Estimation of hospital emergency room data using OTC pharmaceutical sales and normalized LMS filters

We address the problem of surveillance of over-the-counter pharmaceutical (OTC) sales as a potential early indicator of developing public health conditions from the viewpoint of linear adaptive filter theory: the clinical data is the primary channel to be estimated from the OTC data that form the reference channels. The estimation is performed using an FIR filter and the normalized LMS algorithm. Our results indicate that multichannel adaptive FIR least square filtering is a viable means of estimating public health conditions from OTC sales, and provide quantitative measures of time dependent correlations between the clinical data and the OTC data channels.