Moore swab performs equal to composite and outperforms grab sampling for SARS-CoV-2 monitoring in wastewater

Abstract

Wastewater-based epidemiology (WBE) approaches to detect SARS-CoV-2 in municipal wastewater can provide unique information on the incidence or prevalence of COVID-19 in community. However, there are several technical challenges coupled with sewage sampling for SARS-CoV-2, including intermittent shedding of viruses, sampling time, volume, and frequency. Sampling schemes thus may need to be tailored to reach out highly sensitive, accurate, and reliable results. Herein, we compared the accuracy and threshold cycle (Ct) profiles of SARS-CoV-2 in Moore swabs, composite (16-h), and grab samples taken from sewage manholes (n = 17) at the Middle Eastern city of Tehran, Iran, on two occasions (November 2020 and May 2021). Samples were concentrated by polyethylene glycol precipitation and the corresponding Ct values for CDC ‘N’ and ‘ORF1ab’ assays were derived by means of real time RT-qPCR. Overall, the Moore swabs performed equal to samples composited over 16 h for qualitative monitoring, and 34/34 (100%) were positive for SARS-CoV-2. The ‘N’ assay showed the highest detection frequency as compared to ‘ORF1ab’. The mean Moore swab Ct profiles were more consistent with 16 h composite sampling as compared with corresponding grab samples, providing hints as to the best sampling protocol to adopt when planning a sewage monitoring campaign particularly under WBE. Furthermore, our analyses on local differences showed somewhat higher virus copy numbers in the southern areas. The experimental design of this study revealed that the Moore swab and composite samples are more sensitive than grab samples, suggesting that the collection of grab samples may be inappropriate for characterizing total number of viral RNA copies in sewage samples. Given the transiently presence of human host-restricted infections such as SARS-CoV-2 and the simplicity and affordability of Moore swab, the method is well suited for disease surveillance in resource poor regions struggling with limited capacity for clinical testing.