| This thesis presents an early disease outbreak detection algorithm called What's Strange About Recent Events (WSARE). Unlike traditional disease outbreak detection algorithms that look for peaks in a univariate time series of health-care data, WSARE tries to improve its timeliness of detection by taking a novel multivariate approach. Current health-care data used for surveillance are no longer simply a time series of aggregate daily counts. Instead, a wealth of spatial; temporal, demographic, and symptom information is available. WSARE incorporates all of this information using a rule-based approach that compares recent health-care data against data from a baseline distribution and finds subgroups of the data whose proportions have changed the most in the recent 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 powerful detection algorithm that is careful to evaluate the significance of the alarms that it raises. |
