Recursive Least Squares Prediction of Syndromic Data for Surveillance

This paper describes a method to predict syndromic data for surveillance of public health using the method of recursive least squares and a new method of correcting for the day of week effect in order to have a prediction of the background upon which detections of actual events can be computed