Bayesian Network Based Early Disease Outbreak Detection Software

Abstract

Traditional bio surveillance algorithms detect disease outbreaks by looking for peaks in a uni-variate time series of health-care data. Current health-care surveillance data, however, are no longer simply uni-variate data streams. Instead, a wealth of spatial, temporal, demographic and symptomatic information is available. Here is 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 which shows trend. 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.