The statistical process control (SPC) community has developed a wealth of robust, sensitive monitoring methods in the form of control charts [1]. Although such charts have been implemented for a wide variety of health monitoring purposes [2], some implementations monitor data that violate basic assumptions required by the control charts [3] yielding alerting methods with uncertain detection performance. This problem highlights an inherent obstacle to the use of traditional SPC methods for syndromic surveillance: the nature of the data. Syndromic data streams are based not on physical science, as are manufacturing processes, but on changing population behavior and evolving data acquisition and classification procedures. To overcome this obstacle, either more sophisticated detection algorithms must be developed or the data must be preconditioned so that it is appropriate for traditional monitoring tools. Objective: For robust detection performance, alerting algorithms for biosurveillance require input data free of trends, day-of-week effects, and other systematic behavior. Time series forecasting methods may be used to remove this behavior by subtracting forecasts from observations to form residuals for algorithmic input. This abstract examines and compares methods for the automatic preconditioning of health indicator data to enable the timely prospective monitoring required for effective syndromic surveillance.
Objective
For robust detection performance, alerting algorithms for biosurveillance require input data free of trends, day-of-week effects, and other systematic behavior. Time series forecasting methods may be used to remove this behavior by subtracting forecasts from observations to form residuals for algorithmic input. This abstract examines and compares methods for the automatic preconditioning of health indicator data to enable the timely prospective monitoring required for effective syndromic surveillance.