WSARE: What's Strange About Recent Events?

This article presents an algorithm for performing early detection of disease outbreaks by searching a database of emergency department cases for anomalous patterns. Traditional techniques for anomaly detection are unsatisfactory for this problem because they identify individual data points that are rare due to particular combinations of features. Thus, these traditional algorithms discover isolated outliers of particularly strange events, such as someone accidentally shooting their ear, that are not indicative of a new outbreak. Instead, we would like to detect groups with specific characteristics that have a recent pattern of illness that is anomalous relative to historical patterns. We propose using an anomaly detection algorithm that would characterize each anomalous pattern with a rule. The significance of each rule would be carefully evaluated using the Fisher exact test and a randomization test. In this study, we compared our algorithm with a standard detection algorithm by measuring the number of false positives and the timeliness of detection. Simulated data, produced by a simulator that creates the effects of an epidemic on a city, were used for evaluation. The results indicate that our algorithm has significantly better detection times for common significance thresholds while having a slightly higher false positive rate.