Fundamentals of SARS-CoV-2 Detection in Wastewater for Early Epidemic Prediction and Key Learnings on Treatment Processes for Removal of Viral Fragments

Arora, Sudipti; Sutaria, Devanshi; Meena, Ekta; Nag, Aditi

doi:10.1007/978-981-16-9001-3_30

Cited by 1 publication

(2 citation statements)

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“…COVID-19 absenteeism has predominantly been explored based on its impact on the healthcare workforce. Studies have invariably demonstrated a marked increase in absenteeism rates relative to pre-pandemic periods (de Paiva, Dos Santos, and de Lima Dalmolin 2022; Grandi, Silva, and Barbosa 2022; Challener et al 2021). Indeed, these have been significant contributing factors manifesting in record-high rates of fatigue, burnout and workers prematurely leaving the workforce relative to pre-pandemic times (Maunder et al 2021; Murthy 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Wastewater-based surveillance (WBS) has emerged as a useful strategy enabling real-time monitoring of the incidence and prevalence of several infectious diseases at the population level (Arora et al 2022; Asghar et al 2014; Hellmér et al 2014). The strength of WBS relates to its ability to provide longitudinal objective and inclusive information on the burden of disease in monitored populations that is independent of clinical testing and other healthcare system resources.…”

Section: Introductionmentioning

confidence: 99%

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Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism

Acosta¹,

Dai

Bautista

et al. 2023

Preprint

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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.

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