Towards Real Time Epidemiology: Data Assimilation, Modeling and Anomaly Detection of Health Surveillance Data Streams
Identifieur interne : 000F90 ( Istex/Corpus ); précédent : 000F89; suivant : 000F91Towards Real Time Epidemiology: Data Assimilation, Modeling and Anomaly Detection of Health Surveillance Data Streams
Auteurs : Luís M. A. Bettencourt ; Ruy M. Ribeiro ; Gerardo Chowell ; Timothy Lant ; Carlos Castillo-ChavezSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: An integrated quantitative approach to data assimilation, prediction and anomaly detection over real-time public health surveillance data streams is introduced. The importance of creating dynamical probabilistic models of disease dynamics capable of predicting future new cases from past and present disease incidence data is emphasized. Methods for real-time data assimilation, which rely on probabilistic formulations and on Bayes’ theorem to translate between probability densities for new cases and for model parameters are developed. This formulation creates future outlook with quantified uncertainty, and leads to natural anomaly detection schemes that quantify and detect disease evolution or population structure changes. Finally, the implementation of these methods and accompanying intervention tools in real time public health situations is realized through their embedding in state of the art information technology and interactive visualization environments.
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DOI: 10.1007/978-3-540-72608-1_8
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A." last="Bettencourt">Luís M. A. Bettencourt</name><affiliation><mods:affiliation>Theoretical Division and Center for Non-Linear Studies, Los Alamos National Laboratory, MS B284, Los Alamos NM 87545, USA</mods:affiliation></affiliation></author><author><name sortKey="Ribeiro, Ruy M" sort="Ribeiro, Ruy M" uniqKey="Ribeiro R" first="Ruy M." last="Ribeiro">Ruy M. 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The importance of creating dynamical probabilistic models of disease dynamics capable of predicting future new cases from past and present disease incidence data is emphasized. Methods for real-time data assimilation, which rely on probabilistic formulations and on Bayes’ theorem to translate between probability densities for new cases and for model parameters are developed. This formulation creates future outlook with quantified uncertainty, and leads to natural anomaly detection schemes that quantify and detect disease evolution or population structure changes. Finally, the implementation of these methods and accompanying intervention tools in real time public health situations is realized through their embedding in state of the art information technology and interactive visualization environments.</div></front></TEI><istex><corpusName>springer-ebooks</corpusName><author><json:item><name>Luís M. A. 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The importance of creating dynamical probabilistic models of disease dynamics capable of predicting future new cases from past and present disease incidence data is emphasized. Methods for real-time data assimilation, which rely on probabilistic formulations and on Bayes’ theorem to translate between probability densities for new cases and for model parameters are developed. This formulation creates future outlook with quantified uncertainty, and leads to natural anomaly detection schemes that quantify and detect disease evolution or population structure changes. Finally, the implementation of these methods and accompanying intervention tools in real time public health situations is realized through their embedding in state of the art information technology and interactive visualization environments.</p></abstract><textClass ana="keyword"><keywords xml:lang="en"><term>real time epidemiology</term><term>data assimilation</term><term>Bayesian inference</term><term>anomaly detection</term><term>interactive visualization</term><term> surveillance</term></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>I</label><item><term type="Primary">Computer Science</term></item><label>I18040</label><item><term type="Secondary" subtype="priority-1">Information Systems Applications (incl.Internet)</term></item><label>I18030</label><item><term 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ContainsESM="No" Language="En" MediaType="eBook" NumberingStyle="Unnumbered" OutputMedium="All" TocLevels="0"><BookID>978-3-540-72608-1</BookID><BookTitle>Intelligence and Security Informatics: Biosurveillance</BookTitle><BookSubTitle>Second NSF Workshop, BioSurveillance 2007, New Brunswick, NJ, USA, May 22, 2007. Proceedings</BookSubTitle><BookVolumeNumber>4506</BookVolumeNumber><BookSequenceNumber>4506</BookSequenceNumber><BookDOI>10.1007/978-3-540-72608-1</BookDOI><BookTitleID>149903</BookTitleID><BookPrintISBN>978-3-540-72607-4</BookPrintISBN><BookElectronicISBN>978-3-540-72608-1</BookElectronicISBN><BookChapterCount>25</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2007</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I18040" Priority="1" Type="Secondary">Information