Optimal Selection of Sampling Points within Sewer Networks for Wastewater-Based Epidemiology Applications

authored by: Yao Yao, Yibo Zhu, Regina Nogueira, Frank Klawonn, Markus Wallner
Abstract: Wastewater-based epidemiology (WBE) has great potential to monitor community public health, especially during pandemics. However, it faces substantial hurdles in pathogen surveillance through WBE, encompassing data representativeness, spatiotemporal variability, population estimates, pathogen decay, and environmental factors. This paper aims to enhance the reliability of WBE data, especially for early outbreak detection and improved sampling strategies within sewer networks. The tool implemented in this paper combines a monitoring model and an optimization model to facilitate the optimal selection of sampling points within sewer networks. The monitoring model utilizes parameters such as feces density and average water consumption to define the detectability of the virus that needs to be monitored. This allows for standardization and simplicity in the process of moving from the analysis of wastewater samples to the identification of infection in the source area. The entropy-based model can select optimal sampling points in a sewer network to obtain the most specific information at a minimum cost. The practicality of our tool is validated using data from Hildesheim, Germany, employing SARS-CoV-2 as a pilot pathogen. It is important to note that the tool’s versatility empowers its extension to monitor other pathogens in the future.
Organisation(s): Institute of Sanitary Engineering and Waste Management
External Organisation(s): Helmholtz Centre for Infection Research (HZI)
Ostfalia University of Applied Sciences
Federal Institute for Geosciences and Natural Resources (BGR)
Consulting Engineer
BPI Hannover * Verworn Beratende Ingenieure
Type: Article
Journal: Methods and Protocols
Volume: 7
ISSN: 2409-9279
Publication date: 05.01.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Biochemistry, Genetics and Molecular Biology (miscellaneous), Structural Biology, Biotechnology
Sustainable Development Goals: SDG 3 - Good Health and Well-being
Electronic version(s): https://doi.org/10.3390/mps7010006 (Access: Open)