Use of multiple data streams to conduct Bayesian biologic surveillance.
INTRODUCTION
Emergency department (ED) records and over-the-counter (OTC) sales data are two of the most commonly used sources of data for syndromic surveillance. The majority of detection algorithms monitor these data sources separately and either do not combine them or combine them in an ad hoc fashion. This report outlines a new causal model that combines the two data sources coherently to perform outbreak detection.


OBJECTIVES
This report describes the extension of the Population-wide Anomaly Detection and Assessment (PANDA) Bayesian biologic surveillance algorithm to combine information from multiple data streams. It also outlines the assumptions and techniques used to make this approach scalable for real-time surveillance of a large population.


METHODS
A causal Bayesian network model used previously was extended to incorporate evidence from daily OTC sales data. At the level of individual persons, the actions that result in the purchase of OTC products and in admission to an ED were modeled.


RESULTS
Preliminary results indicate that this model has a tractable running time consisting of 209 seconds for initialization and approximately 4 seconds for every hour's worth of ED data, as measured on a Pentium-4 three-Gigahertz machine with two Gigabytes of RAM.


CONCLUSION
Preliminary results for surveillance using a new Bayesian algorithm that models the interaction between ED and OTC data are positive regarding the run time of the algorithm.