Wastewater surveillance has become a powerful tool to monitor circulating viruses at a community level. Currently, most wastewater surveillance efforts use target-based approaches such as quantitative PCR techniques or hybrid capture. This study explores the feasibility of using unbiased ultra-deep metagenomic sequencing as a comprehensive approach to wastewater surveillance. To test this, composite influent wastewater samples were collected weekly from January 2024 through June 2025. Unbiased sequencing was performed on all samples with an average depth of 1.1 billion reads per sample. Human enteric viruses such as rotaviruses, astroviruses, and noroviruses were detected at high levels in virtually every sample. SARS-CoV-2 was also detected in most samples and the counts per sample positively correlated with digital PCR (dPCR) measurements. Less abundant respiratory pathogens such as influenza A and B, rhinoviruses, parainfluenzaviruses, and human coronaviruses 229E, OC43, NL63, and HKU1 were also regularly detected. However, those pathogens displayed distinct and reproducible winter and spring seasonality. Several unexpected viruses were also detected, such as several detections of highly pathogenic avian influenza H5N1 (HPAI H5N1) genotype B3.13, a month-long surge of hepatitis A virus, and a large season-specific surge in influenza C virus. The most abundant known virus detected was the
Tobamovirus
tomato brown rugose fruit virus, which was present year-round at high abundance. However, other tobamoviruses such as tomato mosaic virus were detected primarily in the late growing season. This eighteen-month study highlights that unbiased ultra deep sequencing enables detection of expected and unexpected viral pathogens without targeted enrichment.
Importance
This study demonstrates that untargeted ultra deep metagenomic sequencing can provide a comprehensive and scalable tool for wastewater surveillance of viral pathogens. By generating approximately 1 billion reads per sample across 78 consecutive weeks, we captured expected pathogens such as SARS-CoV-2, noroviruses, and influenza viruses. Additionally, we captured unexpected viral signals such as influenza C and highly pathogenic avian influenza H5N1. The wide range of viral taxa captured in this study also displays epidemiologically relevant seasonality. We also observed a correlation between metagenomic SARS-CoV-2 read counts and dPCR values to validate this method against other wastewater surveillance methods currently in use. Our findings highlight how ultra deep metagenomics can enhance pandemic preparedness, enable early detection of novel or re-emerging viruses, and broaden the scope of One Health monitoring by capturing human, animal, and plant viral signatures from a composite wastewater sample.