Using decision fusion methods to improve outbreak detection in disease surveillance
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Springer
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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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algorithms, health, decision-support systems, decision fusion, capacity development, bayesian theory, decision making, disease surveillance