一种多变量分析,以解释废水流行病学中的病原体度的残留误差
Markus Wallner1, Omar V Müller2, Andrea A Goméz2
1Ostfalia University of Applied Sciences, Faculty of Civil and Environmental Engineering, 29556 Suderburg, Germany.
The Science of the total environment
|December 25, 2024
概括
基于废水的流行病学 (WBE) 病例预测准确性随着关注变体 (VOC) 数据的显著改善. 然而,频繁的VOC变化限制了定量WBE预测,特别是在复杂的废水系统中.
科学领域:
- 环境微生物学环境微生物学
- 流行病学 流行病学
- 废水分析 废水分析
背景情况:
- 在COVID-19期间,基于废水的流行病学 (WBE) 激增,用于人口健康监测.
- WBE通过分析废水病原体度来预测健康病例.
- 目前的WBE预测模型面临由于稀释,衰变和废水矩阵效应的错误.
研究的目的:
- 确定影响预测错误的基于废水的关键流行病学相关参数 (WBERPs).
- 评估WBERP对WBE案例预测准确性的影响.
- 为了在不同类型的废水采集区中比较模型性能.
主要方法:
- 用线性回归和随机森林回归来进行病例预测.
- 使用随机森林回归来确定WBERP的重要性.
- 研究了两个流域:一个有综合下水道,另一个有单独的下水道.
主要成果:
- 关注的变种 (VOC) 被确定为最关键的时间变量WBERP.
- 包括VOC在内,在两个水域中,预测准确度提高了约30% (RMSE).
- 物理化学参数和便标记只显示了边际改善.
- 复杂的废水矩阵 (综合下水道) 对WBE精度的影响更大.
结论:
- 挥发性有机化合物数据显著增强了WBE预测模型,但由于变异演变,引入了实际挑战.
- 挥发性有机化合物的动态性限制了WBE预测的定量准确性.
- 废水矩阵的复杂性影响了WBE的可靠性,影响了病原体度与病例的关系.
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