A novel measure for data stream anomaly detection in a bio-surveillance system

The primary concern of a bio-surveillance system is to analyze and interpret data as they are collected and then decide whether further investigation is required. Decision makers need to know whether the data in the current test interval are sufficiently different from expected counts to cause an alert. Despite the fact that a number of detection methods have been proposed, we notice in the literature the users of current systems can still experience extremely high false alarm rate. We propose a novel measure that takes into account both anomaly magnitude and anomaly frequencies for bio-surveillance, and experimental results show that the proposed measure performs better than conventional measures for bio-surveillance.