An IDEA for short term outbreak projection: nearcasting using the basic reproduction number.
Identifieur interne : 000398 ( Main/Exploration ); précédent : 000397; suivant : 000399An IDEA for short term outbreak projection: nearcasting using the basic reproduction number.
Auteurs : David N. Fisman [Canada] ; Tanya S. Hauck [Canada] ; Ashleigh R. Tuite [Canada] ; Amy L. Greer [Canada]Source :
- PloS one [ 1932-6203 ] ; 2013.
Descripteurs français
- KwdFr :
- Grippe humaine (épidémiologie), Humains (MeSH), Incidence (MeSH), Maladies transmissibles (épidémiologie), Modèles statistiques (MeSH), Nunavut (épidémiologie), Prévision (méthodes), Sous-type H1N1 du virus de la grippe A (MeSH), Taux de reproduction de base (statistiques et données numériques), Épidémies de maladies (statistiques et données numériques).
- MESH :
- méthodes : Prévision.
- statistiques et données numériques : Taux de reproduction de base, Épidémies de maladies.
- épidémiologie : Grippe humaine, Maladies transmissibles, Nunavut.
- Humains, Incidence, Modèles statistiques, Sous-type H1N1 du virus de la grippe A.
English descriptors
- KwdEn :
- Basic Reproduction Number (statistics & numerical data), Communicable Diseases (epidemiology), Disease Outbreaks (statistics & numerical data), Forecasting (methods), Humans (MeSH), Incidence (MeSH), Influenza A Virus, H1N1 Subtype (MeSH), Influenza, Human (epidemiology), Models, Statistical (MeSH), Nunavut (epidemiology).
- MESH :
- geographic , epidemiology : Nunavut.
- epidemiology : Communicable Diseases, Influenza, Human.
- methods : Forecasting.
- statistics & numerical data : Basic Reproduction Number, Disease Outbreaks.
- Humans, Incidence, Influenza A Virus, H1N1 Subtype, Models, Statistical.
Abstract
BACKGROUND
Communicable disease outbreaks of novel or existing pathogens threaten human health around the globe. It would be desirable to rapidly characterize such outbreaks and develop accurate projections of their duration and cumulative size even when limited preliminary data are available. Here we develop a mathematical model to aid public health authorities in tracking the expansion and contraction of outbreaks with explicit representation of factors (other than population immunity) that may slow epidemic growth.
METHODOLOGY
The Incidence Decay and Exponential Adjustment (IDEA) model is a parsimonious function that uses the basic reproduction number R0, along with a discounting factor to project the growth of outbreaks using only basic epidemiological information (e.g., daily incidence counts).
PRINCIPAL FINDINGS
Compared to simulated data, IDEA provides highly accurate estimates of total size and duration for a given outbreak when R0 is low or moderate, and also identifies turning points or new waves. When tested with an outbreak of pandemic influenza A (H1N1), the model generates estimated incidence at the i+1(th) serial interval using data from the i(th) serial interval within an average of 20% of actual incidence.
CONCLUSIONS AND SIGNIFICANCE
This model for communicable disease outbreaks provides rapid assessments of outbreak growth and public health interventions. Further evaluation in the context of real-world outbreaks will establish the utility of IDEA as a tool for front-line epidemiologists.
