Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data.
Identifieur interne : 000016 ( Main/Exploration ); précédent : 000015; suivant : 000017Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data.
Auteurs : L J Martin [Canada] ; H. Dong [Canada] ; Q. Liu [Canada] ; J. Talbot [Canada] ; W. Qiu [Canada] ; Y. Yasui [Canada, États-Unis]Source :
- Epidemiology and infection [ 1469-4409 ] ; 2019.
Descripteurs français
- KwdFr :
- Adolescent (MeSH), Adulte (MeSH), Adulte d'âge moyen (MeSH), Alberta (épidémiologie), Analyse spatio-temporelle (MeSH), Enfant (MeSH), Enfant d'âge préscolaire (MeSH), Grippe humaine (thérapie), Grippe humaine (épidémiologie), Humains (MeSH), Jeune adulte (MeSH), Modèles biologiques (MeSH), Modèles linéaires (MeSH), Nourrisson (MeSH), Nouveau-né (MeSH), Saisons (MeSH), Service hospitalier d'urgences (statistiques et données numériques), Sous-type H1N1 du virus de la grippe A (MeSH), Sujet âgé (MeSH), Sujet âgé de 80 ans ou plus (MeSH), Surveillance de la santé publique (méthodes), Utilisation des installations et des services (statistiques et données numériques).
- MESH :
- méthodes : Surveillance de la santé publique.
- statistiques et données numériques : Service hospitalier d'urgences, Utilisation des installations et des services.
- thérapie : Grippe humaine.
- épidémiologie : Alberta, Grippe humaine.
- Adolescent, Adulte, Adulte d'âge moyen, Analyse spatio-temporelle, Enfant, Enfant d'âge préscolaire, Humains, Jeune adulte, Modèles biologiques, Modèles linéaires, Nourrisson, Nouveau-né, Saisons, Sous-type H1N1 du virus de la grippe A, Sujet âgé, Sujet âgé de 80 ans ou plus.
English descriptors
- KwdEn :
- Adolescent (MeSH), Adult (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Alberta (epidemiology), Child (MeSH), Child, Preschool (MeSH), Emergency Service, Hospital (statistics & numerical data), Facilities and Services Utilization (statistics & numerical data), Humans (MeSH), Infant (MeSH), Infant, Newborn (MeSH), Influenza A Virus, H1N1 Subtype (MeSH), Influenza, Human (epidemiology), Influenza, Human (therapy), Linear Models (MeSH), Middle Aged (MeSH), Models, Biological (MeSH), Public Health Surveillance (methods), Seasons (MeSH), Spatio-Temporal Analysis (MeSH), Young Adult (MeSH).
- MESH :
- geographic , epidemiology : Alberta.
- epidemiology : Influenza, Human.
- methods : Public Health Surveillance.
- statistics & numerical data : Emergency Service, Hospital, Facilities and Services Utilization.
- therapy : Influenza, Human.
- Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Humans, Infant, Infant, Newborn, Influenza A Virus, H1N1 Subtype, Linear Models, Middle Aged, Models, Biological, Seasons, Spatio-Temporal Analysis, Young Adult.
Abstract
Predicting the magnitude of the annual seasonal peak in influenza-like illness (ILI)-related emergency department (ED) visit volumes can inform the decision to open influenza care clinics (ICCs), which can mitigate pressure at the ED. Using ILI-related ED visit data from the Alberta Real Time Syndromic Surveillance Net for Edmonton, Alberta, Canada, we developed (training data, 1 August 2004-31 July 2008) and tested (testing data, 1 August 2008-19 February 2014) spatio-temporal statistical prediction models of daily ILI-related ED visits to estimate high visit volumes 3 days in advance. Our Main Model, based on a generalised linear mixed model with random intercept, incorporated prediction residuals over 14 days and captured increases in observed volume ahead of peaks. During seasonal influenza periods, our Main Model predicted volumes within ±30% of observed volumes for 67%-82% of high-volume days and within 0.3%-21% of observed seasonal peak volumes. Model predictions were not as successful during the 2009 H1N1 pandemic. Our model can provide early warning of increases in ILI-related ED visit volumes during seasonal influenza periods of differing intensities. These predictions may be used to support public health decisions, such as if and when to open ICCs, during seasonal influenza epidemics.
