Prediction paradigm involving time series applied to total blood issues data from England

Blood products are essential for modern medicine, but managing their collection and supply in the face of fluctuating demands represents a major challenge. As deterministic models based on predicted changes in population have been problematic, there remains a need for more precise and reliable prediction of demands. Here, we propose a paradigm incorporating four different time-series methods to predict red blood cell (RBC) issues 4 to 24 weeks ahead.

We used daily aggregates of RBC units issued from 2005 to 2011 from the National Health Service Blood and Transplant. We generated a new set of nonoverlapping weekly data by summing the daily data over 7 days and derived the average blood issues per week over 4-week periods. We used four methods for linear prediction of blood demand by computing the coefficients with the minimum mean squared error and weighted least squares error algorithms.

We optimized the time-window size, order of the prediction, and order of the polynomial fit for our data set. The four time-series methods, essentially using different weightings to data points, gave very similar results and predicted mean RBC issues with a standard deviation of the percentage error of 3.0% for 4 weeks ahead and 4.0% for 24 weeks ahead.

This paradigm allows prediction of demand for RBCs and could be developed to provide reliable and precise prediction up to 24 weeks ahead to improve the efficiency of blood services and sufficiency of blood supply with reduced costs.