DOI: 10.1371/journal.pone.0083622
PubMed: 24391797
PubMed Central: PMC3877403
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Fisman</name><affiliation wicri:level="4"><nlm:affiliation>The Dalla Lana School of Public Health, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; The Decision Centre for Infectious Disease Epidemiology (DeCIDE), Toronto, Ontario, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>The Dalla Lana School of Public Health, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; The Decision Centre for Infectious Disease Epidemiology (DeCIDE), Toronto, Ontario</wicri:regionArea><orgName type="university">Université de Toronto</orgName><placeName><settlement type="city">Toronto</settlement><region type="state">Ontario</region></placeName></affiliation></author><author><name sortKey="Hauck, Tanya S" sort="Hauck, Tanya S" uniqKey="Hauck T" first="Tanya S" last="Hauck">Tanya S. Hauck</name><affiliation wicri:level="4"><nlm:affiliation>Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ontario</wicri:regionArea><orgName type="university">Université de Toronto</orgName><placeName><settlement type="city">Toronto</settlement><region type="state">Ontario</region></placeName></affiliation></author><author><name sortKey="Tuite, Ashleigh R" sort="Tuite, Ashleigh R" uniqKey="Tuite A" first="Ashleigh R" last="Tuite">Ashleigh R. Tuite</name><affiliation wicri:level="4"><nlm:affiliation>The Dalla Lana School of Public Health, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; The Decision Centre for Infectious Disease Epidemiology (DeCIDE), Toronto, Ontario, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>The Dalla Lana School of Public Health, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; The Decision Centre for Infectious Disease Epidemiology (DeCIDE), Toronto, Ontario</wicri:regionArea><orgName type="university">Université de Toronto</orgName><placeName><settlement type="city">Toronto</settlement><region type="state">Ontario</region></placeName></affiliation></author><author><name sortKey="Greer, Amy L" sort="Greer, Amy L" uniqKey="Greer A" first="Amy L" last="Greer">Amy L. Greer</name><affiliation wicri:level="4"><nlm:affiliation>The Dalla Lana School of Public Health, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; The Decision Centre for Infectious Disease Epidemiology (DeCIDE), Toronto, Ontario, Canada ; Modeling and Projection Section of the Professional Guidelines and Public Health Practice Division, Centre for Communicable Diseases and Infection Control, Public Health Agency of Canada, Toronto, Ontario, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>The Dalla Lana School of Public Health, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; The Decision Centre for Infectious Disease Epidemiology (DeCIDE), Toronto, Ontario, Canada ; Modeling and Projection Section of the Professional Guidelines and Public Health Practice Division, Centre for Communicable Diseases and Infection Control, Public Health Agency of Canada, Toronto, Ontario</wicri:regionArea><orgName type="university">Université de Toronto</orgName><placeName><settlement type="city">Toronto</settlement><region type="state">Ontario</region></placeName></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Basic Reproduction Number (statistics & numerical data)</term><term>Communicable Diseases (epidemiology)</term><term>Disease Outbreaks (statistics & numerical data)</term><term>Forecasting (methods)</term><term>Humans (MeSH)</term><term>Incidence (MeSH)</term><term>Influenza A Virus, H1N1 Subtype (MeSH)</term><term>Influenza, Human (epidemiology)</term><term>Models, Statistical (MeSH)</term><term>Nunavut (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Grippe humaine (épidémiologie)</term><term>Humains (MeSH)</term><term>Incidence (MeSH)</term><term>Maladies transmissibles (épidémiologie)</term><term>Modèles statistiques (MeSH)</term><term>Nunavut (épidémiologie)</term><term>Prévision (méthodes)</term><term>Sous-type H1N1 du virus de la grippe A (MeSH)</term><term>Taux de reproduction de base (statistiques et données numériques)</term><term>Épidémies de maladies (statistiques et données numériques)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Nunavut</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Communicable Diseases</term><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Forecasting</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Prévision</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Basic Reproduction Number</term><term>Disease Outbreaks</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Taux de reproduction de base</term><term>Épidémies de maladies</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Maladies transmissibles</term><term>Nunavut</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Incidence</term><term>Influenza A Virus, H1N1 Subtype</term><term>Models, Statistical</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term><term>Incidence</term><term>Modèles statistiques</term><term>Sous-type H1N1 du virus de la grippe A</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>Communicable disease outbreaks of novel or existing pathogens threaten human health around the globe. It would be desirable to rapidly characterize such outbreaks and develop accurate projections of their duration and cumulative size even when limited preliminary data are available. Here we develop a mathematical model to aid public health authorities in tracking the expansion and contraction of outbreaks with explicit representation of factors (other than population immunity) that may slow epidemic growth.</p></div><div type="abstract" xml:lang="en"><p><b>METHODOLOGY</b></p><p>The Incidence Decay and Exponential Adjustment (IDEA) model is a parsimonious function that uses the basic reproduction number R0, along with a discounting factor to project the growth of outbreaks using only basic epidemiological information (e.g., daily incidence counts).