Modeling Baseline Shifts in Multivariate Disease Outbreak Detection

Methods Existing multivariate algorithms only model disease-relevant data streams (e.g., anti-fever medication sales or patient visits with constitutional syndrome for detection of flu outbreak). On the contrary, we also incorporate a non-disease-relevant data stream as a control factor. We assume that the counts from all data streams follow a Multinomial distribution. Given this distribution, the expected value of the distribution parameter is not subject to change during a baseline shift; however, it has to change in order to model an outbreak. Therefore, this distribution inherently adjusts for the baseline shifts. In addition, we use the generalized Dirichlet (GD) distribution to model the parameter, since GD distribution is one of the conjugate prior of Multinomial [2]. We call this model the Multinomial-Generalized-Dirichlet (MGD) model.