Wastewater surveillance uncovers regional diversity and dynamics of SARS-CoV-2 variants across nine states in the USA

Wastewater-based epidemiology (WBE) is a non-invasive and cost-effective approach for monitoring the spread of a pathogen within a community. WBE has been adopted as one of the methods to monitor the spread and population dynamics of the SARS-CoV-2 virus, but significant challenges remain in the bioinformatic analysis of WBE-derived data. Here, we have developed a new distance metric, CoVdist, and an associated analysis tool that facilitates the ap-plication of ordination analysis to WBE data and the identification of viral population changes based on nucleotide var-iants. We applied these new approaches to a large-scale dataset from 18 cities in nine states of the USA using wastewater collected from July 2021 to June 2022. We found that the trends in the shift between the Delta and Om-icron SARS-CoV-2 lineages were largely consistent with what was seen in clinical data, but that wastewater analysis offered the added benefit of revealing significant differences in viral population dynamics at the state, city, and even neighborhood scales. We also were able to observe the early spread of variants of concern and the presence of recombinant lineages during the transitions between variants, both of which are challenging to analyze based on clinically-derived viral genomes. The methods outlined here will be beneficial for future applications of WBE to mon-itor SARS-CoV-2, particularly as clinical monitoring becomes less prevalent. Additionally, these approaches are gener-alizable, allowing them to be applied for the monitoring and analysis of future viral outbreaks.

Automated summary

Wastewater-based epidemiology is a non-invasive and cost-effective method for monitoring the spread of pathogens. In this study, a new distance metric and analysis tool were developed to analyze wastewater data and identify viral population changes. The findings showed that wastewater analysis can provide additional insights into viral population dynamics compared to clinical data. These methods have the potential to be applied in future viral outbreak monitoring.