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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Vydané v:Viruses Ročník 17; číslo 1; s. 109
Hlavní autori: Armenta-Castro, Arnoldo, de la Rosa, Orlando, Aguayo-Acosta, Alberto, Oyervides-Muñoz, Mariel Araceli, Flores-Tlacuahuac, Antonio, Parra-Saldívar, Roberto, Sosa-Hernández, Juan Eduardo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 01.01.2025
MDPI
Predmet:
Algorithms
Biomarkers
Catchment areas
Cluster Analysis
College campuses
COVID-19
COVID-19 - epidemiology
COVID-19 vaccines
data-based decision-making
Datasets
Decision Making
Epidemiology
Genomes
Health aspects
Health surveillance
Humans
Immunization
Infectious diseases
Machine Learning
Medical research
Medicine, Experimental
Methods
Mexico - epidemiology
Neural networks
Public health
Regression analysis
Risk Assessment
SARS-CoV-2
Sentinel health events
Severe acute respiratory syndrome coronavirus 2
Social distancing
Wastewater
Wastewater - virology
wastewater surveillance
Wastewater treatment
Wastewater-Based Epidemiological Monitoring
Mexico
United States
United States > US
SARS-CoV-2
machine learning
data-based decision-making
wastewater surveillance
ISSN:1999-4915, 1999-4915
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Shrnutí: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 (R2 = 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.
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ISSN:1999-4915
1999-4915
DOI:10.3390/v17010109