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.

献血者分析和献血预测是任何采血中心必须面临的两项关键任务。分析对于有针对性的促销活动非常重要，招募将保证随着时间的推移血液单位的高产量的捐赠者。预测捐献者未来的到达可以适当调整采集中心的规模，并提供有关未来血液单位生产的可靠信息。这两项任务都可以通过捐赠事件强度函数的统计预测模型来解决。我们提出了一个贝叶斯模型，它将这种强度描述为个体捐赠者的随机弱点及其固定时间和时间相关协变量的函数。我们的模型根据捐赠者的个人特征解释了他们第一次捐赠后的行为。我们将其应用于意大利意大利血友协会米兰部门提供的经常性捐赠者的数据。事实证明，我们的方法适合这些数据，但它也可以轻松应用于其他血液采集中心。该方法还可以得出一般指示，并通过定量分析提供给工作人员。