颗粒洞察:一种基于废水的机器学习方法,用于本地化COVID-19住院预测
Nusrat Tabassum1,2, Mohammad Mihrab Chowdhury1,2, Christopher S McMahan3
1Center for Public Health Modeling and Response, Clemson University, Clemson, SC, USA.
medRxiv : the preprint server for health sciences
|July 16, 2025
概括
基于废水的流行病学 (WBE) 准确预测COVID-19住院病例,提前可达14天. 这种方法增强了公共卫生监测和传染病的医疗保健规划.
科学领域:
- 环境微生物学环境微生物学
- 流行病学 流行病学
- 公共卫生监督是对公共卫生的监督.
背景情况:
- 基于废水的流行病学 (WBE) 是社区健康监测的新兴工具.
- 预测疾病住院情况有助于医疗保健资源管理和准备.
研究的目的:
- 评估废水中的SARS-CoV-2RNA,以预测南卡罗来纳州的COVID-19住院病例.
- 评估WBE在废水处理厂和邮政编码层面的预测效果.
主要方法:
- 从2020年4月至2021年2月期间,从六个废水处理厂 (WWTP) 分析了SARS-CoV-2RNA度.
- 利用波桑回归和随机森林模型预测7,14,21天的住院预测.
- 与全州医院住院索赔数据相比,验证了模型性能.
主要成果:
- 随机森林模型显示14天前的COVID-19住院预测的准确度最高.
- 在WWTP中实现了91.16%的中位数百分比协议,在邮政编码中达到78.12%.
- 在精细的地理尺度上展示了强大和及时的预测能力.
结论:
- 基于废水的流行病学提供了一种可靠的方法来预测传染病住院治疗.
- 开发的建模框架可适应对其他传染病的监测.
- 通过早期发现疾病趋势,WBE增强了公共卫生应对和规划.
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