Details
| Original language | English |
|---|---|
| Article number | 6 |
| Journal | Methods and Protocols |
| Volume | 7 |
| Issue number | 1 |
| Publication status | Published - 5 Jan 2024 |
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.
Keywords
- information theory, optimal sampling point, pathogen surveillance, wastewater-based epidemiology
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry, Genetics and Molecular Biology (miscellaneous)
- Biochemistry, Genetics and Molecular Biology(all)
- Structural Biology
- Biochemistry, Genetics and Molecular Biology(all)
- Biotechnology
Sustainable Development Goals
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In: Methods and Protocols, Vol. 7, No. 1, 6, 05.01.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Optimal Selection of Sampling Points within Sewer Networks for Wastewater-Based Epidemiology Applications
AU - Yao, Yao
AU - Zhu, Yibo
AU - Nogueira, Regina
AU - Klawonn, Frank
AU - Wallner, Markus
N1 - This research was funded by the European Regional Development Fund (ERDF) and Lower Saxony, grant numbers ZW 7-85094959 and ZW 7-85125149.
PY - 2024/1/5
Y1 - 2024/1/5
N2 - 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.
AB - 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.
KW - information theory
KW - optimal sampling point
KW - pathogen surveillance
KW - wastewater-based epidemiology
UR - http://www.scopus.com/inward/record.url?scp=85187243205&partnerID=8YFLogxK
U2 - 10.3390/mps7010006
DO - 10.3390/mps7010006
M3 - Article
VL - 7
JO - Methods and Protocols
JF - Methods and Protocols
SN - 2409-9279
IS - 1
M1 - 6
ER -