Review: Wastewater Surveillance Methods for SARS-CoV-2 Variant Tracking and Beyond
A comprehensive review of end-to-end workflows for wastewater-based variant surveillance, comparing sequencing-dependent approaches (amplicon and hybrid-capture sequencing) with sequencing-independent methods (RT-dPCR, allele-specific RT-qPCR, nested PCR coupled with LC-MS). Discusses bottlenecks including primer drift, low-frequency variant detection, and quality control challenges. Outlines requirements for automated on-site monitoring systems and extension to multi-pathogen and antimicrobial resistance surveillance.
The original study
Tracking viral variants by wastewater surveillance, from laboratory diagnosis towards on-site monitoring: lessons learned from the SARS-CoV-2 pandemic.
- Authors
- Zhang Q, Gao C, Ge X, Sun Y, Liu P, Zhang XX
- Journal
- Journal of environmental management
- PMID
- 41844030
Original abstract
Wastewater-based epidemiology (WBE) provides a scalable, population-level biosurveillance layer that complements clinical testing for monitoring SARS-CoV-2 circulation, particularly when diagnostic participation, access, or representativeness is limited. As SARS-CoV-2 continues to evolve, wastewater surveillance has expanded beyond quantifying total viral RNA to also resolving signature mutations and mixed lineage compositions in complex matrices. This review synthesizes end-to-end workflows for variants-directed WBE, spanning sample collection and viral signal enrichment, sequencing-dependent approaches (tiled-amplicon and hybrid-capture sequencing coupled with lineage deconvolution of mixed samples), and sequencing-independent approaches based on targeted mutation detection, including RT-dPCR, allele-specific RT-qPCR, and nested-PCR coupled with LC-MS. We compare these modalities in terms of resolution, sensitivity, turnaround time, and operational constraints, and highlight recurrent bottlenecks such as uneven genome coverage, low-frequency mutation detection, primer/assay drift, and the need for robust quality control and benchmarking under real wastewater conditions. To reduce end-to-end latency and improve sustainability, we outline requirements and research priorities for integrated, automated on-site (or near-site) monitoring systems, emphasizing compact enrichment/extraction modules, field-deployable detection chemistries, and rapid reporting pipelines. Finally, we extend the COVID-era framework beyond SARS-CoV-2, discussing how wastewater and environmental surveillance can be institutionalized as a multi-hazard public health intelligence platform supporting multiplex respiratory panels, pathogen-agnostic sequencing for anomaly detection, mobility-linked sentinel networks, and One Health applications such as antimicrobial resistance monitoring.