Using decision fusion methods to improve outbreak detection in disease surveillance

Vista sencilla de ítem
dc.creatorTexier, Gaëtan
dc.creatorAllodji, Rodrigue S.
dc.creatorDiop, Loty
dc.creatorMeynard, Jean-Baptiste
dc.creatorPellegrin, Liliane
dc.creatorChaudet, Hervé
dc.date2019-08-13
dc.date2024-06-21T09:05:42Z
dc.date2024-06-21T09:05:42Z
dc.date.accessioned2026-06-27T15:15:26Z
dc.descriptionWhen outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors.
dc.identifierhttps://hdl.handle.net/10568/146057
dc.identifier.urihttp://hdl.handle.net/123456789/99058
dc.languageen
dc.publisherSpringer
dc.rightsOpen Access
dc.sourceTexier, Gaëtan; Allodji, Rodrigue S.; Diop, Loty; Meynard, Jean-Baptiste; Pellegrin, Liliane; and Chaudet, Hervé. 2019. Using decision fusion methods to improve outbreak detection in disease surveillance. BMC Medical Informatics and Decision Making 19: 38. https://doi.org/10.1186/s12911-019-0774-3
dc.subjectalgorithms
dc.subjecthealth
dc.subjectdecision-support systems
dc.subjectdecision fusion
dc.subjectcapacity development
dc.subjectbayesian theory
dc.subjectdecision making
dc.subjectdisease surveillance
dc.titleUsing decision fusion methods to improve outbreak detection in disease surveillance
dc.typeJournal Article

Archivos

Colecciones

Instituto Internacional de Investigación sobre Políticas Alimentarias (IFPRI)