Real-time epidemic forecasting for pandemic influenza.
Identifieur interne : 000565 ( Main/Exploration ); précédent : 000564; suivant : 000566Real-time epidemic forecasting for pandemic influenza.
Auteurs : I M Hall [Royaume-Uni] ; R. Gani ; H E Hughes ; S. LeachSource :
- Epidemiology and infection [ 0950-2688 ] ; 2007.
Descripteurs français
- KwdFr :
- MESH :
- Wicri :
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : Hong Kong, Spain.
- epidemiology : Influenza, Human.
- Disease Outbreaks, Forecasting, Humans, Influenza A Virus, H5N1 Subtype, Models, Theoretical, Regression Analysis, Time Factors.
Abstract
The ongoing worldwide spread of the H5N1 influenza virus in birds has increased concerns of a new human influenza pandemic and a number of surveillance initiatives are planned, or are in place, to monitor the impact of a pandemic in near real-time. Using epidemiological data collected during the early stages of an outbreak, we show how the timing of the maximum prevalence of the pandemic wave, along with its amplitude and duration, might be predicted by fitting a mass-action epidemic model to the surveillance data by standard regression analysis. This method is validated by applying the model to routine data collected in the United Kingdom during the different waves of the previous three pandemics. The success of the method in forecasting historical prevalence suggests that such outbreaks conform reasonably well to the theoretical model, a factor which may be exploited in a future pandemic to update ongoing planning and response.
DOI: 10.1017/S0950268806007084
PubMed: 16928287
PubMed Central: PMC2870596
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Leach</name></author></analytic><series><title level="j">Epidemiology and infection</title><idno type="ISSN">0950-2688</idno><imprint><date when="2007" type="published">2007</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Disease Outbreaks (MeSH)</term><term>Forecasting (MeSH)</term><term>Hong Kong (epidemiology)</term><term>Humans (MeSH)</term><term>Influenza A Virus, H5N1 Subtype (MeSH)</term><term>Influenza, Human (epidemiology)</term><term>Models, Theoretical (MeSH)</term><term>Regression Analysis (MeSH)</term><term>Spain (epidemiology)</term><term>Time Factors (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de régression (MeSH)</term><term>Espagne (épidémiologie)</term><term>Facteurs temps (MeSH)</term><term>Grippe humaine (épidémiologie)</term><term>Hong Kong (épidémiologie)</term><term>Humains (MeSH)</term><term>Modèles théoriques (MeSH)</term><term>Prévision (MeSH)</term><term>Sous-type H5N1 du virus de la grippe A (MeSH)</term><term>Épidémies de maladies (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Hong Kong</term><term>Spain</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Espagne</term><term>Grippe humaine</term><term>Hong Kong</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Disease Outbreaks</term><term>Forecasting</term><term>Humans</term><term>Influenza A Virus, H5N1 Subtype</term><term>Models, Theoretical</term><term>Regression Analysis</term><term>Time Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse de régression</term><term>Facteurs temps</term><term>Humains</term><term>Modèles théoriques</term><term>Prévision</term><term>Sous-type H5N1 du virus de la grippe A</term><term>Épidémies de maladies</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Hong Kong</term><term>Espagne</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The ongoing worldwide spread of the H5N1 influenza virus in birds has increased concerns of a new human influenza pandemic and a number of surveillance initiatives are planned, or are in place, to monitor the impact of a pandemic in near real-time. Using epidemiological data collected during the early stages of an outbreak, we show how the timing of the maximum prevalence of the pandemic wave, along with its amplitude and duration, might be predicted by fitting a mass-action epidemic model to the surveillance data by standard regression analysis. This method is validated by applying the model to routine data collected in the United Kingdom during the different waves of the previous three pandemics. The success of the method in forecasting historical prevalence suggests that such outbreaks conform reasonably well to the theoretical model, a factor which may be exploited in a future pandemic to update ongoing planning and response.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">16928287</PMID><DateCompleted><Year>2007</Year><Month>05</Month><Day>17</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0950-2688</ISSN><JournalIssue CitedMedium="Print"><Volume>135</Volume><Issue>3</Issue><PubDate><Year>2007</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Epidemiology and infection</Title><ISOAbbreviation>Epidemiol. Infect.</ISOAbbreviation></Journal><ArticleTitle>Real-time epidemic forecasting for pandemic influenza.</ArticleTitle><Pagination><MedlinePgn>372-85</MedlinePgn></Pagination><Abstract><AbstractText>The ongoing worldwide spread of the H5N1 influenza virus in birds has increased concerns of a new human influenza pandemic and a number of surveillance initiatives are planned, or are in place, to monitor the impact of a pandemic in near real-time. Using epidemiological data collected during the early stages of an outbreak, we show how the timing of the maximum prevalence of the pandemic wave, along with its amplitude and duration, might be predicted by fitting a mass-action epidemic model to the surveillance data by standard regression analysis. This method is validated by applying the model to routine data collected in the United Kingdom during the different waves of the previous three pandemics. The success of the method in forecasting historical prevalence suggests that such outbreaks conform reasonably well to the theoretical model, a factor which may be exploited in a future pandemic to update ongoing planning and response.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Hall</LastName><ForeName>I M</ForeName><Initials>IM</Initials><AffiliationInfo><Affiliation>Microbial Risk Assessment, Health Protection Agency, Centre for Emergency Preparedness and Response, Porton Down, Wiltshire, UK. 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Gani</name><name sortKey="Hughes, H E" sort="Hughes, H E" uniqKey="Hughes H" first="H E" last="Hughes">H E Hughes</name><name sortKey="Leach, S" sort="Leach, S" uniqKey="Leach S" first="S" last="Leach">S. Leach</name></noCountry><country name="Royaume-Uni"><noRegion><name sortKey="Hall, I M" sort="Hall, I M" uniqKey="Hall I" first="I M" last="Hall">I M Hall</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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