Wastewater surveillance without prior lineage classification for reliable real time SARS-CoV-2 variant identification

The COVID-19 pandemic remains paradigmatic for the urgency of identifying emerging variants of rapidly mutating viruses in near real time. Grasping the infection dynamics enables better management of public health measures, including the timely allocation of resources. Wastewater surveillance has proven effective in estimating infection incidence and detecting variants in particular if testing rates declined due to milder disease manifestations. However, current methods typically rely on the prior classification of SARS-CoV-2 lineages or their signature mutations, which hampers the speed of variants detection. We present an alternative method that overcomes this limitation by identifying genetic changes in the viral population over time without requiring prior lineage classification. This approach was applied to wastewater samples from plants covering Swiss catchments in Altenrhein, St. Gallen, Geneva, and Zurich. To address noise, only samples with read depths above 40 and genome coverage of at least 90% were included. Genetic diversity within pooled populations over two time periods was compared to assess changes in viral composition. Application of this novel method enabled detection of shifts in genetic populations that corresponded to the emergence of known variants of concern and of the Omicron variant with reasonable precision without prior lineage classification. Notably the approach overcame the inherently high genomic noise in wastewater compared to clinical samples. In summary, we introduce a valuable tool for reliable, real time predictions for the emergence of potentially threatening virus variants from waste water samples that overcomes the need for prior lineage classification and high patient samples.