DOI: 10.1017/S0950268819001948
PubMed: 31787127
PubMed Central: PMC7003624
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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H1N1 du virus de la grippe A</term><term>Sujet âgé</term><term>Sujet âgé de 80 ans ou plus</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Predicting the magnitude of the annual seasonal peak in influenza-like illness (ILI)-related emergency department (ED) visit volumes can inform the decision to open influenza care clinics (ICCs), which can mitigate pressure at the ED. Using ILI-related ED visit data from the Alberta Real Time Syndromic Surveillance Net for Edmonton, Alberta, Canada, we developed (training data, 1 August 2004-31 July 2008) and tested (testing data, 1 August 2008-19 February 2014) spatio-temporal statistical prediction models of daily ILI-related ED visits to estimate high visit volumes 3 days in advance. Our Main Model, based on a generalised linear mixed model with random intercept, incorporated prediction residuals over 14 days and captured increases in observed volume ahead of peaks. During seasonal influenza periods, our Main Model predicted volumes within ±30% of observed volumes for 67%-82% of high-volume days and within 0.3%-21% of observed seasonal peak volumes. Model predictions were not as successful during the 2009 H1N1 pandemic. Our model can provide early warning of increases in ILI-related ED visit volumes during seasonal influenza periods of differing intensities. These predictions may be used to support public health decisions, such as if and when to open ICCs, during seasonal influenza epidemics.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">31787127</PMID><DateCompleted><Year>2020</Year><Month>04</Month><Day>02</Day></DateCompleted><DateRevised><Year>2020</Year><Month>04</Month><Day>02</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1469-4409</ISSN><JournalIssue CitedMedium="Internet"><Volume>147</Volume><PubDate><Year>2019</Year><Month>12</Month><Day>02</Day></PubDate></JournalIssue><Title>Epidemiology and infection</Title><ISOAbbreviation>Epidemiol. Infect.</ISOAbbreviation></Journal><ArticleTitle>Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data.</ArticleTitle><Pagination><MedlinePgn>e312</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1017/S0950268819001948</ELocationID><Abstract><AbstractText>Predicting the magnitude of the annual seasonal peak in influenza-like illness (ILI)-related emergency department (ED) visit volumes can inform the decision to open influenza care clinics (ICCs), which can mitigate pressure at the ED. Using ILI-related ED visit data from the Alberta Real Time Syndromic Surveillance Net for Edmonton, Alberta, Canada, we developed (training data, 1 August 2004-31 July 2008) and tested (testing data, 1 August 2008-19 February 2014) spatio-temporal statistical prediction models of daily ILI-related ED visits to estimate high visit volumes 3 days in advance. Our Main Model, based on a generalised linear mixed model with random intercept, incorporated prediction residuals over 14 days and captured increases in observed volume ahead of peaks. During seasonal influenza periods, our Main Model predicted volumes within ±30% of observed volumes for 67%-82% of high-volume days and within 0.3%-21% of observed seasonal peak volumes. Model predictions were not as successful during the 2009 H1N1 pandemic. Our model can provide early warning of increases in ILI-related ED visit volumes during seasonal influenza periods of differing intensities. These predictions may be used to support public health decisions, such as if and when to open ICCs, during seasonal influenza epidemics.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Martin</LastName><ForeName>L J</ForeName><Initials>LJ</Initials><Identifier Source="ORCID">0000-0002-8034-1221</Identifier><AffiliationInfo><Affiliation>School of Public Health, University of Alberta, Edmonton, AB, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Dong</LastName><ForeName>H</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>School of Public Health, University of Alberta, Edmonton, AB, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Q</ForeName><Initials>Q</Initials><AffiliationInfo><Affiliation>School of Public Health, University of Alberta, Edmonton, AB, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Talbot</LastName><ForeName>J</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>School of Public Health, University of Alberta, Edmonton, AB, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Qiu</LastName><ForeName>W</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>School of Public Health, University of Alberta, Edmonton, AB, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yasui</LastName><ForeName>Y</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>School of Public Health, University of Alberta, Edmonton, AB, Canada.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>12</Month><Day>02</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Epidemiol Infect</MedlineTA><NlmUniqueID>8703737</NlmUniqueID><ISSNLinking>0950-2688</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000416" MajorTopicYN="N" Type="Geographic">Alberta</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004636" MajorTopicYN="N">Emergency Service, Hospital</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000078336" MajorTopicYN="N">Facilities and Services Utilization</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007223" MajorTopicYN="N">Infant</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007231" MajorTopicYN="N">Infant, Newborn</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D053118" MajorTopicYN="Y">Influenza A Virus, H1N1 Subtype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000628" MajorTopicYN="N">therapy</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016014" MajorTopicYN="N">Linear Models</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008954" MajorTopicYN="Y">Models, Biological</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D062486" MajorTopicYN="N">Public Health Surveillance</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012621" MajorTopicYN="N">Seasons</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D062211" MajorTopicYN="Y">Spatio-Temporal Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Emergency medical services</Keyword><Keyword MajorTopicYN="Y">epidemiologic methods</Keyword><Keyword MajorTopicYN="Y">population surveillance</Keyword><Keyword MajorTopicYN="Y">prediction modelling</Keyword><Keyword MajorTopicYN="Y">respiratory tract infections</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>12</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>12</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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Dong</name><name sortKey="Liu, Q" sort="Liu, Q" uniqKey="Liu Q" first="Q" last="Liu">Q. Liu</name><name sortKey="Qiu, W" sort="Qiu, W" uniqKey="Qiu W" first="W" last="Qiu">W. Qiu</name><name sortKey="Talbot, J" sort="Talbot, J" uniqKey="Talbot J" first="J" last="Talbot">J. Talbot</name><name sortKey="Yasui, Y" sort="Yasui, Y" uniqKey="Yasui Y" first="Y" last="Yasui">Y. Yasui</name></country><country name="États-Unis"><region name="Tennessee"><name sortKey="Yasui, Y" sort="Yasui, Y" uniqKey="Yasui Y" first="Y" last="Yasui">Y. Yasui</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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