Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology

Rauch, Wolfgang; Schenk, Hannes; Insam, Heribert; Markt, Rudolf; Kreuzinger, Norbert

doi:10.1016/j.envres.2022.113809

Cited by 26 publications

(27 citation statements)

References 32 publications

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“…Viral load was defined as the concentration of gene copies detected multiplied by the daily wastewater flow rate. For comparison between WWTPs, viral loads were normalized to NH 4 -N load assuming a NH 4 -N excretion per person and day of 8 g N [ 37 , 38 ] resulting in mega (M), giga (G), or tera (T) gene copies per person per day (gcPE −1 /d −1 ). To test for significant difference of reported NH 4 -N concentration between WWTPs, the Kruskal–Wallis rank sum test and the Wilcoxon rank sum as post hoc test with Bonferroni correction were performed.…”

Section: Methodsmentioning

confidence: 99%

Expanding the Pathogen Panel in Wastewater Epidemiology to Influenza and Norovirus

Markt

Stillebacher

Nägele

et al. 2023

Viruses

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Since the start of the 2019 pandemic, wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the prevalence of SARS-CoV-2. With methods and infrastructure being settled, it is time to expand the potential of this tool to a wider range of pathogens. We used over 500 archived RNA extracts from a WBE program for SARS-CoV-2 surveillance to monitor wastewater from 11 treatment plants for the presence of influenza and norovirus twice a week during the winter season of 2021/2022. Extracts were analyzed via digital PCR for influenza A, influenza B, norovirus GI, and norovirus GII. Resulting viral loads were normalized on the basis of NH4-N. Our results show a good applicability of ammonia-normalization to compare different wastewater treatment plants. Extracts originally prepared for SARS-CoV-2 surveillance contained sufficient genomic material to monitor influenza A, norovirus GI, and GII. Viral loads of influenza A and norovirus GII in wastewater correlated with numbers from infected inpatients. Further, SARS-CoV-2 related non-pharmaceutical interventions affected subsequent changes in viral loads of both pathogens. In conclusion, the expansion of existing WBE surveillance programs to include additional pathogens besides SARS-CoV-2 offers a valuable and cost-efficient possibility to gain public health information.

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Section: Methodsmentioning

confidence: 99%

Expanding the Pathogen Panel in Wastewater Epidemiology to Influenza and Norovirus

Markt

Stillebacher

Nägele

et al. 2023

Viruses

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“…However, as the available survey data for parameter estimation is limited, we refrain here from doing so. ) and coefficient of determination (𝑅 2 )see e.g., Rauch et al [21]. The results of all five metrics indicate a high pairwise similarity of all three models, with POS-WBE forming a cluster (𝑅 2 = 0.93).…”

Section: True Infection Dynamicsmentioning

confidence: 87%

“…Wastewater based epidemiology (WBE) in the context of public health aims to derive information on the occurrence of pathogens in the watershed of a sewer system by samplingusually at the influent of a treatment plant [35]. Adapting the basic formulation for the case of SARS-CoV-2, the (measured) virus load at the monitoring point is related to the population drained with the sewer system (for details on data preprocessing see e.g., Rauch et al [21]):…”

Section: Estimating Prevalence From Wastewater-based Epidemiologymentioning

confidence: 99%

“…The timeline of the data in Figure 1 specifies the Austrian situation on a national basis from the start of the monitoring in April 2020 to December 2022. Note that we apply a moving average smoothing filter to the data with a sampling width of 7 days for NINF, NFAT and Id (see Rauch et al [21]). We use the same smoothing filter also for NTEST but need to set the sampling width here to 21 days due to the high random fluctuations in the number of daily tests.…”

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confidence: 99%

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Estimating actual SARS-CoV-2 infections from secondary data

Rauch,

Schenk,

Rauch

et al. 2023

Preprint

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Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in clinical testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., a) test positivity b) infection fatality and c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation – rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

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“…42 While it is thus impossible to detect a rise immediately, in the medium term, such patterns become obvious and share a high correlation with other measurements of infected people. A standard approach to smoothing this noisy data is the application of local regression techniques such as the LOESS method 43,44 . In this paper, as described in section Methods, we feed the original measurement values into a mathematical model that tries to fit a smooth functional relation as good as possible.…”

Section: Wastewater Datamentioning

confidence: 99%

Estimating the Covid-19 Prevalence from Wastewater

Mohring,

Leithäuser,

Wlazlo

et al. 2023

Preprint

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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 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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Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology

Cited by 26 publications

References 32 publications

Expanding the Pathogen Panel in Wastewater Epidemiology to Influenza and Norovirus

Expanding the Pathogen Panel in Wastewater Epidemiology to Influenza and Norovirus

Estimating actual SARS-CoV-2 infections from secondary data

Estimating the Covid-19 Prevalence from Wastewater

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