Relationship between SARS-CoV-2 in wastewater and clinical data from five wastewater sheds

Wartell, Brian A.; Ballare, Sudheer; Ghandehari, Shahrzad Saffari; Arcellana, Patricia Dotingco; Proano, Camila; Kaya, Devrim; Niemeier, Debra; Kjellerup, Birthe V.

doi:10.1016/j.hazadv.2022.100159

Cited by 7 publications

(6 citation statements)

References 62 publications

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“…SARS-CoV-2 RNA concentration in wastewater exhibited good relationship with reported active and hospitalised COVID-19 cases and showed good agreement with previous studies ( Acosta et al, 2022 , 2023 ; Mohapatra et al, 2023 ; Wartell et al, 2022 ). However, there was variability in the strength of the correlation between SARS-CoV-2 RNA concentrations and clinical data throughout the four waves.…”

Section: Discussionsupporting

confidence: 88%

SARS-CoV-2 wastewater surveillance in the Czech Republic: Spatial and temporal differences in SARS-CoV-2 RNA concentrations and relationship to clinical data and wastewater parameters

Sovová,

Vašíčková,

Valášek

et al. 2024

Water Research X

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Section: Discussionsupporting

confidence: 88%

SARS-CoV-2 wastewater surveillance in the Czech Republic: Spatial and temporal differences in SARS-CoV-2 RNA concentrations and relationship to clinical data and wastewater parameters

Sovová,

Vašíčková,

Valášek

et al. 2024

Water Research X

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“…Here, the estimation of the number of absent workers due to COVID-19 infections was performed via a Poisson regression model. Other studies also have shown the trends and strong correlation of clinical data with the SARS-CoV-2 RNA abundance in wastewater on a weekly basis (Wartell et al 2022; Fahrenfeld et al 2022; Halwatura et al 2022; Krivoňáková et al 2021).…”

Section: Discussionmentioning

confidence: 93%

“…To date, WBS studies have primarily focused on understanding the temporal changes in COVID-19 in communities by measuring SARS-CoV-2 RNA concentration in wastewater derived from WWTP (Pang et al 2022; Acosta et al 2022; Ahmed et al 2020; de Sousa et al 2022; Hasan et al 2021; Karthikeyan et al 2021; Wartell et al 2022; Tandukar et al 2022; Acosta et al 2021; Gonçalves et al 2021; Wang et al 2020; Zhou et al 2021; Gibas et al 2021; Betancourt et al 2021; Dai et al 2022). These data have enabled evidence-informed decisions by public health officials as well as members of the public.…”

Section: Discussionmentioning

confidence: 99%

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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“…This rapid and cost-effective approach offers valuable insights into the presence and prevalence of viruses, bacteria associated with antimicrobial resistance (AMR), and their AMR genes at a community level [2,5,7,8]. Studies have shown a high correlation between wastewater data and clinical data and therefore WWS has been proposed as a valuable and independent approach complementing traditional clinical epidemiology [9][10][11][12]. In addition of monitoring ongoing circulation of pathogens within a population, there are some evidence indicating the potential of WWS to anticipate future outbreaks [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

EU surveys insights: analytical tools, future directions, and the essential requirement for reference materials in wastewater monitoring of SARS-CoV-2, antimicrobial resistance and beyond

Paracchini,

Petrillo,

Rajashekar

et al. 2024

Preprint

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Background Wastewater surveillance (WWS) acts as a vigilant sentinel system for communities, analysing sewage to protect public health by detecting outbreaks and monitoring trends in pathogens and contaminants. To achieve a thorough comprehension of present and upcoming practices in WWS, two EU surveys were conducted targeting WWS laboratories across Europe and other regions. The first survey explored a diverse range of activities currently undertaken or planned by laboratories. The second survey specifically targeted methods and quality controls utilised for SARS-CoV-2 surveillance. Results The findings of the two surveys provide a comprehensive insight into the procedures and methodologies applied in WWS. In Europe, WWS primarily focuses on SARS-CoV-2 with 99% of the survey participants dedicated to this virus. However, the responses highlighted a lack of standardisation in the methodologies employed for monitoring SARS-CoV-2. The surveillance of other pathogens, including antimicrobial resistance, is currently fragmented and conducted by only a limited number of laboratories. Notably, these activities are anticipated to expand in the future. Survey replies emphasize the collective recognition of the need to enhance the accuracy of results in WWS practices, reflecting a shared commitment to advancing precision and effectiveness in WWS methodologies. Conclusions These surveys identified the need for quality standards and reference materials to enhance the accuracy and reliability of WWS methods in the future. In addition, it is important to broaden surveillance efforts beyond SARS-CoV-2 to include other emerging pathogens and antimicrobial resistance to ensure a comprehensive approach to protecting public health.

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Relationship between SARS-CoV-2 in wastewater and clinical data from five wastewater sheds

Cited by 7 publications

References 62 publications

SARS-CoV-2 wastewater surveillance in the Czech Republic: Spatial and temporal differences in SARS-CoV-2 RNA concentrations and relationship to clinical data and wastewater parameters

SARS-CoV-2 wastewater surveillance in the Czech Republic: Spatial and temporal differences in SARS-CoV-2 RNA concentrations and relationship to clinical data and wastewater parameters

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

EU surveys insights: analytical tools, future directions, and the essential requirement for reference materials in wastewater monitoring of SARS-CoV-2, antimicrobial resistance and beyond

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