High-Frequency, High-Throughput Quantification of SARS-CoV-2 RNA in Wastewater Settled Solids at Eight Publicly Owned Treatment Works in Northern California Shows Strong Association with COVID-19 Incidence

Abstract

A number of recent retrospective studies have demonstrated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater are associated with coronavirus disease 2019 (COVID-19) cases in the corresponding sewersheds. Implementing high-resolution, prospective efforts across multiple plants depends on sensitive measurements that are representative of COVID-19 cases, scalable for high-throughput analysis, and comparable across laboratories. We conducted a prospective study across eight publicly owned treatment works (POTWs). A focus on SARS-CoV-2 RNA in solids enabled us to scale up our measurements with a commercial lab partner. Samples were collected daily, and results were posted to a website within 24 h. SARS-CoV-2 RNA in daily samples correlated with the incidence of COVID-19 cases in the sewersheds; a 1 log₁₀ increase in SARS-CoV-2 RNA in settled solids corresponds to a 0.58 log₁₀ (4×) increase in sewershed incidence rate. SARS-CoV-2 RNA signals measured with the commercial laboratory partner were comparable across plants and comparable to measurements conducted in a university laboratory when normalized by pepper mild mottle virus (PMMoV) RNA. Results suggest that SARS-CoV-2 RNA should be detectable in settled solids for COVID-19 incidence rates of >1/100,000 (range, 0.8 to 2.3 cases per 100,000). These sensitive, representative, scalable, and comparable methods will be valuable for future efforts to scale up wastewater-based epidemiology. IMPORTANCE Access to reliable, rapid monitoring data is critical to guide response to an infectious disease outbreak. For pathogens that are shed in feces or urine, monitoring wastewater can provide a cost-effective snapshot of transmission in an entire community via a single sample. In order for a method to be useful for ongoing COVID-19 monitoring, it should be sensitive for detection of low concentrations of SARS-CoV-2, representative of incidence rates in the community, scalable to generate data quickly, and comparable across laboratories. This paper presents a method utilizing wastewater solids to meet these goals, producing measurements of SARS-CoV-2 RNA strongly associated with COVID-19 cases in the sewershed of a publicly owned treatment work. Results, provided within 24 h, can be used to detect incidence rates as low as approximately 1/100,000 cases and can be normalized for comparison across locations generating data using different methods.

Keywords: COVID-19; SARS-CoV-2; settled solids; wastewater.

FIG 1

Time series of (top to bottom) N copies per gram (cp/g) in wastewater, N/PMMoV, and laboratory-confirmed SARS-CoV-2 incidence rate (bottom) for each of eight POTWs and their sewersheds from mid-November/December 2020 to 31 March 2021. Points represent daily values. Lines are 7-day, centered smoothed averages. For wastewater, this is a trimmed average where the highest and lowest values from the seven day period are removed before averaging.

FIG 2

7-day smoothed COVID-19 incidence rate plotted against daily wastewater measurements (top row) and 7-day trimmed wastewater measurements (bottom row). From left to right, plots show the association between incidence rate and N gene copies (gc)/g, N gc/g normalized by PMMoV, and N gc/g scaled by a factor (F) that includes PMMoV, TSS, and partitioning coefficients presented in Wolfe et al. (7).

FIG 3

Data from Wolfe et al. (7) (in color) showing daily measurement of SARS-CoV-2 in wastewater overlaid on results from Fig. 2 showing daily measurements from this study. Both data sets show 7-day smoothed COVID-19 incidence rate plotted against daily wastewater measurements of N gene copies/g, N gc/g normalized by PMMoV, and N gc/g scaled by F. The two data sets are both generated from analysis of solids; however, Wolfe et al. (7) used a more labor-intensive, small scale set of methods and slightly different genomic targets versus the high-throughput methods presented in this paper.