Investigating trends in SARS-CoV-2 RNA concentrations in wastewater

In a recent study posted on medrex sib*Preprint server, researchers characterized trends in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA levels in wastewater.

Wastewater-based epidemiology (WBE) relies on infectious disease marker levels in wastewater to assess disease incidence in a community. The 2019 coronavirus disease (COVID-19) pandemic has increased the focus on WBE. Specifically, SARS-CoV-2 RNA concentrations in wastewater precipitated solids correlate well with clinical COVID-19 cases (laboratory-confirmed cases) that occurred in the same sewage shed.

Moreover, trends in SARS-CoV-2 RNA concentrations in wastewaters precede trends in COVID-19 cases in the community. However, there is uncertainty in his interpretation and use of WBE data to aid decision making. Furthermore, efforts on how to proactively monitor trends are limited. rate of change, simple moving average, etc., it pays little attention to trend stability and statistical significance and can often be confusing/misleading.

About research

In the present study, researchers used daily measurements of SARS-CoV-2 RNA in wastewater to compare three trend analysis methods and assess their performance. From November 15, 2020 to September 15, 2022, daily sampling from wastewater facilities in the San Jose-Santa Clara area began. The authors used SARS-CoV-2 nucleocapsid (N) gene concentration data in normalized gene copies/g dry weight. By pepper mild mottle virus (PMMoV) concentration.

Three analytical indicators, percentage change (PC), Mann-Kendall (MK) trend test, and relative strength index (RSI), were used to identify trends in N or PMMoV over time. The RSI helps inform trend stability, while the other two inform about statistical significance. RSI was calculated against a 7-day right-aligned moving average of N or PMMoV using a 14-day lookback period.

For PC, the authors used the formula used by the US Centers for Disease Control and Prevention (CDC) to calculate trends in SARS-CoV-2 RNA concentrations in wastewater. A nonparametric test, the MK trend test, was used to assess monotonic trends in time series datasets. The MK trend test was applied to test raw N or PMMoV data using a 14-day lookback period.

Heatmaps are shown for the entire analysis period and for the three COVID-19 waves caused by 1) SARS-CoV-2 Delta, 2) Omicron BA.1, and 3) Omicron BA.2 and BA.5 variants. Created individually. Additionally, the authors downsampled the dataset and repeated the PC and MK trend test methods. Downsampled datasets were generated for all combinations covering frequencies from 2 samples/week to 6 samples/week.

Trends in sewage SARS-CoV-2 RNA were stratified as increasing, decreasing, or stable using three indices. In addition, MK trend test and PC calculated upper and lower sensitivity and specificity for each downsampled dataset. Up/down sensitivity was defined as correctly identifying an up/down trend, whereas specificity was the ability to not identify a trend.

findings

All methods identified increasing, decreasing, and stable trends in SARS-CoV-2 RNA concentrations in wastewater. MK trend test and PC identified an uptrend earlier than the RSI at the beginning of the delta and BA.2/BA.5 waves. In particular, the MK trend test confirmed an increasing trend at the onset of the delta and BA.2/BA.5 waves 17 days before his and 12 days before the RSI, respectively.

Similarly, PC identified an increasing trend at the onset of the delta and BA.2/BA.5 waves, 16 and 26 days before the RSI, respectively. Additionally, MK Trend Test and PC identified a downtrend at the end of the COVID-19 wave before the RSI. In downsampling analysis, both the MK trend test and PC had low sensitivity at low sampling frequencies (up/down), but improved with increasing frequency.

The MK trend test achieved acceptable upper/lower sensitivity with a minimum of 5 samples/week. PC, on the other hand, reached acceptable upper/lower sensitivities with at least 4 samples/week. Notably, the specificity remained similar and very high for both methods at all sampling frequencies.

Conclusion

MK trend test and PC used daily data more than RSI to provide early warning of uptrends and downtrends. Downsampling analysis suggested that sampling frequencies of at least 4 and 5 samples per week were required to identify trends using the PC and MK trend tests, respectively. The PC has fewer samples per week than the MK trend test, so budget-constrained WBE programs may prefer the PC.

Overall, MK trend test and PC are inference-based methods that can be used to classify trends in a standard way. These trend analysis approaches have been adopted by the WBE program to inform public health departments about how trends in COVID-19 cases may change, especially as SARS-CoV-2 laboratory testing rates decline. may occur.

*Important Notices

medRxiv publishes non-peer-reviewed, preliminary scientific reports and should not be considered conclusive, to guide clinical practice/health-related actions, or to be treated as established information .