Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA
dc.contributor.author | Zehnder, C | |
dc.contributor.author | Béen, F | |
dc.contributor.author | Vojinovic, Z | |
dc.contributor.author | Savic, D | |
dc.contributor.author | Torres, AS | |
dc.contributor.author | Mark, O | |
dc.contributor.author | Zlatanovic, L | |
dc.contributor.author | Abebe, YA | |
dc.date.accessioned | 2023-11-14T10:33:13Z | |
dc.date.issued | 2023-10-04 | |
dc.date.updated | 2023-11-14T08:50:53Z | |
dc.description.abstract | Wastewater-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. | en_GB |
dc.identifier.citation | Vol. 7(10), article e2023GH000866 | en_GB |
dc.identifier.doi | https://doi.org/10.1029/2023GH000866 | |
dc.identifier.uri | http://hdl.handle.net/10871/134521 | |
dc.identifier | ORCID: 0000-0001-9567-9041 (Savic, Dragan) | |
dc.language.iso | en | en_GB |
dc.publisher | American Geophysical Union (AGU) / Wiley | en_GB |
dc.relation.url | https://data.rivm.nl/covid-19/COVID-19_prevalentie.json | en_GB |
dc.relation.url | https://www.ncbi.nlm.nih.gov/pubmed/37799774 | en_GB |
dc.rights | © 2023 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. | en_GB |
dc.subject | COVID‐19 | en_GB |
dc.subject | SARS‐CoV‐2 | en_GB |
dc.subject | machine learning | en_GB |
dc.subject | sewer network modeling | en_GB |
dc.subject | support vector machine | en_GB |
dc.subject | wastewater‐based epidemiology | en_GB |
dc.title | Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2023-11-14T10:33:13Z | |
dc.identifier.issn | 2471-1403 | |
exeter.article-number | ARTN e2023GH000866 | |
exeter.place-of-publication | United States | |
dc.description | This is the final version. Available on open access from the American Geophysical Union via the DOI in this record | en_GB |
dc.description | Data Availability Statement: The COVID-19 contagious persons per day data used for generating the COVID-19 hotspot prevalence in the study are available from the Dutch National Institute for Public Health and the Environment (RIVM) at https://data.rivm.nl/covid-19/COVID-19_prevalentie.json with license http://creativecommons.org/publicdomain/mark/1.0/deed.nl. | en_GB |
dc.identifier.eissn | 2471-1403 | |
dc.identifier.journal | GeoHealth | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | en_GB |
dcterms.dateAccepted | 2023-09-10 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2023-10-04 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2023-11-14T10:31:34Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2023-11-14T10:33:21Z | |
refterms.panel | B | en_GB |
refterms.dateFirstOnline | 2023-10-04 |
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