Data Analysis and Modeling for Transitioning Between Laboratory Methods for Detecting SARS-CoV-2 in Wastewater

Wastewater surveillance has proven to be a useful tool to monitor pathogens such as SARS-CoV-2 as it is a nonintrusive way to survey the potential disease burden of the population contributing to a sewershed. With the expansion of this field since the beginning of the COVID-19 pandemic, laboratory methods to process wastewater and quantify pathogen nucleic acid levels have improved as technologies changed, efforts expanded in size and scope, and supply chain issues were resolved. Maintaining data continuity is crucial for labs undergoing method transitions to accurately assess infectious disease levels over time and compare measured RNA concentrations to public health data. Despite the dynamic nature of laboratory methods and the necessity to ensure uninterrupted data, to our knowledge there has not been a study that unites two datasets from different lab methods for pathogen quantification from environmental samples. Here, we describe a lab transition from SARS-CoV-2 RNA quantification using a low-throughput, manual filtration-based wastewater concentration and RNA extraction followed by qPCR to a high-throughput, automated magnetic bead-based concentration and extraction followed by dPCR. During the two-month transition period, wastewater samples from across the Chicago metropolitan area were processed with both methods in parallel. We evaluated a variety of regression models to relate the RNA measurements from both methods and found a log-log model was most appropriate after removing outliers and discrepancy points to improve model performance. We also evaluated the consequences of assigning values to samples that were below the detection limit. Our study demonstrates that data continuity can be maintained throughout a transition of laboratory methods if there is a sufficient period of overlap between the methods for an appropriate model to be constructed to relate the datasets.