The power of wastewater surveillance & direct extraction:
Detecting COVID-19 trends 7 days sooner
The results of a wastewater surveillance study demonstrate the capability to detect trends in community COVID-19 cases 7 days before cases are reported.
By comparing SARS-CoV-2 concentration in wastewater using the direct extraction method to reported clinical cases across the same time period, the results demonstrate the capability of wastewater surveillance to detect trends in community COVID-19 cases 7 days prior to cases being reported.
Learn more below, or contact us for details about how LuminUltra’s game-changing wastewater testing solution can be applied in your system.
Wastewater surveillance has become a useful tool for monitoring the trend of COVID-19 cases at a community level. Wastewater testing for SARS-CoV-2, the virus that causes COVID-19, is a non-invasive method that delivers accurate results that have been shown to give advanced warning of clinical case trends. Conventional methods typically require specialized equipment and skilled operators to pre-concentrate large volumes of wastewater prior to analysis for SARS-CoV-2. LuminUltra has developed the GeneCount® SARS-CoV-2 Wastewater Test Kit for extracting SARS-CoV-2 RNA directly from a 1 mL sample of raw wastewater using magnetic binding bead technology thereby removing the need for pre-concentrating samples. Furthermore, it allows for simultaneous testing of both the liquid and solids fraction of the wastewater increasing the accuracy of the result.
A side-by-side comparison study was performed using the GeneCount® SARS-CoV-2 Wastewater Test Kit alongside electronegative membrane RNA concentrating, which is a commonly used lab method. Each method was performed on split wastewater samples and the RNA was subsequently tested using the same RT-qPCR assay to target the SARS-CoV-2 genome. Multiple samples were tested with wastewater sample sets from six different community wastewater treatment facilities collected over a period of several weeks resulting in a total sample set of 31 samples.
Comparing Method Results
RNA samples prepared by the direct extraction and electronegative membrane concentration methods were tested using the same RT-qPCR assay for a side-by-side comparison. The RT-qPCR assay used in this study targets the N1 and N2 genes of the SARS-CoV-2 virus.
The Pearson correlation coefficient was used to determine if there was a significant difference between the N1 or N2 target within the results for each method. The statistical analysis showed no significant difference (p-value < 0.01) between the measured concentrations (copies/mL) of N1 or N2 targets for the individual methods. The relationship between the N1 and N2 targets for each method is shown in Figure 1.
For a method-to-method comparison, the Pearson correlation coefficient was used to compare the N1 and N2 target concentrations (copies/mL) for both methods. The statistical analysis showed the N2 gene target results were not statistically different between the direct method and the electronegative membrane concentration method (p-value < 0.01). However, the correlation determined the N1 target results were statistically significant between the two methods (p-value = 0.15). Further investigation is required to understand the differences in the N1 target. Although tested with another RT-qPCR assay for the purposes of this study, the LuminUltra GeneCount® SARS-CoV-2 Wastewater Test Kit is designed to test for the N2 gene target in RT-qPCR analysis. The relationship between the methods and the individual N1 and N2 target concentrations are shown in Figure 2.
Correlating Direct Extraction Data with Reported Clinical Cases
Literature reports that wastewater-based epidemiology can give up to several days lead time on clinical case trends within a population; therefore, the direct extraction data (copies/mL) was compared side-by-side to publicly available clinical case data (reported cases/week) from one of the testing locations. Clinical COVID-19 case data was not available for other testing locations therefore correlations could not be analyzed.
Reported clinical data (weekly totals) and measured wastewater data (random intervals) were not conducted on identical time intervals, therefore a LOESS regression model was first used to interpolate between data points. The Pearson correlation coefficient (r) was determined between the two models. Potential lagging relationships were identified by shifting the models by 1 day up to a maximum of 10 days. At each interval, the Pearson correlation coefficient was calculated, and the maximum absolute coefficient of all possible intervals was determined as the optimal relationship. The LOESS regression models developed from the clinical and wastewater unpaired data points are shown in Figure 3.
The optimal relationship between the two models was calculated at a lag relationship of 7 days (r = 0.6) showing wastewater data detected similar trends to COVID-19 cases 7 days prior to cases being reported. Figure 4 shows the LOESS models for the clinical and wastewater data as well as the adjusted model to represent the lag time where the initial time point for clinical data is shifted back 7 days. The adjusted clinical model displays a strong relationship with the wastewater model.
The Pearson correlation coefficient (r = 0.6) and corresponding lag relationship (7 days) demonstrates the capability of wastewater surveillance to detect trends in community COVID-19 cases 7 days prior to clinical cases, as reported in literature. The lead time on clinical case trends from wastewater surveillance can be a valuable tool for pandemic response allowing for informed public health decisions such as increased clinical testing or adjustments to social restrictions.
Wastewater samples were analyzed for SARS-CoV-2 using two methods: the GeneCount® SARS-CoV-2 Wastewater Test Kit and electronegative membrane filtration. The Pearson correlation coefficient was used to compare the results of both preparation methods as well to compare the direct extraction method results to publicly available clinical data from one of the sampling sites. In summary, the results showed:
- When using the Pearson correlation coefficient to compare the N1 and N2 targets of the individual tests, there was no significant difference between the targets for either test (p-value < 0.01).
- When using the Pearson correlation coefficient to compare the sample preparation methods, there was no significant difference between the results for the N2 target (p-value < 0.01) however, a significant difference was seen between the results for the N1 target (p-value = 0.15). Further investigation is required to understand the differences.
- The Pearson correlation coefficient was used to compare the LOESS regression models generated to pair wastewater and clinical data sets. The optimal correlation was calculated by maximizing the Pearson correlation coefficient (r) by shifting the data by one day up to 10 days. The optimal correlation was calculated at a lag relationship of 7 days (r = 0.6).
- By comparing SARS-CoV-2 concentration in wastewater using the direct extraction method to reported clinical cases across the same time period, the results demonstrate the capability of wastewater surveillance to detect trends in community COVID-19 cases 7 days prior to cases being reported.