Association of SARS-CoV-2 presence in sewage with public adherence to precautionary measures and reported COVID-19 prevalence in Tehran.

Compared to the growing body of literature on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection and quantification in sewage, there are limited studies reporting on correlations between the viral loads in sewage and the prevalence of infected patients. The present work is a part of the regular monitoring effort for SARS-CoV-2 in wastewater influents from seven wastewater treatment plants (WWTPs) in Tehran, Iran, starting from late September 2020 until early April 2021. These facilities cover ~64% of the metropolis serving >5000,000 M individuals. The study set out to track the trends in the prevalence of COVID-19 in the community using wastewater based epidemiology (WBE) and to investigate whether these measurements correlate with officially reported infections in the population. Composite sewage samples collected over 16 h were enriched by polyethylene glycol precipitation and the corresponding threshold cycle (Ct) profiles for CDC 'N' and 'ORF1ab' assays were derived through real time RT-qPCR. Monte Carlo simulation model was employed to provide estimates of the disease prevalence in the study area. RNA from SARS-CoV-2 was detectable in 100% ('N' assay) and 81% ('ORF1ab' assay) of totally 91 sewage samples, with viral loads ranging from 40 to 45,000 gene copies/L. The outbreak of COVID-19 positively correlated (R2 = 0.80) with the measured viral load in sewage samples. Furthermore, sewage SARS-CoV-2 RNA loads preceded infections in the population by 1 to 2 days, which were in line with public adherence with and support for government instructions to contain the pandemic. Given the transient presence of human host-restricted infections such as SARS-CoV-2, these results provide evidence for assessment of the effectiveness of coordinated efforts that specifically address public health responses based on wastewater-based disease surveillance against not only COVID-19 but also for future infectious outbreaks.

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

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.