Chen Chung-Yen, Chang Yu-Hsiang, Chen Chi-Hsin Sally, Chang Sui-Yuan, Chan Chang-Chuan, Chen Pau-Chung, Su Ta-Chen
Department of Environmental and Occupational Medicine, National Taiwan University Hospital Yunlin Branch, No. 95, Xuefu Rd., Huwei Township, Yunlin County 632, Taiwan; Department of Environmental and Occupational Medicine, National Taiwan University Hospital, No. 1, Changde St., Zhongzheng Dist., Taipei City 100, Taiwan; Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, No. 17, Xuzhou Rd., Taipei City 100, Taiwan.
Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, No. 17, Xuzhou Rd., Taipei City 100, Taiwan.
Ecotoxicol Environ Saf. 2025 Jul 1;299:118299. doi: 10.1016/j.ecoenv.2025.118299. Epub 2025 May 20.
As COVID-19 shifts toward endemicity, ongoing surveillance remains critical to identifying and containing potential outbreaks, particularly in high-risk settings. Wastewater monitoring at targeted institutions offers a promising approach for early detection; however, its utility in forecasting broader epidemic trends remains underexplored. This study aimed to establish the wastewater surveillance platform for SARS-CoV-2 in a University Hospital to forecast the epidemic at the hospital, the surrounding community, and the city levels. During April and October 2022, we conducted routine wastewater sampling at seven sampling wells across the campus twice weekly. The direct viral RNA capture method was adopted for the pretreatment, concentration, and extraction of viral RNA. The presence of SARS-CoV-2 RNA in the wastewater samples was detected and quantified with RT-qPCR targeting N1, N2, and E-gene. SARS-CoV-2 signals relative to pepper mild mottle virus were calculated. Simple linear regression models were used to model the future moving averages of cumulative confirmed cases per 100,000 population at the hospital, community, and city levels. High consistency was observed in the E, N1, and N2 gene targets. Even with only eight new cases in the Zhongzheng District (5.42 per 100,000 population) and 145 cases in the entire city (5.85 per 100,000 population), the virus can be detected in sewage, indicating promising sensitivity. The relative viral signals in the wastewater were strongly associated with future epidemiological indicators at the hospital, community, and city levels. Wastewater sampling and quantification of SARS-CoV-2 is proven to be an efficient and robust method for the tracking and forecasting of infection trends within and beyond hospital settings.
随着新冠病毒转向地方性流行，持续监测对于识别和遏制潜在疫情爆发仍然至关重要，尤其是在高风险环境中。在目标机构进行废水监测为早期发现提供了一种有前景的方法；然而，其在预测更广泛疫情趋势方面的效用仍未得到充分探索。本研究旨在建立某大学医院的新冠病毒废水监测平台，以预测医院、周边社区及城市层面的疫情。在2022年4月至10月期间，我们每周两次在校园内的七个采样井进行常规废水采样。采用直接病毒RNA捕获方法对病毒RNA进行预处理、浓缩和提取。使用针对N1、N2和E基因的逆转录定量聚合酶链反应（RT-qPCR）检测和定量废水样本中新冠病毒RNA的存在情况。计算相对于辣椒轻斑驳病毒的新冠病毒信号。使用简单线性回归模型对医院、社区和城市层面每10万人口累计确诊病例的未来移动平均值进行建模。在E、N1和N2基因靶点上观察到高度一致性。即使在中正区仅有8例新增病例（每10万人口5.42例）且全市仅有145例病例（每10万人口5.85例）的情况下，仍能在污水中检测到病毒，表明灵敏度良好。废水中的相对病毒信号与医院、社区和城市层面未来的流行病学指标密切相关。事实证明，对新冠病毒进行废水采样和定量分析是一种高效且可靠的方法，可用于跟踪和预测医院内外的感染趋势。
