贝叶斯式FitForecast:一个用户友好的R工具箱,用于用普通微分方程进行参数估计和预测
Hamed Karami1, Amanda Bleichrodt2, Ruiyan Luo2
1Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA.
BMC medical informatics and decision making
|October 15, 2025
概括
贝叶斯式FitForecast是一个新的R工具箱,简化了对普通微分方程 (ODE) 模型的贝叶斯式参数估计和预测. 它降低了复杂的动态系统的编码障碍,增强了公共卫生和流行病学决策.
科学领域:
- 计算生物学和生物信息学
- 流行病学和公共卫生.
- 数学建模的数学建模
背景情况:
- 普通微分方程 (ODEs) 对于科学和医疗保健中的动态系统建模至关重要.
- 对ODE模型的贝叶斯校准和预测通常需要广泛的编码专业知识.
- 需要可访问的工具来促进动态系统中的贝叶斯推理.
研究的目的:
- 介绍贝叶斯式FitForecast,一个用户友好的R工具箱.
- 简化ODE模型的贝叶斯参数估计和预测.
- 降低应用贝叶斯方法在健康信息学和公共卫生中的技术障碍.
主要方法:
- 为ODE模型自动生成Stan文件.
- 用户友好的界面用于模型配置和预先定义.
- 应用到历史流行病数据集 (例如,1918年流感,1896年孟买瘟疫) 和模拟数据.
- 评估参数估计和预测性能.
主要成果:
- 证明了可靠的参数估计和预测.
- 成功应用到现实世界和模拟的流行病数据.
- 在不同的观测误差结构下验证的性能 (Poisson,负二项式).
- 提供了全面的模型性能评估工具.
结论:
- 提高时间序列建模和预测先进贝叶斯方法的可访问性.
- 在医疗预测和流行病学研究中广泛应用.
- 包括一个交互式的闪亮的网络应用程序和教程视频用于用户支持.
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