Real-time epidemic forecasting for pandemic influenza
Identifieur interne : 001B29 ( Istex/Corpus ); précédent : 001B28; suivant : 001B30Real-time epidemic forecasting for pandemic influenza
Auteurs : I. M. Hall ; R. Gani ; H. E. Hughes ; S. LeachSource :
- Epidemiology and Infection [ 0950-2688 ] ; 2007-04.
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.
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DOI: 10.1017/S0950268806007084
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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.</p></abstract></profileDesc><revisionDesc><change when="2006-08-24">Created</change><change when="2007-04">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/ark:/67375/6GQ-XVDK77QR-W/fulltext.txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="corpus cambridge not found" wicri:toSee="no header"><istex:xmlDeclaration>version='1.0' encoding='UTF-8'</istex:xmlDeclaration><istex:docType PUBLIC="-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.0 20120330//EN" URI="JATS-journalpublishing1.dtd" name="istex:docType"></istex:docType><istex:document><article dtd-version="1.0" article-type="research-article"><front><journal-meta><journal-id journal-id-type="publisher-id">HYG</journal-id><journal-title-group><journal-title>Epidemiology and Infection</journal-title><abbrev-journal-title>Epidemiol. 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M.</given-names></name><aff>Microbial Risk Assessment, Health Protection Agency, <institution>Centre for Emergency Preparedness and Response</institution>, Porton Down, Wiltshire, <country>UK</country></aff></contrib><contrib><name><surname>GANI</surname><given-names>R.</given-names></name><aff>Microbial Risk Assessment, Health Protection Agency, <institution>Centre for Emergency Preparedness and Response</institution>, Porton Down, Wiltshire, <country>UK</country></aff></contrib><contrib><name><surname>HUGHES</surname><given-names>H. 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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. 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