A Bayesian approach for detecting a disease that is not being modeled.
Identifieur interne : 000729 ( PubMed/Corpus ); précédent : 000728; suivant : 000730A Bayesian approach for detecting a disease that is not being modeled.
Auteurs : John M. Aronis ; Jeffrey P. Ferraro ; Per H. Gesteland ; Fuchiang Tsui ; Ye Ye ; Michael M. Wagner ; Gregory F. CooperSource :
- PloS one [ 1932-6203 ] ; 2020.
Abstract
Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new or transformed diseases is likely to continue, the detection and characterization of emergent diseases is an important problem. We describe a Bayesian statistical model that can detect and characterize previously unknown and unmodeled diseases from patient-care reports and evaluate its performance on historical data.
DOI: 10.1371/journal.pone.0229658
PubMed: 32109254
Links to Exploration step
pubmed:32109254Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Bayesian approach for detecting a disease that is not being modeled.</title>
<author><name sortKey="Aronis, John M" sort="Aronis, John M" uniqKey="Aronis J" first="John M" last="Aronis">John M. Aronis</name>
<affiliation><nlm:affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Ferraro, Jeffrey P" sort="Ferraro, Jeffrey P" uniqKey="Ferraro J" first="Jeffrey P" last="Ferraro">Jeffrey P. Ferraro</name>
<affiliation><nlm:affiliation>Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Gesteland, Per H" sort="Gesteland, Per H" uniqKey="Gesteland P" first="Per H" last="Gesteland">Per H. Gesteland</name>
<affiliation><nlm:affiliation>Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Tsui, Fuchiang" sort="Tsui, Fuchiang" uniqKey="Tsui F" first="Fuchiang" last="Tsui">Fuchiang Tsui</name>
<affiliation><nlm:affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Ye, Ye" sort="Ye, Ye" uniqKey="Ye Y" first="Ye" last="Ye">Ye Ye</name>
<affiliation><nlm:affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Wagner, Michael M" sort="Wagner, Michael M" uniqKey="Wagner M" first="Michael M" last="Wagner">Michael M. Wagner</name>
<affiliation><nlm:affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Cooper, Gregory F" sort="Cooper, Gregory F" uniqKey="Cooper G" first="Gregory F" last="Cooper">Gregory F. Cooper</name>
<affiliation><nlm:affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</nlm:affiliation>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2020">2020</date>
<idno type="RBID">pubmed:32109254</idno>
<idno type="pmid">32109254</idno>
<idno type="doi">10.1371/journal.pone.0229658</idno>
<idno type="wicri:Area/PubMed/Corpus">000729</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000729</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">A Bayesian approach for detecting a disease that is not being modeled.</title>
<author><name sortKey="Aronis, John M" sort="Aronis, John M" uniqKey="Aronis J" first="John M" last="Aronis">John M. Aronis</name>
<affiliation><nlm:affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Ferraro, Jeffrey P" sort="Ferraro, Jeffrey P" uniqKey="Ferraro J" first="Jeffrey P" last="Ferraro">Jeffrey P. Ferraro</name>
<affiliation><nlm:affiliation>Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Gesteland, Per H" sort="Gesteland, Per H" uniqKey="Gesteland P" first="Per H" last="Gesteland">Per H. Gesteland</name>
<affiliation><nlm:affiliation>Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Tsui, Fuchiang" sort="Tsui, Fuchiang" uniqKey="Tsui F" first="Fuchiang" last="Tsui">Fuchiang Tsui</name>
<affiliation><nlm:affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Ye, Ye" sort="Ye, Ye" uniqKey="Ye Y" first="Ye" last="Ye">Ye Ye</name>
<affiliation><nlm:affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Wagner, Michael M" sort="Wagner, Michael M" uniqKey="Wagner M" first="Michael M" last="Wagner">Michael M. Wagner</name>
<affiliation><nlm:affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Cooper, Gregory F" sort="Cooper, Gregory F" uniqKey="Cooper G" first="Gregory F" last="Cooper">Gregory F. Cooper</name>
<affiliation><nlm:affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</nlm:affiliation>
</affiliation>
</author>
</analytic>
<series><title level="j">PloS one</title>
<idno type="eISSN">1932-6203</idno>
<imprint><date when="2020" type="published">2020</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new or transformed diseases is likely to continue, the detection and characterization of emergent diseases is an important problem. We describe a Bayesian statistical model that can detect and characterize previously unknown and unmodeled diseases from patient-care reports and evaluate its performance on historical data.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="In-Data-Review" Owner="NLM"><PMID Version="1">32109254</PMID>
<DateRevised><Year>2020</Year>
<Month>03</Month>
<Day>11</Day>
</DateRevised>
<Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>15</Volume>
<Issue>2</Issue>
