对贝叶斯和频率主义方法进行流行病预测的比较研究:从模拟和历史数据的洞察力
Hamed Karami1, Ruiyan Luo2, Pejman Sanaei1
1Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA.
Statistical methods in medical research
|October 25, 2025
概括
这项研究比较了贝叶斯式和频率式的流行病预测方法. 两种方法都没有一贯优于其他方法,性能因流行病阶段和数据质量而异.
科学领域:
- 流行病学 流行病学
- 计算统计学 计算统计学
- 数学建模的数学建模
背景情况:
- 准确的流行病预测对公共卫生至关重要.
- 确定性隔间模型被广泛用于流行病模拟.
- 对比估计框架对于提高预测准确性至关重要.
研究的目的:
- 为了比较贝叶斯式和频率式的流行病预测估计框架.
- 在共享的建模结构和错误假设下评估预测性能.
- 根据流行病阶段和数据质量,为选择适当的估计策略提供指导.
主要方法:
- 贝叶斯推理 (Stan中的MCMC采样) 和Frequentist非线性最小平方 (NLS) 优化的比较.
- 使用模拟数据集和历史爆发 (1918年流感,孟买瘟疫,COVID-19) 的评估.
- 性能指标包括平均绝对误差 (MAE),根平均平方误差 (RMSE),加权间隔得分 (WIS) 和95%的预测间隔覆盖率.
主要成果:
- 预测性能因流行阶段和数据集而异;没有任何一种方法占主导地位.
- 频率主义方法在高峰期 (模拟) 和高峰期 (真实爆发) 后都表现出色,但在高峰期前的准确性较低.
- 贝叶斯方法,特别是具有统一的先验,显示出更高的早期流行病准确性和更好的不确定性量化与稀疏/杂的数据.
结论:
- 频率主义方法经常提供更准确的点预测 (较低的MAE,RMSE,WIS),但不那么强大的间隔估计.
- 贝叶斯方法提供了更强大的不确定性量化,特别是在早期或数据有限的阶段.
- 估计策略的选择应考虑流行病阶段和数据特征,以优化基于预测的决策.
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