Modern biosurveillance relies on multiple sources of both pre-diagnostic and diagnostic data, up-dated daily, to discover disease outbreaks. Intrinsic to this effort are two assumptions: (1) the data
being analyzed contain early indicators of a disease outbreak and (2)
the outbreaks to be detected are not known a priori. However, in
addition to outbreak indicators, syndromic data
streams include such factors as day-of-week effects, seasonal effects,
autocorrelation, and global trends. These explainable factors obscure
unexplained outbreak events and their presence in the data violates standard control chart assumptions. Monitoring
tools such as Shewhart, CuSum, and EWMA control charts will alert
largely based on these explainable factors instead of on outbreaks. The
goal of this paper is twofold: First, to describe a set of tools for identifying explainable patterns such as temporal dependence, and second, to survey and examine several data preconditioning methods that significantly reduce these explainable factors, yielding data better suited for monitoring using the popular control charts.