Choi You-Jung, Kim Lan Hee, Jang Jinhwa, Park Shin Young, Jeong Sejin, Kim Sungpyo, Lee Sangwon, Kim Seong-Sun
Data Analysis Team, Epidemiological Investigation and Analysis Task Force, Central Disease Control Headquarters, Korea Disease Control and Prevention Agency, Cheongju, Korea.
Research Institute for Advanced Industrial Technology, Korea University, Sejong, Korea.
J Korean Med Sci. 2025 Aug 4;40(30):e94. doi: 10.3346/jkms.2025.40.e94.
BACKGROUND: Wastewater surveillance (WS) technology has gained significant attention in many countries due to its role in the monitoring of infectious diseases within communities and complementing clinical testing to prevent coronavirus disease 2019 (COVID-19) outbreaks. In 2023, the Korea Disease Control and Prevention Agency (KDCA) launched the Korea Wastewater Surveillance (KOWAS) project in collaboration with 17 cities and provinces to track COVID-19 outbreaks. METHODS: From January to August 2023, the concentrations of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) E gene in wastewater were monitored in 19 institutes of health and environmental research, all within local governments. Influent samples were collected from 62 wastewater treatment plants (WWTPs) and weekly trends in SARS-CoV-2 E gene concentrations in wastewater were compared to those of new COVID-19 cases. RESULTS: During 34 weeks, the concentration of SARS-CoV-2 in wastewater samples closely mirrored the trends in new COVID-19 cases, showing the effectiveness of WS in detecting the presence of the virus. However, the efficacy of the WS method varied between provinces. Although some provinces showed a significant positive correlation between new COVID-19 cases and SARS-CoV-2 E gene concentrations in wastewater, this correlation was inconsistent between all locations. However, when data were analyzed on a broader regional scale, defined as a grouping of multiple provinces, a higher proportion of regions showed significant correlations. This suggests that analyzing WS data on a broader scale, with larger aggregated populations and higher coverage rates, reduces the influence of local variabilities, such as the proportion of combined sewer types, WWTPs coverage rate, and foot traffic, which may affect alignment at the provincial level. CONCLUSION: The synchrony between trends in SARS-CoV-2 E gene concentrations in wastewater and new COVID-19 cases highlights the effectiveness of KOWAS in tracking new clinical cases. However, measured SARS-CoV-2 RNA concentrations can be affected by socioenvironmental factors (e.g., WWTP treatment capacity, sewer pipeline distances, and coverage populations). Further refinement will involve expanding the surveillance network to include additional WWTPs and a more comprehensive range of monitoring targets.
背景:废水监测(WS)技术因其在社区传染病监测中的作用以及作为临床检测的补充手段以预防2019冠状病毒病(COVID-19)爆发,而在许多国家受到了广泛关注。2023年,韩国疾病控制与预防机构(KDCA)与17个市和省合作启动了韩国废水监测(KOWAS)项目,以追踪COVID-19疫情。 方法:2023年1月至8月,对地方政府下属的19个卫生与环境研究机构的废水样本中严重急性呼吸综合征冠状病毒2(SARS-CoV-2)E基因的浓度进行了监测。从62个污水处理厂(WWTPs)采集进水样本,并将废水中SARS-CoV-2 E基因浓度的每周变化趋势与新增COVID-19病例的趋势进行比较。 结果:在34周内,废水样本中SARS-CoV-2的浓度与新增COVID-19病例的趋势密切相关,显示了废水监测在检测病毒存在方面的有效性。然而,废水监测方法的效果在不同省份之间存在差异。尽管一些省份的新增COVID-19病例与废水中SARS-CoV-2 E基因浓度之间呈现出显著的正相关,但这种相关性在所有地区并不一致。然而,当在更广泛的区域尺度上进行数据分析时,即将多个省份归为一组,显示出显著相关性的地区比例更高。这表明在更大的总人口和更高覆盖率的更广泛尺度上分析废水监测数据,可以减少当地变量的影响,如合流制下水道类型的比例、污水处理厂的覆盖率以及人流量,这些因素可能会影响省级层面的一致性。 结论:废水中SARS-CoV-2 E基因浓度趋势与新增COVID-19病例之间的同步性凸显了KOWAS在追踪新临床病例方面的有效性。然而,所测SARS-CoV-2 RNA浓度可能会受到社会环境因素(如污水处理厂处理能力、下水道管道距离和覆盖人口)的影响。进一步的改进将包括扩大监测网络,纳入更多污水处理厂和更全面的监测目标。
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