Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making
Detection and quantification of disease-related biomarkers in wastewater samples, denominated Wastewater-based Surveillance (WBS), has proven a valuable strategy for studying the prevalence of infectious diseases within populations in a time- and resource-efficient manner, as wastewater samples are...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Viruses |
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| Online Access: | https://www.mdpi.com/1999-4915/17/1/109 |
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| author | Arnoldo Armenta-Castro Orlando de la Rosa Alberto Aguayo-Acosta Mariel Araceli Oyervides-Muñoz Antonio Flores-Tlacuahuac Roberto Parra-Saldívar Juan Eduardo Sosa-Hernández |
| author_facet | Arnoldo Armenta-Castro Orlando de la Rosa Alberto Aguayo-Acosta Mariel Araceli Oyervides-Muñoz Antonio Flores-Tlacuahuac Roberto Parra-Saldívar Juan Eduardo Sosa-Hernández |
| author_sort | Arnoldo Armenta-Castro |
| collection | DOAJ |
| description | Detection and quantification of disease-related biomarkers in wastewater samples, denominated Wastewater-based Surveillance (WBS), has proven a valuable strategy for studying the prevalence of infectious diseases within populations in a time- and resource-efficient manner, as wastewater samples are representative of all cases within the catchment area, whether they are clinically reported or not. However, analysis and interpretation of WBS datasets for decision-making during public health emergencies, such as the COVID-19 pandemic, remains an area of opportunity. In this article, a database obtained from wastewater sampling at wastewater treatment plants (WWTPs) and university campuses in Monterrey and Mexico City between 2021 and 2022 was used to train simple clustering- and regression-based risk assessment models to allow for informed prevention and control measures in high-affluence facilities, even if working with low-dimensionality datasets and a limited number of observations. When dividing weekly data points based on whether the seven-day average daily new COVID-19 cases were above a certain threshold, the resulting clustering model could differentiate between weeks with surges in clinical reports and periods between them with an 87.9% accuracy rate. Moreover, the clustering model provided satisfactory forecasts one week (80.4% accuracy) and two weeks (81.8%) into the future. However, the prediction of the weekly average of new daily cases was limited (R<sup>2</sup> = 0.80, MAPE = 72.6%), likely because of insufficient dimensionality in the database. Overall, while simple, WBS-supported models can provide relevant insights for decision-makers during epidemiological outbreaks, regression algorithms for prediction using low-dimensionality datasets can still be improved. |
| format | Article |
| id | doaj-art-745f060298174253945a0f9b975f2c9b |
| institution | Kabale University |
| issn | 1999-4915 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj-art-745f060298174253945a0f9b975f2c9b2025-01-24T13:52:37ZengMDPI AGViruses1999-49152025-01-0117110910.3390/v17010109Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-MakingArnoldo Armenta-Castro0Orlando de la Rosa1Alberto Aguayo-Acosta2Mariel Araceli Oyervides-Muñoz3Antonio Flores-Tlacuahuac4Roberto Parra-Saldívar5Juan Eduardo Sosa-Hernández6School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, MexicoBiomolecular Innovation Group, Facultad de Agronomía, Universidad Autónoma de Nuevo León, Francisco Villa S/N, Col. Ex Hacienda El Canadá, General Escobedo 66415, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, MexicoDetection and quantification of disease-related biomarkers in wastewater samples, denominated Wastewater-based Surveillance (WBS), has proven a valuable strategy for studying the prevalence of infectious diseases within populations in a time- and resource-efficient manner, as wastewater samples are representative of all cases within the catchment area, whether they are clinically reported or not. However, analysis and interpretation of WBS datasets for decision-making during public health emergencies, such as the COVID-19 pandemic, remains an area of opportunity. In this article, a database obtained from wastewater sampling at wastewater treatment plants (WWTPs) and university campuses in Monterrey and Mexico City between 2021 and 2022 was used to train simple clustering- and regression-based risk assessment models to allow for informed prevention and control measures in high-affluence facilities, even if working with low-dimensionality datasets and a limited number of observations. When dividing weekly data points based on whether the seven-day average daily new COVID-19 cases were above a certain threshold, the resulting clustering model could differentiate between weeks with surges in clinical reports and periods between them with an 87.9% accuracy rate. Moreover, the clustering model provided satisfactory forecasts one week (80.4% accuracy) and two weeks (81.8%) into the future. However, the prediction of the weekly average of new daily cases was limited (R<sup>2</sup> = 0.80, MAPE = 72.6%), likely because of insufficient dimensionality in the database. Overall, while simple, WBS-supported models can provide relevant insights for decision-makers during epidemiological outbreaks, regression algorithms for prediction using low-dimensionality datasets can still be improved.https://www.mdpi.com/1999-4915/17/1/109SARS-CoV-2wastewater surveillancemachine learningdata-based decision-making |
| spellingShingle | Arnoldo Armenta-Castro Orlando de la Rosa Alberto Aguayo-Acosta Mariel Araceli Oyervides-Muñoz Antonio Flores-Tlacuahuac Roberto Parra-Saldívar Juan Eduardo Sosa-Hernández Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making Viruses SARS-CoV-2 wastewater surveillance machine learning data-based decision-making |
| title | Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making |
| title_full | Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making |
| title_fullStr | Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making |
| title_full_unstemmed | Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making |
| title_short | Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making |
| title_sort | interpretation of covid 19 epidemiological trends in mexico through wastewater surveillance using simple machine learning algorithms for rapid decision making |
| topic | SARS-CoV-2 wastewater surveillance machine learning data-based decision-making |
| url | https://www.mdpi.com/1999-4915/17/1/109 |
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