Disease surveillance benchmarks
Building tools and protocols to benchmark wastewater biosurveillance performance
New methods, new standards
Throughout the pandemic, governments utilized wastewater monitoring to detect and quantify emerging infectious disease outbreaks. They employed qPCR to track the spread of COVID-19, and amplicon sequencing to identify and trace SARS-CoV-2 variants. Building on this track record, the NAO team is exploring broader detection methods, such as metagenomic sequencing, to identify, flag, and investigate newly emerging and unknown pathogens. As an emerging technology, however, metagenomic sequencing of wastewater requires comprehensive evaluation, prompting the NAO to develop both computational and wet-lab benchmarking tools.
Benchmarking wastewater metagenomics
While the sensitivity of metagenomic sequencing can be measured in controlled laboratory settings, its performance in real-world environments is still under investigation. For example, it is crucial to determine whether metagenomic sequencing can reliably track the level of pathogen shed into wastewater. Establishing a direct correlation between specific amounts of shed pathogens and quantitative downstream measurements is essential for evaluating the potential of wastewater metagenomics for pathogen-agnostic early warning.
Nucleic-acid tracers as a benchmarking tool
To assess and improve the performance of wastewater monitoring against a known ground truth, the NAO has developed a unique library of DNA-based, non-toxic, and environmentally benign tracers for deposition into wastewater systems. The synthetic genomes encapsulated in these tracers encode regions designed for detection and quantitation through qPCR, amplicon sequencing, and metagenomics, allowing the amount of tracer released at a given location to be quantitatively linked to the amount present in a downstream sample. Large-scale experiments with these tracers could help build a comprehensive understanding of the performance of wastewater disease monitoring under different conditions, bringing us closer to the goal of robust & reliable pathogen detection.