tetano
Editor, Senior Moderator
Sci Rep
. 2024 Jun 22;14(1):14384.
doi: 10.1038/s41598-024-64864-1. Estimating the COVID-19 prevalence from wastewater
Jan Mohring[SUP] 1 [/SUP], Neele Leithäuser[SUP] 2 [/SUP], Jarosław Wlazło[SUP] 2 [/SUP], Marvin Schulte[SUP] 2 [/SUP], Maximilian Pilz[SUP] 2 [/SUP], Johanna Münch[SUP] 2 [/SUP], Karl-Heinz Küfer[SUP] 2 [/SUP]
Affiliations
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±0.031
) and a delay ( 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.
Keywords: Cohort study; Covid-19; Forecast; Mathematical modelling; Wastewater-based epidemiology.
. 2024 Jun 22;14(1):14384.
doi: 10.1038/s41598-024-64864-1. Estimating the COVID-19 prevalence from wastewater
Jan Mohring[SUP] 1 [/SUP], Neele Leithäuser[SUP] 2 [/SUP], Jarosław Wlazło[SUP] 2 [/SUP], Marvin Schulte[SUP] 2 [/SUP], Maximilian Pilz[SUP] 2 [/SUP], Johanna Münch[SUP] 2 [/SUP], Karl-Heinz Küfer[SUP] 2 [/SUP]
Affiliations
- PMID: 38909097
- PMCID: PMC11193770
- DOI: 10.1038/s41598-024-64864-1
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±0.031
) and a delay ( 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.
Keywords: Cohort study; Covid-19; Forecast; Mathematical modelling; Wastewater-based epidemiology.