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. 2021 Aug 1:12:100111.
doi: 10.1016/j.wroa.2021.100111. Epub 2021 Jul 31.

Tools for interpretation of wastewater SARS-CoV-2 temporal and spatial trends demonstrated with data collected in the San Francisco Bay Area

Hannah D Greenwald  1   2 , Lauren C Kennedy  1   2 , Adrian Hinkle  1   2 , Oscar N Whitney  3 , Vinson B Fan  3 , Alexander Crits-Christoph  4   5 , Sasha Harris-Lovett  2 , Avi I Flamholz  3   6 , Basem Al-Shayeb  4   5 , Lauren D Liao  7 , Matt Beyers  8 , Daniel Brown  9 , Alicia R Chakrabarti  10 , Jason Dow  11 , Dan Frost  12 , Mark Koekemoer  11 , Chris Lynch  9 , Payal Sarkar  13 , Eileen White  10 , Rose Kantor  1   2 , Kara L Nelson  1   2   5
Affiliations

Tools for interpretation of wastewater SARS-CoV-2 temporal and spatial trends demonstrated with data collected in the San Francisco Bay Area

Hannah D Greenwald et al. Water Res X. .

Abstract

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be integrated with COVID-19 case data to inform timely pandemic response. However, more research is needed to apply and develop systematic methods to interpret the true SARS-CoV-2 signal from noise introduced in wastewater samples (e.g., from sewer conditions, sampling and extraction methods, etc.). In this study, raw wastewater was collected weekly from five sewersheds and one residential facility. The concentrations of SARS-CoV-2 in wastewater samples were compared to geocoded COVID-19 clinical testing data. SARS-CoV-2 was reliably detected (95% positivity) in frozen wastewater samples when reported daily new COVID-19 cases were 2.4 or more per 100,000 people. To adjust for variation in sample fecal content, four normalization biomarkers were evaluated: crAssphage, pepper mild mottle virus, Bacteroides ribosomal RNA (rRNA), and human 18S rRNA. Of these, crAssphage displayed the least spatial and temporal variability. Both unnormalized SARS-CoV-2 RNA signal and signal normalized to crAssphage had positive and significant correlation with clinical testing data (Kendall's Tau-b (τ)=0.43 and 0.38, respectively), but no normalization biomarker strengthened the correlation with clinical testing data. Locational dependencies and the date associated with testing data impacted the lead time of wastewater for clinical trends, and no lead time was observed when the sample collection date (versus the result date) was used for both wastewater and clinical testing data. This study supports that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence and presents tools that could be applied to make wastewater signal more interpretable and comparable across studies.

Keywords: Bacteroides; COVID-19; CrAssphage; Human 18S rRNA; Pepper mild mottle virus; Wastewater-based epidemiology.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image, graphical abstract
Graphical abstract
Fig. 1
Fig. 1
Map of the six wastewater catchment areas sampled in this study. Samples collected from East Bay Municipal Utility District represent three discrete sampling areas: one including North Berkeley (location N), one including the University of California at Berkeley (location A), and one including Oakland (location S). Location K consists of the full Central Contra Costa Sanitary District sewershed. Location E consists of the full San Jose - Santa Clara Regional Wastewater Facility sewershed. Location Q is from San Quentin Prison, which is in the Central Marin Sanitation Agency sewershed. All locations collect predominantly residential wastewater.
Fig. 2
Fig. 2
Spatial and temporal variation in crAssphage, PMMoV, and Bacteroides wastewater loads. Only one extraction replicate per date per location is shown. 18S rRNA results were not included in the figure for consistency of scale due to the wide range in sample values and are included in the SI (Fig. S8). The temporal variation within each location was assessed as the geometric coefficient of variation, displayed as a percentage above each box. The significance of differences between locations was assessed using a Kruskal-Wallis test with a Bonferroni correction followed by Dunn's test, where * indicates p < 0.001 for bracketed relationships and § (above location E) indicates p < 0.001 for every pairwise location comparison to E, except p > 0.001 when compared to location K (for crAssphage and Bacteroides) and location N (for crAssphage).
Fig. 3
Fig. 3
Rank correlations of both unnormalized and normalized wastewater SARS-CoV-2 concentrations with clinical testing data. N1 concentration, and N1 normalized proposed biomarkers are plotted against a seven-day moving average of new cases per capita per day for sample locations K, S, N, A, and Q. Shapes signify whether wastewater samples were below the qPCR limit of detection (LoD) for the N1 assay, associated with masked clinical case values, or both. Significance of rank correlation values in facet titles is indicated by *=<0.05, ***=<0.0001
Fig. 4
Fig. 4
Example of Lowess bandwidth parameter selection process (Location N) (A) Residual plots for Lowess bandwidth parameter (α; column labels) determination for location N where the bandwidth parameter increases from inclusion of 1 data point (far left) to inclusion of all data points (far right) in each local regression for unnormalized N1 (top) and crAssphage-normalized N1 (bottom). The value of α that minimized the residual was selected (red boxes). (B) Visualization of how bandwidth parameter affected the Lowess trendline for location N. Black dashed line indicates the resulting Lowess trendline when α=0.39.
Fig. 5
Fig. 5
Comparison of wastewater SARS-CoV-2 N1 to geocoded COVID-19 clinical testing results from May to September 2020. Wastewater SARS-CoV-2 N1 signal is compared as unnormalized (top) and crAssphage-normalized (middle), where lines are the most optimal Lowess trendlines. COVID-19 clinical testing results are the daily per capita COVID-19 cases, where lines are the fourteen-day moving average (location N) or seven-day moving averages (all other locations) (bottom). Heatmap visualization of the unnormalized N1 trendlines is included in the SI (Figs. S6 and S7) and visualization of sewersheds by location can be found in Fig. S12.
Fig. 6
Fig. 6
Comparison of wastewater and clinical data at location Q from June to September 2020, where symbols indicate how many RT-qPCR replicates amplified. Wastewater data: (top) unnormalized and (middle) crAssphage-normalized SARS-CoV-2 N1 signal in wastewater, where the horizontal dashed line indicates the limit of detection, and trendlines are the most optimal Lowess trendline (Fig. S4). Clinical data (bottom): daily per capita COVID-19 cases, where the horizontal dashed line indicates 1 case in 1000 people. Vertical dashed lines indicate August 26th, the only date after August 12th when a new COVID-19 case was detected at location Q through clinical surveillance.
Fig. 7
Fig. 7
Estimated minimum number of COVID-19 clinical cases needed for reliable detection of SARS-CoV-2 RNA in wastewater. The cumulative percentage of amplified wastewater RT-qPCR replicates was calculated by ranking the moving averages of daily per capita cases (x-axis) from highest to lowest and calculating the fraction of RT-qPCR replicates that amplified cumulatively (y-axis) for each value of x. The dashed line represents the daily new cases per capita value above which 95% of wastewater RT-qPCR replicates amplified (2.4 cases in 100,000 people).
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