We describe a wavelet-based automated algorithm for
detecting disease outbreaks in temporal syndromic data.
We describe the method, which improves upon the
Goldenberg et al. (2002) algorithm and its implementation
on a diverse set of real syndromic data from multiple data
sources and multiple geographical locations. Our results
show a robust performance which is comparable to a few
recently suggested methods.