统计模型预测 (StatModPredict):一个用户友好的R-Shiny界面,用于与统计模型相配并进行预测
Amanda Bleichrodt1, Amelia Phan1, Ruiyan Luo1
1Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.
PloS one
|August 7, 2025
概括
StatModPredict是一个R-Shiny仪表板,可以在没有编程的情况下实现高级统计时间序列预测. 它使学生和专业人士能够预测流行病轨迹,并有效地分析时间序列数据.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 生物统计学 生物统计学
背景情况:
- 统计时间序列模型对于公共卫生预测至关重要,但需要广泛的编程知识,限制了可访问性.
- 学生,专业人士和政策制定者往往缺乏使用这些强大的预测工具的编程技能.
研究的目的:
- 为了介绍StatModPredict,一个R-Shiny仪表板,旨在提供可访问和直观的预测分析.
- 为了使具有有限编程经验的用户能够使用各种统计模型进行可靠的预测.
主要方法:
- StatModPredict集成了自动回归集成移动平均线 (ARIMA),通用线性模型 (GLM),通用添加模型 (GAM) 和Meta的先知模型.
- 仪表板支持实时预测,回顾分析,模型拟合,评估,可视化和结果比较.
- 用户可以自定义参数,上传外部预测进行比较,并通过可编辑图形分析时间序列数据.
主要成果:
- R-Shiny仪表板,StatModPredict,成功地降低了时间序列预测的编程障碍.
- 使用美国年度艾滋病毒病例数据的演示展示了仪表板对现实世界预测应用的实用性.
- 该工具促进了各种用户群体的探索和使用,包括学生和公共卫生专业人员.
结论:
- StatModPredict使对复杂的预测工具的访问民主化,促进实践学习和跨学科的更广泛应用.
- 开源界面支持任何使用时间序列数据的领域,增强流行病轨迹预测和数据分析.
- 通过消除技术障碍,StatModPredict促进了对预测方法的更广泛采用和潜在用户贡献.
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