获悉随机森林以模拟流行病学先验,政府政策和公共流动性的关联
Tsaone Swaabow Thapelo1, Dimane Mpoeleng2, Gregory Hillhouse3
1Department of Computer Science and Information Systems, Botswana International University of Science and Technology, Palapye, Botswana.
MDM policy & practice
|December 28, 2023
概括
这项研究将适应的SIR流行病模型与随机森林机器学习模型相结合,以改善COVID-19病例预测. 得到信息的随机森林模型实现了84%的预测准确度,超过了默认模型.
科学领域:
- 流行病学和公共卫生.
- 计算生物学和生物信息学
- 传染病建模传染病建模
背景情况:
- 传染病给全球健康和经济带来了重大挑战.
- 目前的建模方法,包括机器学习 (ML) 和基于微分方程的流行病模型,在准确性和一致性方面存在局限性,特别是数据漂移.
- 现有的方法在疾病动态中的参数估计和维度约束方面扎.
研究的目的:
- 开发一个新的框架,将流行病模型与ML集成在一起,以进行增强的传染病分析.
- 为了提高预测2019年冠状病毒病 (COVID-19) 新病例计数 (NCC) 的准确性和一致性.
- 创建一个基于知识的学习框架,用于评估公共卫生干预措施.
主要方法:
- 一个适应的易受感染性恢复 (SIR) 模型被用来为默认的随机森林 (RF) 模型提供信息.
- 已知RF (IRF) 被用于预测新的COVID-19病例 (NCC).
- 使用来自博茨瓦纳 (2020年2月至2022年8月) 的真实数据验证了性能,包括药物和非药物干预,使用损失函数建模的差异.
主要成果:
- 默认的射频模型在建模和预测NCC方面表现出有效性.
- 通知RF有效生殖率显著提高了预测能力,达到84%.
- 这与默认RF的预测功率75%相比,相当于大幅增加.
结论:
- 开发的基于信息的ML框架有效地提高了传染病动态的预测.
- 这种方法提供了一种可靠的方法来评估公共卫生干预措施的影响.
- 这项研究为政策制定者在管理传染病爆发方面提供了宝贵的见解.
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