</p></div><div type="abstract" xml:lang="en"><p><b>PRINCIPAL FINDINGS</b></p><p>Compared to simulated data, IDEA provides highly accurate estimates of total size and duration for a given outbreak when R0 is low or moderate, and also identifies turning points or new waves. When tested with an outbreak of pandemic influenza A (H1N1), the model generates estimated incidence at the i+1(th) serial interval using data from the i(th) serial interval within an average of 20% of actual incidence.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS AND SIGNIFICANCE</b></p><p>This model for communicable disease outbreaks provides rapid assessments of outbreak growth and public health interventions. Further evaluation in the context of real-world outbreaks will establish the utility of IDEA as a tool for front-line epidemiologists.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24391797</PMID><DateCompleted><Year>2014</Year><Month>08</Month><Day>25</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>8</Volume><Issue>12</Issue><PubDate><Year>2013</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>An IDEA for short term outbreak projection: nearcasting using the basic reproduction number.</ArticleTitle><Pagination><MedlinePgn>e83622</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0083622</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Communicable disease outbreaks of novel or existing pathogens threaten human health around the globe. It would be desirable to rapidly characterize such outbreaks and develop accurate projections of their duration and cumulative size even when limited preliminary data are available. Here we develop a mathematical model to aid public health authorities in tracking the expansion and contraction of outbreaks with explicit representation of factors (other than population immunity) that may slow epidemic growth.</AbstractText><AbstractText Label="METHODOLOGY" NlmCategory="METHODS">The Incidence Decay and Exponential Adjustment (IDEA) model is a parsimonious function that uses the basic reproduction number R0, along with a discounting factor to project the growth of outbreaks using only basic epidemiological information (e.g., daily incidence counts).</AbstractText><AbstractText Label="PRINCIPAL FINDINGS" NlmCategory="RESULTS">Compared to simulated data, IDEA provides highly accurate estimates of total size and duration for a given outbreak when R0 is low or moderate, and also identifies turning points or new waves. When tested with an outbreak of pandemic influenza A (H1N1), the model generates estimated incidence at the i+1(th) serial interval using data from the i(th) serial interval within an average of 20% of actual incidence.</AbstractText><AbstractText Label="CONCLUSIONS AND SIGNIFICANCE" NlmCategory="CONCLUSIONS">This model for communicable disease outbreaks provides rapid assessments of outbreak growth and public health interventions. Further evaluation in the context of real-world outbreaks will establish the utility of IDEA as a tool for front-line epidemiologists.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Fisman</LastName><ForeName>David N</ForeName><Initials>DN</Initials><AffiliationInfo><Affiliation>The Dalla Lana School of Public Health, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; The Decision Centre for Infectious Disease Epidemiology (DeCIDE), Toronto, Ontario, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hauck</LastName><ForeName>Tanya S</ForeName><Initials>TS</Initials><AffiliationInfo><Affiliation>Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tuite</LastName><ForeName>Ashleigh R</ForeName><Initials>AR</Initials><AffiliationInfo><Affiliation>The Dalla Lana School of Public Health, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; The Decision Centre for Infectious Disease Epidemiology (DeCIDE), Toronto, Ontario, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Greer</LastName><ForeName>Amy L</ForeName><Initials>AL</Initials><AffiliationInfo><Affiliation>The Dalla Lana School of Public Health, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada ; The Decision Centre for Infectious Disease Epidemiology (DeCIDE), Toronto, Ontario, Canada ; Modeling and Projection Section of the Professional Guidelines and Public Health Practice Division, Centre for Communicable Diseases and Infection Control, Public Health Agency of Canada, Toronto, Ontario, Canada.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>12</Month><Day>31</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D050936" MajorTopicYN="N">Basic Reproduction Number</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003141" MajorTopicYN="N">Communicable Diseases</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="N">Disease Outbreaks</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005544" MajorTopicYN="N">Forecasting</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015994" MajorTopicYN="N">Incidence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D053118" MajorTopicYN="N">Influenza A Virus, H1N1 Subtype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="Y">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D020957" MajorTopicYN="N" Type="Geographic">Nunavut</DescriptorName><QualifierName UI="Q000453" 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sortKey="Fisman, David N" sort="Fisman, David N" uniqKey="Fisman D" first="David N" last="Fisman">David N. Fisman</name></region><name sortKey="Greer, Amy L" sort="Greer, Amy L" uniqKey="Greer A" first="Amy L" last="Greer">Amy L. Greer</name><name sortKey="Hauck, Tanya S" sort="Hauck, Tanya S" uniqKey="Hauck T" first="Tanya S" last="Hauck">Tanya S. Hauck</name><name sortKey="Tuite, Ashleigh R" sort="Tuite, Ashleigh R" uniqKey="Tuite A" first="Ashleigh R" last="Tuite">Ashleigh R. Tuite</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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