A Bayesian dynamic model for influenza surveillance
Identifieur interne : 001478 ( Ncbi/Curation ); précédent : 001477; suivant : 001479A Bayesian dynamic model for influenza surveillance
Auteurs : Paola Sebastiani [États-Unis] ; Kenneth D. Mandl [États-Unis] ; Peter Szolovits [États-Unis] ; Isaac S. Kohane [États-Unis] ; Marco F. Ramoni [États-Unis]Source :
- Statistics in medicine [ 0277-6715 ] ; 2006.
Descripteurs français
- KwdFr :
- Enfant d'âge préscolaire, Flambées de maladies, Grippe humaine (), Grippe humaine (virologie), Grippe humaine (épidémiologie), Humains, Modèles biologiques, Modèles statistiques, Nourrisson, Sous-type H5N1 du virus de la grippe A (croissance et développement), Surveillance de la population (), Théorème de Bayes, États-Unis d'Amérique.
- MESH :
- croissance et développement : Sous-type H5N1 du virus de la grippe A.
- virologie : Grippe humaine.
- épidémiologie : Grippe humaine.
- Enfant d'âge préscolaire, Flambées de maladies, Grippe humaine, Humains, Modèles biologiques, Modèles statistiques, Nourrisson, Surveillance de la population, Théorème de Bayes, États-Unis d'Amérique.
- Wicri :
- geographic : États-Unis.
English descriptors
- KwdEn :
- Bayes Theorem, Child, Preschool, Disease Outbreaks, Humans, Infant, Influenza A Virus, H5N1 Subtype (growth & development), Influenza, Human (epidemiology), Influenza, Human (prevention & control), Influenza, Human (virology), Models, Biological, Models, Statistical, Population Surveillance (methods), United States.
- MESH :
- geographic : United States.
- epidemiology : Influenza, Human.
- growth & development : Influenza A Virus, H5N1 Subtype.
- methods : Population Surveillance.
- prevention & control : Influenza, Human.
- virology : Influenza, Human.
- Bayes Theorem, Child, Preschool, Disease Outbreaks, Humans, Infant, Models, Biological, Models, Statistical.
Abstract
The severe acute respiratory syndrome (SARS) epidemic, the growing fear of an influenza pandemic and the recent shortage of flu vaccine highlight the need for surveillance systems able to provide early, quantitative predictions of epidemic events. We use dynamic Bayesian networks to discover the interplay among four data sources that are monitored for influenza surveillance. By integrating these different data sources into a dynamic model, we identify in children and infants presenting to the pediatric emergency department with respiratory syndromes an early indicator of impending influenza morbidity and mortality. Our findings show the importance of modelling the complex dynamics of data collected for influenza surveillance, and suggest that dynamic Bayesian networks could be suitable modelling tools for developing epidemic surveillance systems.
Url:
DOI: 10.1002/sim.2566
PubMed: 16645996
PubMed Central: 4128871
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PMC:4128871Le document en format XML
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We use dynamic Bayesian networks to discover the interplay among four data sources that are monitored for influenza surveillance. By integrating these different data sources into a dynamic model, we identify in children and infants presenting to the pediatric emergency department with respiratory syndromes an early indicator of impending influenza morbidity and mortality. Our findings show the importance of modelling the complex dynamics of data collected for influenza surveillance, and suggest that dynamic Bayesian networks could be suitable modelling tools for developing epidemic surveillance systems.</p></div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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