Exploration on wastewater-based epidemiology of SARS-CoV-2: Mimic relative quantification with endogenous biomarkers as internal reference

Tang, Langjun; Wu, Jinyong; Li, Rui; Feng, Zhongxi; Zhang, Yanan; Zhao, Yingzhe; Li, Yonghong; Yang, Kun

doi:10.1016/j.heliyon.2023.e15705

Cited by 5 publications

(2 citation statements)

References 58 publications

(85 reference statements)

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“…One notable finding of our study is the sensitivity of the results to the normalization method and weighting of different samples. It is still an open research question, what the gold standard for normalization is 10 , 21 , 29 , 35 , 36 and without a proper reference cohort, it is hard to decide and may also be site-specific. Based on our data, the normalization with the PMMoV reference virus clearly outranks the flow based normalization, since we cannot see the beginning of the next wave that the SentiSurv data clearly indicates.…”

Section: Discussionmentioning

confidence: 99%

Estimating the COVID-19 prevalence from wastewater

Mohring,

Leithäuser,

Wlazło

et al. 2024

Sci Rep

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Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland–Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, the state of Rhineland–Palatinate has commissioned the University Medical Center at Johannes Gutenberg University Mainz to conduct a representative cohort study called SentiSurv, in which an increasing number of up to 12,000 participants have been using sensitive antigen self-tests once or twice a week to test themselves for SARS-CoV-2 and report their status. This puts the state of Rhineland–Palatinate in the fortunate position of having time series of both, the viral load in wastewater and the prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor ($$0.208 \pm 0.031$$ 0.208 ± 0.031 ) and a delay ($$5.07 \pm 2.30$$ 5.07 ± 2.30 days) between the virus load in wastewater, normalized by the pepper mild mottle virus (PMMoV), and the prevalence recorded in the SentiSurv study. The relation is established by fitting an epidemiological model to both time series. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized.

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Section: Discussionmentioning

confidence: 99%

Estimating the COVID-19 prevalence from wastewater

Mohring,

Leithäuser,

Wlazło

et al. 2024

Sci Rep

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show abstract

“…As pointed out above, the results are sensitive against the normalization method and weighting of different samples. It is still an open research question, what the gold standard for normalization is 10,21,29,35,36 and without a proper reference cohort, it is hard to decide and may also be site-specific. Based on our data, the normalization with the PMMoV reference virus clearly outranks the flow based normalization, since we cannot see the beginning of the next wave that the SentiSurv data clearly indicates.…”

Section: Discussionmentioning

confidence: 99%

Estimating the Covid-19 Prevalence from Wastewater

Mohring,

Leithäuser,

Wlazlo

et al. 2023

Preprint

View full text Add to dashboard Cite

Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2 RNA. Therefore, in 2022, also in Rhineland-Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, by commissioning the prevalence study SentiSurv, carried out by the University Medical Center of the Johannes Gutenberg University Mainz, the state of Rhineland-Palatinate has put itself in the fortunate position of having time series of both, the viral load in wastewater and the representative prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor (0.208 +/- 0.031) and a delay (5.07 +/- 2.30 days) between the PMMoV-normalized virus load and the self-reported prevalence with the aid of an adequate epidemiological model. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized.

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