ABSTRACT
Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this technology has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021-April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p[≤]0.01). Real-time contact tracing data was used to confirm an association between wastewater SARS-CoV-2 burden and clinically confirmed cases proving the potential of WBS as a tool for disease monitoring across worksites. Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.
Subject(s)
COVID-19ABSTRACT
Wastewater-based surveillance (WBS) is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19 impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with local workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.3 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5% (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4,524 unrelated absences COVID-19 cases were recorded. Employee absences significantly increased as wastewater signal increased through pandemic waves. Strong correlations between COVID-19-confirmed absences and wastewater SARS-CoV-2 signals (N1 gene: r=0.824, p<0.0001 and N2 gene: r=0.826, p<0.0001) were observed. Linear regression with adjusted R2-value demonstrated a robust association (adjusted R2=0.783), when adjusted by 7 days, indicating wastewater provides a one-week leading signal. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P<0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.