背景。血液不能人工制造,目前没有人类血液的替代品。输血设施中的血液供应需要持续和及时地从献血者那里收集血液。血液采集的建模和预测趋势对于确定当前和未来的容量需求以及适当的血液供应模型至关重要。Objectives.这项研究的目的是确定血液收集或献血模式,并开发时间序列模型,这些模型可以在给定历史数据的情况下进行更新和完善,以预测津巴布韦未来的献血。材料和方法。2009年至2019年期间的每月献血数据是从津巴布韦国家血液服务数据库中回顾性收集的。时间序列模型(即,季节性自回归综合移动平均线[SARIMA]和误差,应用趋势和季节性[ETS])模型并进行比较。选择模型是因为它们能够处理献血数据中明显的季节性和其他时间序列成分。在选择模型和进行分析时,使用了专家意见和经验。结果。献血的时间序列图显示了季节性模式,与津巴布韦学校假期相关的几个月内献血显著下降(4月,August,和12月)和公众假期。在这些假期里,学校捐赠者的数量减少了,同时,由于道路交通事故,血液需求不断增加。模型识别程序建立了SARIMA(1,1,2)(0,1,1)12模型作为预测津巴布韦献血总量的适当模型。结果和预测显示献血呈上升趋势。根据所使用的精度测量,SARIMA模型优于ETS模型。Conclusions.献血过程中的专业知识,再加上统计模型,可以帮助解释津巴布韦献血数据中表现出的趋势。这些发现有助于血液当局计划献血者运动。这些发现是向献血分配更多资源以及何时收集更多血液单位的关键指标。越来越多的献血预测确保了在不久的将来稳定的血库库存。
■一种SARIMA模型可用于预测津巴布韦的献血流量。季节性献血模式在3月份达到高峰,6月/7月,和九月。捐款低谷在4月份,August,十二月,和一月。这些月恰逢津巴布韦的学校假期。SARIMA和ETS模型都提供了类似的预测,但是在预测津巴布韦的献血流量时,拟合度和专业知识的衡量标准略低于SARIMA(1,1,2)(0,1,1)12模型。这些模型结果对于指导献血资源的分配和献血驱动时机是有用的。
Background. Blood cannot be artificially manufactured, and there is currently no substitute for human blood. The supply of blood in transfusion facilities requires constant and timely collection of blood from donors. Modeling and forecasting trends in blood collections are critical for determining both the current and future capacity requirements and appropriate models of adequate blood provision. Objectives. The objective of this study is to determine blood collection or donation patterns and develop time-series models that can be updated and refined in predicting future blood donations in Zimbabwe when given the historical data. Materials and Methods. Monthly blood donation data for the period 2009 to 2019 were collected retrospectively from the National Blood Service Zimbabwe database. Time-series models (i.e., the Seasonal Autoregressive Integrated Moving Average [SARIMA] and Error, Trend and Seasonal [
ETS]) models were applied and compared. The models were chosen because of their ability to handle the seasonality and other time-series components evident in the blood donation data. Expert opinions and experience were used in selecting the models and in making inferences in the analysis. Results. Time-series plots of blood donations showed seasonal patterns, with significant drops in blood donations in months associated with Zimbabwe\'s school holidays (April, August, and December) and public holidays. During these holidays, there is a reduced number of school donors, while at about the same time, there is increasing blood demand as a result of road accidents. Model identification procedures established the SARIMA(1,1,2)(0,1,1)12 model as the appropriate model for forecasting total blood donation in Zimbabwe. The results and forecasts show an upward trend in blood donations. According to the accuracy measures used, the SARIMA model outperforms the
ETS model. Conclusions. Expert knowledge in the blood donation process, coupled with statistical models, can help explain trends exhibited in blood donation data in Zimbabwe. These findings help the blood authorities plan for blood donor campaign drives. The findings are key indicators of where to allocate more resources toward blood donation and when to collect more blood units. The increasing blood donation projections ensure a stable blood bank inventory in the near future.
UNASSIGNED: A SARIMA model can be used to predict the flow of blood donations in Zimbabwe.The seasonal blood donation pattern peaks in the months of March, June/July, and September.The donations troughs are in the months of April, August, December, and January. These are the months coinciding with school holidays in Zimbabwe.Both the SARIMA and
ETS models provided similar forecasts, but measures of fit and expert knowledge gave a slight preference to the SARIMA(1,1,2)(0,1,1)12 model in predicting the flow of blood donations in Zimbabwe.These model results are useful for guiding allocation of blood donation resources and blood donation drive timing.