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Using decision fusion methods to improve outbreak detection in disease surveillance

When 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 severa...

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Autores principales: Texier, Gaëtan, Allodji, Rodrigue S., Diop, Loty, Meynard, Jean-Baptiste, Pellegrin, Liliane, Chaudet, Hervé
Formato: Journal Article
Lenguaje:Inglés
Publicado: Springer 2019
Materias:
algorithms
health
decision-support systems
decision fusion
capacity development
bayesian theory
decision making
disease surveillance
Acceso en línea:https://hdl.handle.net/10568/146057
Sumario:When 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.

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