Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater

Abstract

Wastewater surveillance has proven a key public health tool to understand a wide range of community health diseases and has proven to be especially critical to health departments throughout the SARS CoV-2 pandemic. The size of the population served by a wastewater treatment plant (WWTP) may limit the targeted insight about community disease dynamics. To investigate this concern, samples of wastewater were obtained at lift stations upstream of WWTPs within the sewer network. First, an online, semi-automatic time series model is fitted to the weekly measurements of WWTP samples to estimate the viral trend for the community and compared to the time series observations from the lift stations. Second, deviations from the WWTP trend are identified using an Exponentially Weighted Moving Average (EWMA) control chart. The analysis reveals that the lift stations display slightly different dynamics than the larger WWTP, highlighting the more granular insight gleaned from sampling sites which represent smaller populations. Discussion focuses on the use of our methods to support rapid public health decision-making based on additional, targeted samples in times of concern.