Temporal anomaly detection is a key component of real time surveillance. Today, despite the abundance of temporal information on multiple syndromes, multivariate investigation of temporal anomalies remains under-explored. Traditionally, an outbreak is thought of as disease localization in time. That is, for an event to qualify as an outbreak, a significant deviation from the observed distribution of the disease must occur. However, the underlying processes that govern the health seeking behavior of a population with respect to one disease can potentially impact multiple syndromes leading to observable correlation patterns in the daily rates of those syndromes. Thus, a deviation from the observed correlation pattern between different syndromes can be an early indicator of potential anomalies when the rise in the daily rates of one or more syndrome is not sufficiently discernable to be identified by standard univariate techniques.
Objective
The objectives of this study are to develop a mathematical multi-syndrome framework for early detection of temporal anomalies, to demonstrate improvement in detection sensitivity and timeliness of the multivariate technique compared with those of standard uni-syndrome analysis, and to put forward a new practical concept for timely outbreak investigation.