Parallel deployment of passive and composite samplers for surveillance and variant profiling of SARS-CoV-2 in sewage

Cha, Gyuhyon; Zhu, Kevin; Rao, Gouthami; Lindner, B.; Kocaman, Kumru; Woo, Seongwook; D'amico, Isabelle; Bingham, Lilia  R.; Fischer, Jamie Montague; Flores, Camryn I.; Spencer, John W.; Yathiraj, Pranav; Chung, Hayong; Biliya, Shweta; Djeddar, Naima; Burton, Liza J.; Mascuch, Samantha J.; Brown, Joe; Bryksin, Anton V.; Pinto, Ameet J.; Hatt, Janet K.; Konstantinidis, Konstantinos T.

doi:10.1016/j.scitotenv.2022.161101

Cited by 20 publications

(11 citation statements)

References 44 publications

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“…Failing to pass QC is likely due to poor sample quality resulting in low mapping rates, the cause of which needs further investigation. Previous studies using passive samplers for near-source wastewater monitoring also successfully sequenced the Delta AY.103 and the Omicron BA.1 and BA.2 variants with similar QC pass rates as established in the current study [38,39]. Furthermore, metaviromics approaches have also been successfully applied to passive sampler extracts for the identification of human pathogen viruses in wastewater [40], suggesting that the sample quality is sufficient for a range of genomics applications.…”

Section: Discussionsupporting

confidence: 60%

Near-source passive sampling for monitoring viral outbreaks within a university residential setting

Farkas,

Kevill,

Adwan

et al. 2024

Epidemiol. Infect.

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Wastewater-based epidemiology (WBE) has proven to be a powerful tool for the populationlevel monitoring of pathogens, particularly severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). For assessment, several wastewater sampling regimes and methods of viral concentration have been investigated, mainly targeting SARS-CoV-2. However, the use of passive samplers in near-source environments for a range of viruses in wastewater is still under-investigated. To address this, near-source passive samples were taken at four locations targeting student hall of residence. These were chosen as an exemplar due to their high population density and perceived risk of disease transmission. Viruses investigated were SARS-CoV-2 and its variants of concern (VOCs), influenza viruses, and enteroviruses. Sampling was conducted either in the morning, where passive samplers were in place overnight (17 h) and during the day, with exposure of 7 h. We demonstrated the usefulness of near-source passive sampling for the detection of VOCs using quantitative polymerase chain reaction (qPCR) and next-generation sequencing (NGS). Furthermore, several outbreaks of influenza A and sporadic outbreaks of enteroviruses (some associated with enterovirus D68 and coxsackieviruses) were identified among the resident student population, providing evidence of the usefulness of nearsource, in-sewer sampling for monitoring the health of high population density communities.Key findings -Wastewater surveillance is feasible for small, high-density communities -Passive sampling is a low-cost, simple approach for building-level wastewater monitoring -Enteric and respiratory pathogens can be monitored in sewers quantitatively via quantitative polymerase chain reaction (qPCR) -Viral variants/strains can also be identified in sewers via sequencing Epidemiology and Infection www.cambridge.org/hyg

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Section: Discussionsupporting

confidence: 60%

Near-source passive sampling for monitoring viral outbreaks within a university residential setting

Farkas,

Kevill,

Adwan

et al. 2024

Epidemiol. Infect.

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“…VOI Zeta (P.2), a descendant of the B.1.1.28 strain, was first described in December 2020 in the state of Rio de Janeiro, Brazil, and rapidly spread across the country (Voloch et al 2021 ). Different SARS-CoV-2 variants have been detected in wastewater, and since then, different methods have been compared for virus sequencing (Cha et al 2023 ; Girón-Guzmán et al 2023 ). Recent studies suggest a combination of centralized and decentralized WBE implementation in high-, middle-, and low-income countries for genomic surveillance (Amin et al 2023 ; Gonçalves et al 2022 ; Jarvie et al 2023 ; Prado et al 2023 ).…”

Section: Resultsmentioning

confidence: 99%

SARS-CoV-2 surveillance-based on municipal solid waste leachate in Brazil

Lanzarini

Mannarino

Ribeiro

et al. 2023

Environ Sci Pollut Res

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Municipal solid waste leachate-based epidemiology is an alternative viral tracking tool that applies fresh truck leachate as an early warning of public health emergencies. This study aimed to investigate the potential of SARS-CoV-2 surveillance based on solid waste fresh truck leachate. Twenty truck leachate samples were ultracentrifugated, nucleic acid extracted, and real-time RT-qPCR SARS-CoV-2 N1/N2 applied. Viral isolation, variant of concern (N1/N2) inference, and whole genome sequencing were also performed. SARS-CoV-2 was detected on 40% (8/20) of samples, with a concentration from 2.89 to 6.96 RNA Log 10 100 mL −1 . The attempt to isolate SARS-CoV-2 and recover the whole genome was not successful; however, positive samples were characterized as possible pre-variant of concern (pre-VOC), VOC Alpha (B.1.1.7) and variant of interest Zeta (P.2). This approach revealed an alternative tool to infer SARS-CoV-2 in the environment and may help the management of local surveillance, health, and social policies. Graphical Abstract

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“…Better spatial coverage achieves earlier warnings of the start of an epidemic wave and better targeting of control measures [41]. As wastewater surveillance grows, we may see samples being taken at more WWTPs or even within the sewerage network, reaching ever smaller populations [42,43]. While our results demonstrate that surveillance for Covid-19 can be useful over catchment populations as low as 1500, we don’t know precisely where the limitations of the method lie.…”

Section: Discussionmentioning

confidence: 98%

The impact of signal variability on epidemic growth rate estimation from wastewater surveillance data

Colman

Kao

2023

Preprint

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Background: Testing samples of waste water for markers of infectious disease became a widespread method of surveillance during the COVID-19 pandemic. While these data generally correlate well with other indicators of national prevalence, samples that cover localised regions tend to be highly variable over short time scales. Methods: We introduce a procedure for estimating the real-time growth rate of pathogen prevalence using time series data from wastewater sampling. The number of copies of a target gene found in a sample is modelled as time-dependent random variable whose distribution is estimated using maximum likelihood. The output depends on a hyperparameter that controls the sensitivity to variability in the underlying data. We apply this procedure to data reporting the number of copies of the N1 gene of SARS-CoV-2 collected at water treatment works across Scotland between February 2021 and February 2023. Results: The real-time growth rate of the SARS-CoV-2 prevalence is estimated at 121 wastewater sampling sites covering a diverse range of locations and population sizes. We find that the sensitivity of the fitting procedure to natural variability determines its reliability in detecting the early stages of an epidemic wave. Applying the procedure to hospital admissions data, we find that changes in the growth rate are detected an average of 2 days earlier in wastewater than in hospital admissions data. Conclusion: We provide a robust method to generate reliable estimates of epidemic growth from highly variable data. Applying this method to samples collected at wastewater treatment works provides highly responsive situational awareness to inform public health.

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Parallel deployment of passive and composite samplers for surveillance and variant profiling of SARS-CoV-2 in sewage

Cited by 20 publications

References 44 publications

Near-source passive sampling for monitoring viral outbreaks within a university residential setting

Near-source passive sampling for monitoring viral outbreaks within a university residential setting

SARS-CoV-2 surveillance-based on municipal solid waste leachate in Brazil

The impact of signal variability on epidemic growth rate estimation from wastewater surveillance data

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