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. 2022 Nov 11;2(11):1899-1909.
doi: 10.1021/acsestwater.1c00434. Epub 2022 May 3.

Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States

Claire Duvallet  1 , Fuqing Wu  2   3 , Kyle A McElroy  1 , Maxim Imakaev  1 , Noriko Endo  1 , Amy Xiao  2   3 , Jianbo Zhang  2   3 , Róisín Floyd-O'Sullivan  1 , Morgan M Powell  1 , Samuel Mendola  1 , Shane T Wilson  1 , Francis Cruz  1 , Tamar Melman  4 , Chaithra Lakshmi Sathyanarayana  1 , Scott W Olesen  1 , Timothy B Erickson  5   6   7 , Newsha Ghaeli  1 , Peter Chai  6   8   9 , Eric J Alm  1   2   3   4 , Mariana Matus  1
Affiliations

Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States

Claire Duvallet et al. ACS ES T Water. .

Abstract

Wastewater-based epidemiology has emerged as a promising technology for population-level surveillance of COVID-19. In this study, we present results of a large nationwide SARS-CoV-2 wastewater monitoring system in the United States. We profile 55 locations with at least six months of sampling from April 2020 to May 2021. These locations represent more than 12 million individuals across 19 states. Samples were collected approximately weekly by wastewater treatment utilities as part of a regular wastewater surveillance service and analyzed for SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA concentrations were normalized to pepper mild mottle virus, an indicator of fecal matter in wastewater. We show that wastewater data reflect temporal and geographic trends in clinical COVID-19 cases and investigate the impact of normalization on correlations with case data within and across locations. We also provide key lessons learned from our broad-scale implementation of wastewater-based epidemiology, which can be used to inform wastewater-based epidemiology approaches for future emerging diseases. This work demonstrates that wastewater surveillance is a feasible approach for nationwide population-level monitoring of COVID-19 disease. With an evolving epidemic and effective vaccines against SARS-CoV-2, wastewater-based epidemiology can serve as a passive surveillance approach for detecting changing dynamics or resurgences of the virus.

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Conflict of interest statement

The authors declare the following competing financial interest(s): C.D., K.A.M., N.E., M.I., R.F.-O., M.M.P., S.M., S.T.W., F.C., T.M., C.L.S., and S.W.O. are current or former employees of Biobot Analytics, Inc. E.J.A. is scientific advisor to Biobot Analytics, Inc.

Figures

Figure 1
Figure 1
Time series of results from sampling locations with at least one monthly sample for at least six months from June 2020 through May 2021 (n = 55): blue line, centered three-sample average of normalized wastewater concentrations (genome copies per liter); blue dots, individual normalized wastewater concentration measurements (genome copies per liter); orange line, centered seven-day average of daily new cases in the respective county (new cases). Y-Axes are normalized to the maximum of each time series, and lines are shown without units to emphasize relative trends within each location. X-Axes are consistent across plots, with monthly ticks ranging from April 1, 2020, to May 1, 2021. Plots are labeled with county and state names of the associated catchment. Sampling locations are organized in order of decreasing catchment population; i.e., the largest catchment (1 950 000 people) is at the top left (A1), and the smallest catchment (6400 people) is the bottom right most plot (K5). Rows are labeled with letters and columns with numbers for ease of reference. Individual time series for each location, including daily new cases and detailed units for both axes, are provided in the Abstract Graphic.
Figure 2
Figure 2
Monthly geographic trends in wastewater SARS-CoV-2 RNA concentrations and COVID-19 cases. Blue data (first and third columns from the left): Monthly state-level normalized wastewater SARS-CoV-2 RNA concentrations. The monthly state average is calculated as the average normalized concentration per sampling location per month and then averaged across sampling locations within the same state. Orange data (second and fourth columns from the left): monthly state-level new COVID-19 cases per 100 000 people for counties with at least one wastewater sampling location in that month. The monthly state average is calculated as the monthly average of new daily cases per 100 000 people in counties with a corresponding sampling location and then averaged across counties within the same state. States that did not include any sampling locations are outlined in gray. Color scales apply to all maps and are truncated at the 95th percentile of each data set.
Figure 3
Figure 3
Geographic correlations per month. Scatter plots showing normalized concentrations (X-axis) vs new cases per 100 000 (Y-axis), averaged within each state (points) per month (subplots). Spearman correlations were calculated using scipy.spearmanr; correlations and uncorrected p values are provided on each plot.
Figure 4
Figure 4
Impact of PMMoV normalization on correlations between wastewater SARS-CoV-2 RNA concentrations and COVID-19 cases, within and across locations. The top row shows temporal correlations between wastewater concentrations and new reported COVID-19 cases within locations. (A) A box plot shows the Spearman correlation between wastewater SARS-CoV-2 RNA concentrations (copies per liter) and new reported clinical cases (total cases). Each point is the correlation for one location. Correlations were calculated using three different measures of wastewater concentration: raw, unadjusted SARS-CoV-2 concentrations, SARS-CoV-2 concentrations normalized to a fecal maker (PMMoV), and a three-sample average of the normalized concentrations. (B) Difference in the Spearman correlation when calculated using normalized or raw concentrations. Each point is a sampling location; a positive delta indicates that the Spearman correlation calculated using normalized concentrations is higher than the correlation calculated using raw concentrations. The bottom row shows the geographic correlations with normalized vs raw concentrations. (C) Each point is a month. The Y-axis shows the correlation between states during that month. The X-axis indicates which concentration measure was used to calculate the correlation. (D) Difference in the Spearman correlation when calculated using normalized or raw concentrations. Each point is a month; a positive delta indicates that the Spearman correlation across states calculated using normalized concentrations is higher than the correlation calculated using raw concentrations.
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