Improving wastewater-based epidemiology performance through streamlined automation

Wastewater-based epidemiology (WBE) has enabled us to describe Severe Acute Respiratory Syndrome Coro-navirus 2 (SARS-CoV-2) infections in populations. However, implementation of wastewater monitoring of SARS-CoV-2 is limited due to the need for expert staff, expensive equipment, and prolonged processing times. As WBE increases in scope (beyond SARS-CoV-2) and scale (beyond developed regions), there is a need to make WBE processes simpler, cheaper, and faster. We developed an automated workflow based on a simplified method termed exclusion-based sample preparation (ESP). Our automated workflow takes 40 min from raw wastewater to purified RNA, which is several times faster than conventional WBE methods. The total assay cost per sample/ replicate is $6.50 which includes consumables and reagents for concentration, extraction, and RT-qPCR quan-tification. The assay complexity is reduced significantly, as extraction and concentration steps are integrated and automated. The high recovery efficiency of the automated assay (84.5 +/- 25.4%) yielded an improved Limit of Detection (LoDAutomated=40 copies/mL) compared to the manual process (LoDManual=206 copies/mL), increasing analytical sensitivity. We validated the performance of the automated workflow by comparing it with the manual method using wastewater samples from several locations. The results from the two methods correlated strongly (r = 0.953), while the automated method was shown to be more precise. In 83% of the samples, the automated method showed lower variation between replicates, which is likely due to higher technical errors in the manual process e.g., pipetting. Our automated wastewater workflow can support the expansion of WBE in the fight against Coronavirus Disease of 2019 (COVID-19) and other epidemics.

Automated summary

Wastewater-based epidemiology (WBE) has enabled the description of SARS-CoV-2 infections in populations, but its implementation is limited due to the need for expert staff, expensive equipment, and prolonged processing times. To overcome these limitations, an automated workflow based on a simplified method was developed, which is faster and cheaper than conventional WBE methods.