What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks

Abstract

Traditional biosurveillance algorithms detect disease outbreaks by looking for peaks in a univariate time series of health-care data. Current health-care surveillance data, however, are no longer simply univariate data streams. Instead, a wealth of spatial, temporal, demographic and symptomatic information is available. We present an early disease outbreak detection algorithm called What's Strange About Recent Events (WSARE), which uses a multivariate approach to improve its timeliness of detection. WSARE employs a rule-based technique that compares recent health-care data against data from a baseline distribution and finds subgroups of the recent data whose proportions have changed the most from the baseline data. In addition, health-care data also pose difficulties for surveillance algorithms because of inherent temporal trends such as seasonal effects and day of week variations. WSARE approaches this problem using a Bayesian network to produce a baseline distribution that accounts for these temporal trends. The algorithm itself incorporates a wide range of ideas, including association rules, Bayesian networks, hypothesis testing and permutation tests to produce a detection algorithm that is careful to evaluate the significance of the alarms that it raises.