在俄俄州预测COVID-19:从废水,人口和社会经济数据的洞察力
Fatemeh Rezaeitavabe1, Karen T Coschigano2, Guy Riefler1
1Ohio University, Russ College of Engineering, Department of Civil and Environmental Engineering, Athens, OH 45701, USA.
The Science of the total environment
|February 27, 2025
概括
废水监测有效跟踪COVID-19的传播,机器学习模型准确预测临床病例. 人口和社会经济因素显著影响病毒在社区中的传播和检测率.
科学领域:
- 流行病学 流行病学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 由于人口和社会经济差异的影响,COVID-19 (冠状病毒疾病2019) 对人口产生不均的影响.
- 基于废水的监测 (WBS) 是跟踪SARS-CoV-2 (严重急性呼吸系统综合征冠状病毒2) 的一个有价值的工具,但在预测流行病范围方面面临挑战.
- 了解社区因素如何影响WBS数据对于准确的流行病监测和缓解至关重要.
研究的目的:
- 分析人口和社会经济因素对COVID-19传播和WBS数据的影响.
- 评估机器学习模型在预测临床COVID-19病例中的有效性,从多个社区的WBS数据.
- 确定与临床病例和废水病毒载荷相关的关键因素.
主要方法:
- 分析了来自美国俄俄州55个WBS站点的数据,覆盖人口从3,300到654,817.
- 评估了WBS/临床数据与人口规模,贫困率,种族人口统计和收入中位数等因素之间的相关性.
- 八个机器学习模型被评估了他们使用WBS数据预测临床病例的能力.
主要成果:
- 人口规模,贫困率和种族人口统计 (白人,黑人人口) 与临床病例和WBS结果的相关性最强.
- 人口规模被确定为影响这些相关性的最重要因素.
- 机器学习模型,特别是k-最近邻居 (R2=0.873),随机森林 (R2=0.862) 和XGBoost (R2=0.854),在从WBS数据中预测临床病例方面表现出高准确性.
结论:
- 机器学习具有很大的潜力,可以使用WBS数据预测COVID-19病例,即使在不同的人口和社会经济背景下.
- 当使用先进的统计方法分析WBS时,可以提供对社区级病毒传播的强有力的见解.
- 解决人口和社会经济差异对于有效的流行病减缓战略至关重要.
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