基于废水监测的大学校园COVID-19病例估计,使用机器学习回归模型
Kavindra Yohan Kuhatheva Senaratna1, Sumedha Bhatia1, Goh Shin Giek2
1NUS Environmental Research Institute, National University of Singapore, T-Lab Building, 5A Engineering Drive 1, Singapore 117411, Singapore.
The Science of the total environment
|October 13, 2023
概括
废水监测有效地估计了大学校园中的COVID-19病例,使用随机森林模型. 这种非侵入性方法补充了临床测试,以便及时响应公共卫生问题.
科学领域:
- 环境科学 环境科学
- 流行病学 流行病学
- 数据科学数据科学数据科学
背景情况:
- 废水监测 (WS) 通过监测污水中的SARS-CoV-2RNA来管理COVID-19大流行至关重要.
- 使用WS数据在校园中估计活跃的COVID-19病例需要强大的预测模型.
研究的目的:
- 开发和比较回归模型,以利用废水监测数据在大学校园估计活跃的COVID-19病例.
- 确定校园级和宿舍级COVID-19流行率预测的最准确模型.
主要方法:
- 开发和比较了八种回归模型 (线性回归,多项回归,GAM,LOESS,KNN,SVR,ANN,RF).
- 分析了废水中的SARS-CoV-2RNA度.
- 在多变量模型中,学生人口被纳入为第二个独立变量.
主要成果:
- 多变量随机森林 (RF) 回归模型在校园和宿舍两级预测COVID-19流行率方面表现出最高的准确性.
- 调整正常化的SARS-CoV-2数据并使用多变量建模显著改善了模型性能.
- 最终的RF校园模型在未见数据上实现了0.97的相关系数和20%的平均绝对误差 (MAE).
结论:
- 废水监测,特别是使用多变量随机森林模型,提供了一种准确且非侵入性的方法来估计大学校园中的COVID-19流行率.
- 这种方法可以有效地补充临床试验,使公共卫生干预和反应更快.
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