At the Nucleic Acid Observatory (NAO) we’re evaluating pathogen-agnostic surveillance. A key question is whether metagenomic sequencing of wastewater can be a cost-effective method to detect and mitigate future pandemics. In this report we investigate one piece of this question: at a given stage of a viral pandemic, what fraction of wastewater metagenomic sequencing reads would that virus represent?
To make this concrete, we define RA(1%). If 1% of people are infected with some virus (prevalence) or have become infected with it during a given week (incidence), RA(1%) is the fraction of sequencing reads (relative abundance) generated by a given method that would match that virus. To estimate RA(1%) we collected public health data on sixteen human-infecting viruses, re-analyzed sequencing data from four municipal wastewater metagenomic studies, and linked them with a hierarchical Bayesian model.
Three of the viruses were not present in the sequencing data, and we could only generate an upper bound on RA(1%). Four viruses had a handful of reads, for which we were able to generate rough estimates. For the remaining nine viruses we were able to narrow down RA(1%) for a specific virus-method combination to approximately an order of magnitude. We found RA(1%) for these nine viruses varied dramatically, over approximately six orders of magnitude. It also varied by study, with some viruses seeing an RA(1%) three orders of magnitude higher in one study than another.
The NAO plans to use the estimates from this study as inputs into a modeling framework to assess the cost effectiveness of wastewater MGS detection under different pandemic scenarios, and we include an outline of such a framework with some rough estimates of the costs of different monitoring approaches.
Predicting Virus Relative Abundance in Wastewater
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At the Nucleic Acid Observatory (NAO) we’re evaluating pathogen-agnostic surveillance. A key question is whether metagenomic sequencing of wastewater can be a cost-effective method to detect and mitigate future pandemics. In this report we investigate one piece of this question: at a given stage of a viral pandemic, what fraction of wastewater metagenomic sequencing reads would that virus represent?
To make this concrete, we define RA(1%). If 1% of people are infected with some virus (prevalence) or have become infected with it during a given week (incidence), RA(1%) is the fraction of sequencing reads (relative abundance) generated by a given method that would match that virus. To estimate RA(1%) we collected public health data on sixteen human-infecting viruses, re-analyzed sequencing data from four municipal wastewater metagenomic studies, and linked them with a hierarchical Bayesian model.
Three of the viruses were not present in the sequencing data, and we could only generate an upper bound on RA(1%). Four viruses had a handful of reads, for which we were able to generate rough estimates. For the remaining nine viruses we were able to narrow down RA(1%) for a specific virus-method combination to approximately an order of magnitude. We found RA(1%) for these nine viruses varied dramatically, over approximately six orders of magnitude. It also varied by study, with some viruses seeing an RA(1%) three orders of magnitude higher in one study than another.
The NAO plans to use the estimates from this study as inputs into a modeling framework to assess the cost effectiveness of wastewater MGS detection under different pandemic scenarios, and we include an outline of such a framework with some rough estimates of the costs of different monitoring approaches.
Read the full report: Predicting Virus Relative Abundance in Wastewater.