Cover Image
QR Code

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...

Full description

Main Authors: Texier, Gaëtan, Allodji, Rodrigue S., Diop, Loty, Meynard, Jean-Baptiste, Pellegrin, Liliane, Chaudet, Hervé
Format: Journal Article
Language:Inglés
Published: Springer 2019
Subjects:
algorithms
health
decision-support systems
decision fusion
capacity development
bayesian theory
decision making
disease surveillance
Online Access:https://hdl.handle.net/10568/146057
Summary: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.

Similar Items