We propose a minimum set of meta-information to accompany the reporting of SARS-CoV-2 occurrence in wastewater for improved data interpretation.

There is no standard approach to interoperate the multiple SARS-CoV-2 wastewater surveillance data sets generated during the pandemic. We tested several data processing approaches on wastewater surveillance data sets generated from 19 sewersheds across four major metropolitan areas in the United States from May 2020 through October 2021. First, we explored the effect of different data processing techniques on the correlation between SARS-CoV-2 wastewater RNA load and clinical case counts and found that locally weighted smoothing (LOESS) smoothing applied to multivariate imputation by chain equations (MICE)-imputed wastewater viral load led to the strongest correlations in 16 out of 19 sewersheds (84%). Next, we calculated the rate of change (RC) in wastewater viral load and in clinical cases and found that imputing missing viral load data on a 28-day window produced the strongest correlation (Spearman's ρ = 0.63). Furthermore, we determined an average sensitivity threshold of 2.4 new COVID-19 cases per day resulted in a significant RC in wastewater, but sensitivity varied with the laboratory method used. Our retrospective analysis using RC highlighted certain methodological insights, reduced site-specific impacts, and estimated a wastewater sensitivity threshold�supporting the use of relative, rather than absolute, measures of SARS-CoV-2 wastewater data for more interoperable data sets.

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For over a century, environmental engineers have attempted to control the prokaryotic community within biological processes for wastewater treatment, but there is growing interest in both understanding and harnessing the...