SARS-CoV-2 Wastewater Monitoring in Thuringia, Germany : Analytical Aspects and Normalization of Results

GND
1333439377
ORCID
0000-0003-3910-0845
Affiliation
Department Hamm 2, Hamm-Lippstadt University of Applied Sciences, 59063 Hamm, Germany;
Haeusser, Sarah;
ORCID
0009-0004-7596-9315
Affiliation
Analytik Jena GmbH + Co. KG, 07745 Jena, Germany
Möller, Robert;
GND
13652656X
VIAF
80855664
Affiliation
Institute of Digital and Autonomous Construction, Hamburg University of Technology, 21079 Hamburg, Germany
Smarsly, Kay;
Affiliation
Institute of Digital and Autonomous Construction, Hamburg University of Technology, 21079 Hamburg, Germany
Al-Hakim, Yousuf;
Affiliation
Institute for Water Quality and Resources Management, Vienna University of Technology, 1040 Vienna, Austria
Kreuzinger, Norbert;
Affiliation
Institute of Environmental Engineering, RWTH Aachen University, 52074 Aachen, Germany
Pinnekamp, Johannes;
GND
123964334
ORCID
0000-0001-8157-2753
Affiliation
Institute for Infectious Diseases and Infection Control, Jena University Hospital/Friedrich-Schiller-University
Pletz, Mathias W.;
Affiliation
Department Hamm 2, Hamm-Lippstadt University of Applied Sciences, 59063 Hamm, Germany;
Kluemper, Claudia;
Affiliation
Bauhaus-Institute for Infrastructure Solutions (b.is), Bauhaus University Weimar, 99423 Weimar, Germany;
Beier, Silvio

Wastewater monitoring for SARS-CoV-2 is a valuable tool for surveillance in public health. However, reliable analytical methods and appropriate approaches for the normalization of results are important requirements for implementing state-wide monitoring programs. In times of insufficient case reporting, the evaluation of wastewater data is challenging. Between December 2021 and July 2022, we analyzed 646 samples from 23 WWTPs in Thuringia, Germany. We investigated the performance of a direct capture-based method for RNA extraction (4S-method) and evaluated four normalization methods (NH 4 -N, COD, N tot , and PMMoV) in a pooled analysis using different epidemiological metrics. The performance requirements of the 4S method were well met. The method could be successfully applied to implement a state-wide wastewater monitoring program including a large number of medium and small wastewater treatment plants (<100,000 p.e) in high spatial density. Correlations between wastewater data and 7-day incidence or 7-day-hospitalization incidence were strong and independent from the normalization method. For the test positivity rate, PMMoV-normalized data showed a better correlation than data normalized with chemical markers. In times of low testing frequency and insufficient case reporting, 7-day-incidence data might become less reliable. Alternative epidemiological metrics like hospital admissions and test positivity data are increasingly important for evaluating wastewater monitoring data and normalization methods. Furthermore, future studies need to address the variance in biological replicates of wastewater.

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