Simon L. Grimm

Wastewater monitoring enables non-invasive, population-scale tracking of community infections independent of healthcare-seeking behavior and clinical diagnosis. Metagenomic sequencing extends this capability by enabling broad, pathogen-agnostic detection of hundreds of viral taxa, including emerging threats. Here, we present data from CASPER (the Coalition for Agnostic Sequencing of Pathogens from Environmental Reservoirs), a U.S.-based wastewater metagenomic sequencing network designed for deep, untargeted pathogen monitoring at national scale. This release includes 1,206 samples collected between December 2023 and December 2025 from 27 sites across nine states, covering 13 million people. Deep sequencing (∼1 billion read pairs per sample) generated 1.2 trillion read pairs (358 terabases), capturing diverse human, animal, and plant viruses including rare taxa. CASPER data tracks wastewater digital PCR and clinical abundance trends for SARS-CoV-2, influenza A, and respiratory syncytial virus, and recovers national-scale variant dynamics for SARS-CoV-2 and norovirus. As the largest publicly available untargeted wastewater sequencing dataset to date, CASPER provides a shared and growing resource for pathogen surveillance and microbial ecology.

Detecting and monitoring airborne viruses is critical for mitigating the spread of infectious diseases and safeguarding public health. Indoor air sampling has emerged as a promising but underexplored approach to the surveillance of viral pathogens, complementing more established methods such as clinical diagnostics and wastewater-based epidemiology. This review examines the current state of knowledge on indoor air sampling for viral nucleic acid detection, focusing on the sources and composition of viral bioaerosols, sampling technologies, and strategies for effective implementation. Indoor air contains viral particles originating from human occupants through exhaled respiratory droplets, skin shedding, and resuspension of contaminated dust, making it a valuable target for viral biosurveillance. Although viruses typically account for less than 1% of total sequences in metagenomic studies of air, a wide range of human-infecting viruses, including respiratory and skin-associated pathogens, have been successfully detected in air samples using various capture mechanisms, such as filtration, impaction, impingement, cyclonic separation, electrostatic precipitation, and condensation. However, challenges remain in optimizing sampling efficiency, particularly for submicrometer particles, and in recovering sufficient viral nucleic acids for analysis. Potential strategies for pathogen biosurveillance include active air sampling in high-traffic settings such as airports, public transit systems, and healthcare facilities, as well as passive collection of settled dust from surfaces. The use of heating, ventilation, and air conditioning (HVAC) systems for viral bioaerosol monitoring is also promising, but further research is needed to establish its effectiveness. Future studies should focus on addressing knowledge gaps, optimizing sampling methodologies, and developing best practices for integrating air sampling into public health surveillance systems while considering privacy implications.

Background: Metagenomic sequencing of wastewater (W-MGS) can in principle detect any known or novel pathogen in a population. We quantify the sensitivity and cost of W-MGS for viral pathogen detection by jointly analysing W-MGS and epidemiological data for a range of human-infecting viruses. Methods: Sequencing data from four studies were analysed to estimate the relative abundance (RA) of 11 human-infecting viruses. Corresponding prevalence and incidence estimates were obtained or calculated from academic and public-health reports. These estimates were combined using a hierarchical Bayesian model to predict RA at set prevalence or incidence values, allowing comparison across studies and viruses. These predictions were then used to estimate the sequencing depth and concomitant cost required for pathogen detection using W-MGS with or without use of a hybridization-capture enrichment panel. Findings: After controlling for variation in local infection rates, relative abundance varied by orders of magnitude across studies for a given virus. For instance, a local SARS-CoV-2 weekly incidence of 1% corresponds to predicted SARS-CoV-2 relative abundance ranging from 3.8 × 10 ⁻¹⁰ to 2.4 × 10 ⁻⁷ across studies, translating to orders-of-magnitude variation in the cost of operating a system able to detect a SARS-CoV-2-like pathogen at a given sensitivity. Use of a respiratory virus enrichment panel in two studies dramatically increased predicted relative abundance of SARS-CoV-2, lowering yearly costs by 24-to 29-fold for a system able to detect a SARS-CoV-2-like pathogen before reaching 0.01% cumulative incidence. Interpretation: The large variation in viral relative abundance after controlling for epidemiological factors indicates that other sources of inter-study variation, such as differences in sewershed hydrology and lab protocols, have a substantial impact on the sensitivity and cost of W-MGS. Well-chosen hybridization capture panels can dramatically increase sensitivity and reduce cost for viruses in the panel, but may reduce sensitivity to unknown or unexpected pathogens. Funding: Wellcome Trust; Open Philanthropy; Musk Foundation Research In Context: Evidence before this study: Numerous other studies have performed wastewater metagenomic sequencing (W-MGS), with a range of objectives. However, few have explicitly examined the performance of W-MGS as a monitoring tool. We searched PubMed between database inception and September 2024, using the search terms “MGS OR Metagenomic sequencing OR Metagenomics OR Shotgun sequencing” AND “Performance OR Precision OR Sensitivity OR Cost-effectiveness OR Effectiveness” AND “Virus OR Viral OR Virome” AND “Wastewater OR Sewage”. Among the 88 resulting studies, 17 focused specifically on viruses in wastewater. A 2023 UK study by Child and colleagues assessed hybridization-capture and untargeted sequencing of wastewater for genomic epidemiology, concluding that the former but not the latter provided sufficient coverage for effective variant tracking. However, they did find untargeted sequencing sufficient for presence/absence calls of human pathogens in wastewater, a finding supported by numerous other W-MGS studies. While several studies examined the effect of different W-MGS protocols on viral abundance and composition, none accounted for epidemiological or study effects, and none explicitly quantified the sensitivity and cost of W-MGS for viral detection. Added value of this study: To our knowledge, this study provides the first quantitative assessment of the sensitivity and cost of untargeted and hybridization-capture W-MGS for pathogen surveillance. Linking a large corpus of public wastewater metagenomic sequencing with epidemiological data in a Bayesian model, we predict pathogen relative abundance in W-MGS data at set infection prevalence or incidence, and estimate concomitant read-depth and cost requirements for effective detection across different studies and viruses. Our flexible modelling framework provides a valuable tool for evaluation of sequencing-based surveillance in other contexts. Implications of all the available evidence: The sensitivity of untargeted W-MGS varies greatly with pathogen and study design, and large gaps in our understanding remain for pathogens not present in our data. As untargeted W-MGS protocols undergo further improvements, our Bayesian modelling framework is an effective tool for evaluating the sensitivity of new protocols under different epidemiological conditions. While less pathogen-agnostic, hybridization capture can dramatically increase the sensitivity of W-MGS-based pathogen monitoring, and our findings support piloting it as a tool for biosurveillance of known viruses.