时间序列分析和专家判断在模拟和预测津巴布韦献血趋势中的应用
Coster Chideme1, Delson Chikobvu1
1Department of Mathematical Statistics and Actuarial Sciences, University of the Free State, Bloemfontein, South Africa.
MDM policy & practice
|January 22, 2024
概括
使用SARIMA等时间序列模型预测津巴布韦的献血量,揭示了季节性模式,假日期间出现下降. 这些见解有助于规划收集血液的驱动和确保稳定的血液供应.
科学领域:
- 时间序列分析应用于公共卫生资源管理.
- 生物统计学和流行病学建模用于血液供应链优化.
背景情况:
- 人工血液替代品是不可用的,这使得献血者血液对于输血服务至关重要.
- 持续及时收集血液对于维持足够的血液供应水平至关重要.
- 准确预测血液采集趋势对于能力规划和资源分配至关重要.
研究的目的:
- 为了确定2009-2019年在津巴布韦每月献血的模式.
- 开发和比较时间序列模型,以预测未来的献血量.
- 为血液捐献活动的资源分配和时间提供信息.
主要方法:
- 来自津巴布韦国家血液服务局的每月献血数据的回顾性分析.
- 季节性自回归集成移动平均 (SARIMA) 和错误,趋势和季节性 (ETS) 时间序列模型的应用和比较.
- 纳入专家意见,用于模型选择和数据解释.
主要成果:
- 献血具有季节性,在学校假期 (四月,八月,十二月) 和公共假日期间显著下降.
- 这些假期与学校捐款减少和由于事故增加的需求相吻合.
- 确定SARIMA模型是最适合预测的,其表现优于ETS模型.
- 预测表明献血的总体上升趋势.
结论:
- 将专家知识与统计模型相结合,有效地解释了津巴布韦的献血趋势.
- 这些发现支持对献血者活动和资源分配的积极规划.
- 准确的预测确保了稳定的血液库存和高效的血液供应管理.
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