Tracking the temporal variation of COVID-19 surges through wastewater-based epidemiology during the peak of the pandemic: A six-month long study in Charlotte, North Carolina

Abstract

The global spread of SARS-CoV-2 has continued to be a serious concern after WHO declared the virus to be the causative agent of the coronavirus disease 2019 (COVID-19) a global pandemic. Monitoring of wastewater is a useful tool for assessing community prevalence given that fecal shedding of SARS-CoV-2 occurs in high concentrations by infected individuals, regardless of whether they are asymptomatic or symptomatic. Using tools that are part of wastewater-based epidemiology (WBE) approach, combined with molecular analyses, wastewater monitoring becomes a key piece of information used to assess trends and quantify the scale and dynamics of COVID-19 infection in a specific community, municipality, or area of service. This study investigates a six-month long SARS-CoV-2 RNA quantification in influent wastewater from four municipal wastewater treatment plants (WWTP) serving the Charlotte region of North Carolina (NC) using both RT-qPCR and RT-ddPCR platforms. Influent wastewater was analyzed for the nucleocapsid (N) genes N1 and N2. Both RT-qPCR and RT-ddPCR performed well for detection and quantification of SARS-CoV-2 using the N1 target, while for the N2 target RT-ddPCR was more sensitive. SARS-CoV-2 concentration ranged from 10³ to 10⁵ copies/L for all four plants. Both RT-qPCR and RT-ddPCR showed a significant positive correlation between SARS-CoV-2 concentrations and the 7-day rolling average of clinically reported COVID-19 cases when lagging 5 to 12 days (ρ = 0.52-0.92, p < 0.001-0.02). A major finding of this study is that RT-qPCR and RT-ddPCR generated SARS-CoV-2 data that was positively correlated (ρ = 0.569, p < 0.0001) and can be successfully used to monitor SARS-CoV-2 signals across the WWTP of different sizes and metropolitan service functions without significant anomalies.

Keywords: COVID-19; RT-ddPCR; RT-qPCR; SARS-CoV-2; Wastewater; Wastewater-based epidemiology (WBE).

Graphical abstract

Fig. 1

Map showing the total number of clinically reported COVID-19 cases, county-wise, in the state of NC for the duration of this study (Prepared by the software ArcGIS Pro).

Fig. 2

Showing the two different workflows performed to quantify SARS-CoV-2 in the influent wastewater.

Fig. 3

Map showing the four sewershed location and the overlapping zip codes of Charlotte, NC.

Fig. 4

Heat map of concentrations of (a) N1 and (b) N2 targets to evaluate SARS-CoV-2 prevalence at WWTP A, B, C and D using RT-qPCR and RT-ddPCR. The symbol “×” indicates a missed sampling event and the uncolored blank spaces indicate a sample that was below the limit of detection (LoD).

Fig. 5

SARS-CoV-2 concentration (N1 target) for workflow 1 and 2 quantified by RT-qPCR and RT-ddPCR in the influent wastewater of (a) WWTP A, (b) WWTP B, and (c) WWTP C plotted against the 7-day average cases of each zipcode served by each WWTP. Quadratic polynomial trendline was used for the best fitted curve.

Fig. 6

Linear regressions with reported correlative relationships between the log-transformed SARS-CoV-2 concentration and the 7-day moving average of COVID-19 case counts reported by the NC Department of Health and Human Services for Mecklenburg County. The viral concentration in wastewater was related to either same day reported case counts or lagged case counts.