Identifying trends in SARS-CoV-2 RNA in wastewater to infer changing COVID-19 incidence: Effect of sampling frequency

Abstract

SARS-CoV-2 RNA concentrations in wastewater solids and liquids are correlated with reported incident COVID-19 cases. Reporting of incident COVID-19 cases has changed dramatically with the availability of at-home antigen tests. Wastewater monitoring therefore represents an objective tool for continued monitoring of COVID-19 occurrence. One important use case for wastewater data is identifying when there are sustained changes or trends in SARS-CoV-2 RNA concentrations. Such information can be used to inform public health messaging, testing, and vaccine resources. However, there is limited research on best approaches for identifying trends in wastewater monitoring data. To fill this knowledge gap, we applied three trend analysis methods (relative strength index (RSI), percent change (PC), Mann-Kendall (MK) trend test) to daily measurements of SARS-CoV-2 RNA in wastewater solids from a wastewater treatment plant to characterize trends. Because daily measurements are not common for wastewater monitoring programs, we also conducted a downsampling analysis to determine the minimum sampling frequency necessary to capture the trends identified using the gold standard daily data. The PC and MK trend test appear to perform similarly and better than the RSI in terms of early warning signaling for increasing and decreasing trends, so we only considered the PC and MK trend test methods in the downsampling analysis. Using an acceptable sensitivity and specificity cutoff of 0.5, we found that a minimum of 4 samples/week and 5 samples/week is necessary to detect trends identified by daily sampling using the PC and MK trend test method, respectively. If a higher sensitivity and specificity is needed, then more samples per week would be needed. Public health officials can adopt these trend analysis approaches and sampling frequency recommendations to wastewater monitoring programs aimed at providing information on how incident COVID-19 cases are changing in the contributing communities.

Identifying trends in SARS-CoV-2 RNA in wastewater to infer changing COVID-19 incidence: Effect of sampling frequency

“…All calculations were conducted in R (version 4.1.3) [48]. Data and R code are available publicly through the Stanford Digital Repository (https://doi.org/10.25740/ yg713sw8276) [42].…”

“…The remaining data (April 1, 2021-December 31, 2021, and April 13, 2022-September 15, 2022) have not been previously published. All data used in this study are available publicly through the Stanford Digital Repository (https://doi.org/10.25740/yg713sw8276) [42]. Since the preanalytical and analytical methods used for measuring the SARS-CoV-2 RNA N gene and PMMoV are registered and previously described in peer-reviewed publications, they are not repeated herein.…”

Public health policy impact evaluation: A potential use case for longitudinal monitoring of viruses in wastewater at small geographic scales

“…(https://doi.org/10.25740/ch598gf0783) [58]. Weekly average wastewater SARS-CoV-2 RNA concentrations were positively and significantly correlated with weekly average incident COVID-19 cases during the academic year using both unnormalized and normalized N gene concentrations; this correlation was not statistically significant during the nonacademic portion of the year (Table 2).…”

“…Data from CR2C are novel and not published elsewhere. All wastewater monitoring data used in this study are publicly available through the Stanford Digital Repository (https://doi.org/10.25740/ch598gf0783) [58].…”

Piloting wastewater-based surveillance of norovirus in England

“…The log10 mean averages were calculated for GI, GII, total NoV and ΔG from all sites for each of the national and regional two-weekly aggregates. Upward and downward trends in NoV levels were calculated using a relative strength index (RSI) with the TTR package (Ulrich, 2021) for R (R Core Team, 2023) according to Chan et al (2023), using a rolling 6week look-back period. Each RSI value was assigned a descriptive trend category according to Table 2.…”