Modeling Baseline Shifts in Multivariate Disease Outbreak Detection

Population surges or large events may cause shift of data collected by biosurveillance systems [1]. For example, the Cherry Blossom Festival brings hundreds of thousands of people to DC every year, which results in simultaneous elevations in multiple data streams (Fig. 1). In this paper, we propose an MGD model to accommodate the needs of dealing with baseline shifts.

Objective:

Outbreak detection algorithms monitoring only disease-relevant data streams may be prone to false alarms due to baseline shifts. In this paper, we propose a Multinomial-Generalized-Dirichlet (MGD) model to adjust for baseline shifts.