Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States of America.
Department of Biology, Northern Michigan University, Marquette, Michigan, United States of America.
PLoS One. 2023 Aug 3;18(8):e0289343. doi: 10.1371/journal.pone.0289343. eCollection 2023.
During the COVID-19 pandemic, wastewater-based surveillance has been shown to be a useful tool for monitoring the spread of disease in communities and the emergence of new viral variants of concern. As the pandemic enters its fourth year and clinical testing has declined, wastewater offers a consistent non-intrusive way to monitor community health in the long term. This study sought to understand how accurately wastewater monitoring represented the actual burden of disease between communities. Two communities varying in size and demographics in Michigan were monitored for SARS-CoV-2 in wastewater between March of 2020 and February of 2022. Additionally, each community was monitored for SARS-CoV-2 variants of concern from December 2020 to February 2022. Wastewater results were compared with zipcode and county level COVID-19 case data to determine which scope of clinical surveillance was most correlated with wastewater loading. Pearson r correlations were highest in the smaller of the two communities (population of 25,000) for N1 GC/person/day with zipcode level case data, and date of the onset of symptoms (r = 0.81). A clear difference was seen with more cases and virus signals in the wastewater of the larger community (population 110,000) when examined based on vaccine status, which reached only 50%. While wastewater levels of SARS-CoV-2 had a lower correlation to cases in the larger community, the information was still seen as valuable in supporting public health actions and further data including vaccination status should be examined in the future.
在 COVID-19 大流行期间,基于废水的监测已被证明是监测社区疾病传播和新关注的病毒变体出现的有用工具。随着大流行进入第四个年头,临床检测有所减少,废水提供了一种长期监测社区健康的一致、非侵入性的方法。本研究旨在了解废水监测在多大程度上准确反映了社区之间实际的疾病负担。在密歇根州,两个规模和人口结构不同的社区在 2020 年 3 月至 2022 年 2 月期间对 SARS-CoV-2 进行了废水监测。此外,每个社区还在 2020 年 12 月至 2022 年 2 月期间对关注的 SARS-CoV-2 变体进行了监测。将废水结果与邮政编码和县级 COVID-19 病例数据进行比较,以确定哪种临床监测范围与废水负荷最相关。在两个社区中较小的一个(人口 25000),N1 GC/人/天与邮政编码级别的病例数据和症状开始日期的 Pearson r 相关性最高(r = 0.81)。在人口 110000 的较大社区中,基于疫苗接种情况,废水的病例和病毒信号明显更多,达到了 50%。虽然 SARS-CoV-2 的废水水平与较大社区的病例相关性较低,但这些信息仍被认为对支持公共卫生行动具有价值,未来应进一步研究包括疫苗接种情况在内的更多数据。