Deploying SARS-CoV-2 wastewater monitoring in Kansas: A case study and adoption of high-throughput, automated sample processing

The COVID-19 pandemic has highlighted the potential role that wastewater-based epidemiology (WBE) can play in assessing aggregate community health. However, efforts to translate SARS-CoV-2 gene copy numbers obtained from wastewater samples into meaningful community health indicators are nascent. This work will highlight two efforts to promote the use of WBE: 1) comparison of SARS-CoV-2 wastewater measurements during a low-frequency and a high-frequency clinical testing period and 2) adoption of automated, high-throughput methods to enable state-wide monitoring in Kansas. These efforts measured the SARS-CoV-2 nucleocapsid (N) genes (N1 and N2) weekly using reverse transcriptase droplet digital PCR. To compare measurements with clinical results, two municipal wastewater treatment plants were monitored for six months. Four biomarkers (human mitochondrial gene NADH dehydrogenase subunit 5 (mit5), creatinine, ammonia, and biological oxygen demand (BOD)) were quantified and used to normalize Sars-CoV-2 gene copy numbers to account for variations in sewershed conditions. The normalized values were correlated to daily new case data and one-, two-, and three-week cumulative case data. Mitochondrial and creatinine normalization methods showed the strongest correlations throughout the studying indicating that human-specific biomarkers were better at normalizing wastewater data than ammonia or BOD. During low-frequency clinical testing periods, results were strongly correlated with a six-day case data lag (ρ = 0.83), while the high-frequency clinical testing period yielded correlations of 0.81 with a one-day case data lag. To expand these efforts to a state-wide monitoring effort, a high-throughput, automated workflow was compared to the original PEG-precipitation method. The transition to a new methodology has allowed for monitoring in 95 out of 105 Kansas counties.

 

Learning Objectives