Predicting donations and profiling donors in a blood collection center: a Bayesian approach

Donor profiling and donation prediction are two key tasks that any blood collection center must face. Profiling is important to target promotion campaigns, recruiting donors who will guarantee a high production of blood units over time. Predicting the future arrivals of donors allows to size the collection center properly and to provide reliable information on the future production of blood units. Both tasks can be addressed through a statistical prediction model for the intensity function of the donation event. We propose a Bayesian model, which describes this intensity as a function of individual donor's random frailties and their fixed-time and time-dependent covariates. Our model explains donors' behaviors from their first donation based on their individual characteristics. We apply it to data of recurrent donors provided by the Milan department of the Associazione Volontari Italiani del Sangue in Italy. Our method proved to fit those data, but it can also be easily applied to other blood collection centers. The method also allows general indications to be drawn, supported by quantitative analyses, to be provided to staff.