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 first detecting increasing and decreasing trends using a 14-day look-back period, 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.