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深的流水流感病毒流行病学:基线值和多种病原体监测的影响

Xiuyuan Shi^1,2, Shisong Fang², Chen Du²

¹School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, 518055, China.

Emerging microbes & infections

|August 26, 2025

概括

污水监测可以准确地追踪流感A型病毒 (IAV),从而预测季节性模式,并优化疫苗接种等公共卫生干预措施. 这种方法提供了IAV和其他呼吸道病毒的早期警告,提高了疾病控制.

科学领域:

环境微生物学
公共卫生监督
流行病学

背景情况:

在COVID-19后,A型流感病毒 (IAV) 的季节性变得不可预测,使公共卫生干预时间变得复杂.
准确的实时监测对于基于证据的公共卫生决定至关重要.
废水监测提供了全民对临床监测的补充方法.

研究的目的:

确定废水中IAV度的动态基线.
优化公共卫生干预的时间,包括疫苗接种和非药物措施.
使用病毒和物理化学参数评估随机森林模型的预测能力.

主要方法:

从2023年3月到2024年3月使用实时PCR对2,764个废水样本进行IAV的量化.
开发和优化一个集成IAV度,流量,温度,COD,TN和TP的随机森林模型.
使用平均值,几何值和中位值建立IAV度基线.

主要成果:

季节性IAV模式在2023年冬季和2024年春季出现峰值.
优化的随机森林模型实现了高精度 (R2 = 0.988,MAE = 2,307).
定义的基线中位数为2 - 4周的早期预警流感季节的开始,并检测出其他呼吸道病毒的异常增加.

结论:

污水监测为优化疫苗接种和干预时间提供了至关重要的数据,特别是在资源有限的环境中.
已建立的IAV基线可以提高早期疫情风险检测和社区健康保护.
随机森林模型有效地使用关键废水参数预测IAV病例.

关键词:

随机森林模型基本情况 A型流感病毒类似流感的感染基于废水的流行病学

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