PreprintArticleVersion 1This version is not peer-reviewed
Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making
Version 1
: Received: 16 October 2024 / Approved: 16 October 2024 / Online: 17 October 2024 (02:47:08 CEST)
How to cite:
Armenta-Castro, A.; de la Rosa, O.; Aguayo-Acosta, A.; Oyervides-Muñoz, M. A.; Flores-Tlacuahuac, A.; Parra-Saldívar, R.; Sosa-Hernández, J. E. Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making. Preprints2024, 2024101297. https://doi.org/10.20944/preprints202410.1297.v1
Armenta-Castro, A.; de la Rosa, O.; Aguayo-Acosta, A.; Oyervides-Muñoz, M. A.; Flores-Tlacuahuac, A.; Parra-Saldívar, R.; Sosa-Hernández, J. E. Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making. Preprints 2024, 2024101297. https://doi.org/10.20944/preprints202410.1297.v1
Armenta-Castro, A.; de la Rosa, O.; Aguayo-Acosta, A.; Oyervides-Muñoz, M. A.; Flores-Tlacuahuac, A.; Parra-Saldívar, R.; Sosa-Hernández, J. E. Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making. Preprints2024, 2024101297. https://doi.org/10.20944/preprints202410.1297.v1
APA Style
Armenta-Castro, A., de la Rosa, O., Aguayo-Acosta, A., Oyervides-Muñoz, M. A., Flores-Tlacuahuac, A., Parra-Saldívar, R., & Sosa-Hernández, J. E. (2024). Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making. Preprints. https://doi.org/10.20944/preprints202410.1297.v1
Chicago/Turabian Style
Armenta-Castro, A., Roberto Parra-Saldívar and Juan Eduardo Sosa-Hernández. 2024 "Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making" Preprints. https://doi.org/10.20944/preprints202410.1297.v1
Abstract
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 83.3% accuracy rate. Moreover, the clustering model provided satisfactory forecasts one week (79.2% accuracy) and two weeks (72.9%) into the future. However, the prediction of the weekly average of new daily cases was limited (R2 = 0.452, MAPE = 180.2%), 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.
Public Health and Healthcare, Public, Environmental and Occupational Health
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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