Large-scale analysis of SARS-CoV-2 genomic data from sewage using eVarEPS reveals that amino acid mutations detected in sewage provide an early warning on population prevalence

Key amino acid mutations in SARS-CoV-2 genomes drive the emergence of variants of concern, impacting virus transmission and immune escape. However, there is still a lack of large-scale monitoring and risk assessment of new amino acid variants in sewage. To address this gap, we developed eVarEPS (Environmental SARS-CoV-2 Variations Evaluation and Prewarning System), a system for the early identification and assessment of SARS-CoV-2 variants in environmental samples. We identified new amino acid mutations in 27,762 sewage sequencing datasets from public databases and published literature and carried out risk assessment of these variants. In addition, we collected 1976 sewage samples and 3817 human samples from Shenzhen from November 2022 to January 2024, employing eVarEPS to monitor and assess the risk of new variants. Using eVarEPS, we detected 5543 unique amino acid variants in global sewage samples, and multi-dimensional risk assessment of amino acid variants suggested that the variants with strong receptor affinity, weak neutralizing antibody affinity, and low difficulty of amino acid change were more likely to spread in the population (P < 0.01). We identified 1345 high-risk variants specifically identified in sewage, which should be focused on. We identified variants detected in sewage an average of 147 days earlier than in human populations, with 46.62% of these new amino acid variants subsequently spreading in the population. In addition, risk assessment showed amino acid variations with higher risk are more likely to prevail in the population (64.93 %, P < 0.001). Application of eVarEPS in the Shenzhen region, we detected the characteristic 19 amino acid variants of major lineages in Shenzhen sewage, one month to one year earlier than the discovery of the corresponding lineages. We conclude that tracking new SARS-CoV-2 amino acid variants in sewage offers more sensitive and timely insights into population prevalence. High-risk mutations identified by eVarEPS are more likely to spread and should be closely monitored. This study enhances our understanding of SARS-CoV-2 variation dynamics and provides an efficient method for future pathogen monitoring and early warning. eVarEPS has been developed into a database and online tool that can be freely accessed at https://nmdc.cn/ncovn/analyze/eVarEPS.