Machine Learning for Detecting Virus Infection Hotspots Via Wastewater‐Based Epidemiology: The Case of SARS‐CoV‐2 RNA

Author:

Zehnder Calvin1ORCID,Béen Frederic2,Vojinovic Zoran1345,Savic Dragan234ORCID,Torres Arlex Sanchez1,Mark Ole6ORCID,Zlatanovic Ljiljana78,Abebe Yared Abayneh19ORCID

Affiliation:

1. Water Supply, Sanitation and Environmental Engineering Department IHE Delft Institute for Water Education Delft The Netherlands

2. KWR Water Research Institute Nieuwegein The Netherlands

3. Centre for Water Systems College of Engineering Mathematics and Physical Sciences University of Exeter Exeter UK

4. Faculty of Civil Engineering University of Belgrade Belgrade Serbia

5. National Cheng Kung University Tainan Taiwan

6. Krüger Veolia Søborg Denmark

7. Sanitary Engineering Delft University of Technology Delft The Netherlands

8. PWN Velserbroek The Netherlands

9. Department of Hydraulic Engineering Faculty of Civil Engineering and Geosciences Delft University of Technology Delft The Netherlands

Abstract

AbstractWastewater‐based epidemiology (WBE) has been proven to be a useful tool in monitoring public health‐related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater‐detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under‐represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically‐based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS‐CoV‐2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time‐resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back‐tracing of human‐excreted biomarkers based on only sampling at the outlet or other key points, but would require high‐frequency, contaminant‐specific sensor systems that are not available currently.

Publisher

American Geophysical Union (AGU)

Subject

Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Public Health, Environmental and Occupational Health,Pollution,Waste Management and Disposal,Water Science and Technology,Epidemiology,Global and Planetary Change

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