namePart type="family">Kasamatsu</namePart><affiliation>The Smith Kettlewell Eye Research Institute, 2318 Fillmore Street, 94115, San Francisco, CA, USA</affiliation><affiliation>E-mail: takuji@ski.org</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Lars</namePart><namePart type="family">Schwabe</namePart><affiliation>University of Rostock, 18051, Rostock, Germany</affiliation><affiliation>E-mail: lars.schwabe@uni-rostock.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Ning</namePart><namePart type="family">Zhong</namePart><affiliation>Department of Life Science and Informatics, Maebashi Institute of Technology, 460-1 Kamisadori-cho, 371-0816, Maebashi, Gunma, Japan</affiliation><affiliation>E-mail: zhong@maebashi-it.ac.jp</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 encoding="w3cdtf">2013</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="SCI">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI23060">Health Informatics</topic><topic authority="SpringerSubjectCodes" authorityURI="SCI18008">Information Systems and Communication Service</topic></subject><identifier type="DOI">10.1007/978-3-319-02753-1</identifier><identifier type="ISBN">978-3-319-02752-4</identifier><identifier type="eISBN">978-3-319-02753-1</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">320022</identifier><identifier type="BookID">978-3-319-02753-1</identifier><identifier type="BookChapterCount">53</identifier><identifier type="BookVolumeNumber">8211</identifier><identifier type="BookSequenceNumber">8211</identifier><identifier type="PartChapterCount">4</identifier><part><date>2013</date><detail type="part"><title>Special Session on Intelligent Healthcare Data Analytics</title></detail><detail 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type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, Stanford, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm 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type="text">editor</roleTerm></role></name><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2013</copyrightDate><issuance>serial</issuance></originInfo><relatedItem type="constituent"><titleInfo><title>Lecture Notes in Artificial Intelligence</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><affiliation>Lancaster University, Lancaster, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takeo</namePart><namePart type="family">Kanade</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name 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