<PubDate><Year>2020</Year>
</PubDate>
</JournalIssue>
<Title>PloS one</Title>
<ISOAbbreviation>PLoS ONE</ISOAbbreviation>
</Journal>
<ArticleTitle>A Bayesian approach for detecting a disease that is not being modeled.</ArticleTitle>
<Pagination><MedlinePgn>e0229658</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0229658</ELocationID>
<Abstract><AbstractText>Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new or transformed diseases is likely to continue, the detection and characterization of emergent diseases is an important problem. We describe a Bayesian statistical model that can detect and characterize previously unknown and unmodeled diseases from patient-care reports and evaluate its performance on historical data.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Aronis</LastName>
<ForeName>John M</ForeName>
<Initials>JM</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0001-5821-3672</Identifier>
<AffiliationInfo><Affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ferraro</LastName>
<ForeName>Jeffrey P</ForeName>
<Initials>JP</Initials>
<AffiliationInfo><Affiliation>Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Gesteland</LastName>
<ForeName>Per H</ForeName>
<Initials>PH</Initials>
<AffiliationInfo><Affiliation>Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Tsui</LastName>
<ForeName>Fuchiang</ForeName>
<Initials>F</Initials>
<AffiliationInfo><Affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ye</LastName>
<ForeName>Ye</ForeName>
<Initials>Y</Initials>
<AffiliationInfo><Affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Wagner</LastName>
<ForeName>Michael M</ForeName>
<Initials>MM</Initials>
<AffiliationInfo><Affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Cooper</LastName>
<ForeName>Gregory F</ForeName>
<Initials>GF</Initials>
<AffiliationInfo><Affiliation>Real-time Outbreak and Disease Surveillance (RODS) Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2020</Year>
<Month>02</Month>
<Day>28</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>United States</Country>
<MedlineTA>PLoS One</MedlineTA>
<NlmUniqueID>101285081</NlmUniqueID>
<ISSNLinking>1932-6203</ISSNLinking>
</MedlineJournalInfo>
<CitationSubset>IM</CitationSubset>
<CoiStatement>The authors have declared that no competing interests exist.</CoiStatement>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2019</Year>
<Month>09</Month>
<Day>04</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2020</Year>
<Month>02</Month>
<Day>12</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2020</Year>
<Month>2</Month>
<Day>29</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2020</Year>
<Month>2</Month>
<Day>29</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2020</Year>
<Month>2</Month>
<Day>29</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>epublish</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">32109254</ArticleId>
<ArticleId IdType="doi">10.1371/journal.pone.0229658</ArticleId>
<ArticleId IdType="pii">PONE-D-19-24937</ArticleId>
<ArticleId IdType="pmc">PMC7048291</ArticleId>
</ArticleIdList>
<ReferenceList><Reference><Citation>Biomed Res Int. 2015;2015:751738</Citation>
<ArticleIdList><ArticleId IdType="pubmed">26425552</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>Science. 2017 Jul 14;357(6347):149-152</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28706037</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>Nature. 2018 Jun;558(7709):180-182</Citation>
<ArticleIdList><ArticleId IdType="pubmed">29880819</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>J Biomed Inform. 2015 Feb;53:15-26</Citation>
<ArticleIdList><ArticleId IdType="pubmed">25181466</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>Emerg Infect Dis. 2016 Aug;22(8):1387-94</Citation>
<ArticleIdList><ArticleId IdType="pubmed">27434186</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>Emerg Infect Dis. 2019 Sep;25(9):1676-1682</Citation>
<ArticleIdList><ArticleId IdType="pubmed">31407660</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
<ReferenceList><Reference><Citation>J Public Health (Oxf). 2018 Dec 1;40(4):878-885</Citation>
<ArticleIdList><ArticleId IdType="pubmed">29059331</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
</PubmedData>
</pubmed>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/SrasV1/Data/PubMed/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000729 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd -nk 000729 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Sante |area= SrasV1 |flux= PubMed |étape= Corpus |type= RBID |clé= pubmed:32109254 |texte= A Bayesian approach for detecting a disease that is not being modeled. }}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/RBID.i -Sk "pubmed:32109254" \ | HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd \ | NlmPubMed2Wicri -a SrasV1
![]() | This area was generated with Dilib version V0.6.33. | ![]() |