Inferring the sensitivity of wastewater metagenomic sequencing for pathogen early detection

Abstract

AbstractDetecting novel pathogens at an early stage requires robust early warning that is both sensitive and pathogen-agnostic. Wastewater metagenomic sequencing (W-MGS) could meet these goals, but its sensitivity and financial feasibility depend on the relative abundance of novel pathogen sequences in W-MGS data. Here we collate W-MGS data from a diverse range of studies to characterize the relative abundance of known viruses in wastewater. We then develop a Bayesian statistical model to integrate these data with epidemiological estimates for 13 human-infecting viruses, and use it to estimate the expected relative abundance of different viral pathogens for a given prevalence or incidence in the community. Our results reveal pronounced variation between studies, with estimates differing by one to three orders of magnitude for the same pathogen: for example, the expected relative abundance of SARS-CoV-2 at 1% weekly incidence varied between 10-7and 10-10. Integrating these estimates with a simple cost model highlights similarly wide inter-study and inter-pathogen variation in the cost of W-MGS-based early detection, with a mean yearly cost estimate of roughly $19,000 for a Norovirus-like pathogen and $2.9 million for a SARS-CoV-2-like pathogen at 1% incidence. The model and parameter estimates presented here represent an important resource for future investigation into the performance of wastewater MGS, and can be extended to incorporate new wastewater datasets as they become available.