Introduction The SARS-CoV-2 pandemic has caused a global crisis that has impacted not only health care systems, but also economies and societies. The constraints in clinical testing provide challenges in reliably assessing the prevalence of variations, particularly in regions with limited resources, testing, and sequencing capabilities. Sewage-based epidemiology uses SARS-CoV-2 in sewage as an indicator, can monitor and provide early warning of viral transmission in communities, thereby informing response strategies. Methods In this study, sewage samples and clinical patient samples were collected in Yantai City, Shandong Province. RT-qPCR and high-throughput sequencing techniques were employed to identify and analyze SARS-CoV-2, respectively. Results Our results showed that the dynamic trend of SARS-CoV-2 RNA concentration in sewage samples coincided with the positive rate of clinical surveillance cases (Spearman’s ρ = 0.97, p 0.001). A significantly higher number of SARS-CoV-2 lineages were detected in sewage compared to clinical samples (paired t -test, t = 6, df = 4, p 0.05), and the growth of the dominant strain can be detected in sewage samples up to a week in advance. Discussion Our study demonstrates that effluent genomic surveillance is a rapid, sensitive, and scalable method. It enables the timely identification of SARS-CoV-2 variants and the detection of hidden transmission. It can be applied to SARS-CoV-2 early warning as well as epidemiologic surveillance. However, this study has certain limitations. First, due to financial constraints, only a limited number of clinical samples were analyzed, which may have underestimated the diversity of SARS-CoV-2 lineages in the patient population. Second, the absence of information on the physicochemical characteristics of sewage may have limited our understanding of environmental factors affecting viral stability and detection efficiency.
Sun et al. (Tue,) studied this question.