Systems Applications (incl.Internet)</BookSubject><BookSubject Code="I18030" Priority="2" Type="Secondary">Data Mining and Knowledge Discovery</BookSubject><BookSubject Code="I13022" Priority="3" Type="Secondary">Computer Communication Networks</BookSubject><BookSubject Code="I23050" Priority="4" Type="Secondary">Computational Biology/Bioinformatics</BookSubject><BookSubject Code="I24040" Priority="5" Type="Secondary">Computers and Society</BookSubject><BookSubject Code="I24067" Priority="6" Type="Secondary">Management of Computing and Information Systems</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup></BookInfo><BookHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>Daniel</GivenName><FamilyName>Zeng</FamilyName></EditorName><Contact><Email>zeng@eller.arizona.edu</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Ivan</GivenName><FamilyName>Gotham</FamilyName></EditorName><Contact><Email>ijg01@health.state.ny.us</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Ken</GivenName><FamilyName>Komatsu</FamilyName></EditorName><Contact><Email>komatsk@azdhs.gov</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Cecil</GivenName><FamilyName>Lynch</FamilyName></EditorName><Contact><Email>colynch@ucdavis.edu</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Mark</GivenName><FamilyName>Thurmond</FamilyName></EditorName><Contact><Email>mcthurmond@usdavis.edu</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>David</GivenName><FamilyName>Madigan</FamilyName></EditorName><Contact><Email>dmadigan@rci.rutgers.edu</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Bill</GivenName><FamilyName>Lober</FamilyName></EditorName><Contact><Email>lober@u.washington.edu</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>James</GivenName><FamilyName>Kvach</FamilyName></EditorName><Contact><Email>jandk30@aol.com</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Hsinchun</GivenName><FamilyName>Chen</FamilyName></EditorName><Contact><Email>hchen@eller.arizona.edu</Email></Contact></Editor></EditorGroup></BookHeader><Part ID="Part1"><PartInfo TocLevels="0"><PartID>1</PartID><PartNumber>I</PartNumber><PartSequenceNumber>1</PartSequenceNumber><PartTitle>Long Papers</PartTitle><PartChapterCount>17</PartChapterCount></PartInfo><SubPart ID="SubPart3"><SubPartInfo><SubPartID>3</SubPartID><SubPartSequenceNumber>3</SubPartSequenceNumber><SubPartTitle>Outbreak Detection Algorithms</SubPartTitle><SubPartChapterCount>5</SubPartChapterCount></SubPartInfo><Chapter ID="Chap8" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>8</ChapterID><ChapterDOI>10.1007/978-3-540-72608-1_8</ChapterDOI><ChapterSequenceNumber>8</ChapterSequenceNumber><ChapterTitle Language="En">Towards Real Time Epidemiology: Data Assimilation, Modeling and Anomaly Detection of Health Surveillance Data Streams</ChapterTitle><ChapterFirstPage>79</ChapterFirstPage><ChapterLastPage>90</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2007</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></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Luís</GivenName><GivenName>M.</GivenName><GivenName>A.</GivenName><FamilyName>Bettencourt</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Ruy</GivenName><GivenName>M.</GivenName><FamilyName>Ribeiro</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Gerardo</GivenName><FamilyName>Chowell</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Timothy</GivenName><FamilyName>Lant</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff3"><AuthorName DisplayOrder="Western"><GivenName>Carlos</GivenName><FamilyName>Castillo-Chavez</FamilyName></AuthorName></Author><Affiliation ID="Aff1"><OrgName>Theoretical Division and Center for Non-Linear Studies, Los Alamos National Laboratory, MS B284, Los Alamos NM 87545</OrgName><OrgAddress><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Decision Theater, Arizona State University, PO Box 878409, Tempe AZ, 85287-8409</OrgName><OrgAddress><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>Department of Mathematics and Statistics, Arizona State University, PO Box 871804, Tempe AZ, 85287-1804</OrgName><OrgAddress><Country>USA</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>An integrated quantitative approach to data assimilation, prediction and anomaly detection over real-time public health surveillance data streams is introduced. The importance of creating dynamical probabilistic models of disease dynamics capable of predicting future new cases from past and present disease incidence data is emphasized. Methods for real-time data assimilation, which rely on probabilistic formulations and on Bayes’ theorem to translate between probability densities for new cases and for model parameters are developed. This formulation creates future outlook with quantified uncertainty, and leads to natural anomaly detection schemes that quantify and detect disease evolution or population structure changes. Finally, the implementation of these methods and accompanying intervention tools in real time public health situations is realized through their embedding in state of the art information technology and interactive visualization environments.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>real time epidemiology</Keyword><Keyword>data assimilation</Keyword><Keyword>Bayesian inference</Keyword><Keyword>anomaly detection</Keyword><Keyword>interactive visualization</Keyword><Keyword> surveillance</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></SubPart></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Towards Real Time Epidemiology: Data Assimilation, Modeling and Anomaly Detection of Health Surveillance Data Streams</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Towards Real Time Epidemiology: Data Assimilation, Modeling and Anomaly Detection of Health Surveillance Data Streams</title></titleInfo><name type="personal"><namePart type="given">Luís</namePart><namePart type="given">M.</namePart><namePart type="given">A.</namePart><namePart type="family">Bettencourt</namePart><affiliation>Theoretical Division and Center for Non-Linear Studies, Los Alamos National Laboratory, MS B284, Los Alamos NM 87545, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ruy</namePart><namePart type="given">M.</namePart><namePart type="family">Ribeiro</namePart><affiliation>Theoretical Division and Center for Non-Linear Studies, Los Alamos National Laboratory, MS B284, Los Alamos NM 87545, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Gerardo</namePart><namePart type="family">Chowell</namePart><affiliation>Theoretical Division and Center for Non-Linear Studies, Los Alamos National Laboratory, MS B284, Los Alamos NM 87545, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Timothy</namePart><namePart type="family">Lant</namePart><affiliation>Decision Theater, Arizona State University, PO Box 878409, Tempe AZ, 85287-8409, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Carlos</namePart><namePart type="family">Castillo-Chavez</namePart><affiliation>Department of Mathematics and Statistics, Arizona State University, PO Box 871804, Tempe AZ, 85287-1804, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="proceedings" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2007</dateIssued><copyrightDate encoding="w3cdtf">2007</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: An integrated quantitative approach to data assimilation, prediction and anomaly detection over real-time public health surveillance data streams is introduced. The importance of creating dynamical probabilistic models of disease dynamics capable of predicting future new cases from past and present disease incidence data is emphasized. Methods for real-time data assimilation, which rely on probabilistic formulations and on Bayes’ theorem to translate between probability densities for new cases and for model parameters are developed. This formulation creates future outlook with quantified uncertainty, and leads to natural anomaly detection schemes that quantify and detect disease evolution or population structure changes. Finally, the implementation of these methods and accompanying intervention tools in real time public health situations is realized through their embedding in state of the art information technology and interactive visualization environments.</abstract><subject lang="en"><genre>Keywords</genre><topic>real time epidemiology</topic><topic>data assimilation</topic><topic>Bayesian inference</topic><topic>anomaly detection</topic><topic>interactive visualization</topic><topic>surveillance</topic></subject><relatedItem type="host"><titleInfo><title>Intelligence and Security Informatics: Biosurveillance</title><subTitle>Second NSF Workshop, BioSurveillance 2007, New Brunswick, NJ, USA, May 22, 2007. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Daniel</namePart><namePart type="family">Zeng</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Ivan</namePart><namePart type="family">Gotham</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Ken</namePart><namePart type="family">Komatsu</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Cecil</namePart><namePart type="family">Lynch</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Mark</namePart><namePart type="family">Thurmond</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">David</namePart><namePart type="family">Madigan</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bill</namePart><namePart type="family">Lober</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">James</namePart><namePart type="family">Kvach</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Hsinchun</namePart><namePart type="family">Chen</namePart><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">2007</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer 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