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A Machine Learning Tool with An Integrated Dataset Towards the Construction of An Early Warning System for Dengue in Zulia State, Venezuela.

Introduction: The present study arises in response to the sustained increase in dengue outbreaks in Latin America, with special emphasis on the state of Zulia, Venezuela. This region, composed of 21 municipalities, is highly vulnerable to dengue transmission. Given this scenario, it is essential to...

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Published in:Revista del Cuerpo Médico del Hospital Nacional Almanzor Aguinaga Asenjo Vol. 18; no. 2; pp. 1 - 31
Main Authors: Maritza, Cabrera, José, Naranjo-Torres, Ángel, Cabrera, Lysien, Zambrano, Alfonso J., Rodriguez-Morales
Format: Article
Published: Cuerpo Medico del Hospital Nacional Almanzor Aguinaga Asenjo abr-jun2025
Subjects:
El Niño
Machine learning
Kriging
La Niña
Support vector machines
Online Access:View this record in EBSCOhost
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        atl: A Machine Learning Tool with An Integrated Dataset Towards the Construction of An Early Warning System for Dengue in Zulia State, Venezuela.
      aug:
        au:
          Maritza, Cabrera
          José, Naranjo-Torres
          Ángel, Cabrera
          Lysien, Zambrano
          Alfonso J., Rodriguez-Morales
        affil:
          Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Universidad Católica del Maule, Talca 3480094, Chile
          Academic and ML Consulting Department, Global Consulting H&G, 8682 Sorrento Street, Orlando, FL 32819, USA
          Facultad de Ingeniería, Universidad Fermín Toro, Calle Principal C.C. Chucho Briceño Cabudare, Lara, Venezuela
          Department of Morphological Sciences, School of Medical, Sciences, Universidad Nacional Autónoma de Honduras, Tegucigalpa, Honduras
          Master of Clinical Epidemiology and Biostatistics, Universidad Cientfica del Sur, Lima, 15067, Peru
          Grupo de Investigación Biomedicina, Faculty of Medicine, Fundación Universitaria Autónoma de las Américas-Institución Universitaria Visión de las Américas, Pereira, Colombia
      su:
        El Niño
        Machine learning
        Kriging
        La Niña
        Support vector machines
      sug:
        subj:
          El Niño
          Machine learning
          Kriging
          La Niña
          Support vector machines
      keyword:
        Dengue fever
        Disease Outbreaks
        Epidemiology
        Machine Learning
        Aprendizaje automático
        Brotes de enfermedades
        Epidemiologia
        Fiebre de Dengue
      ab:
        Introduction: The present study arises in response to the sustained increase in dengue outbreaks in Latin America, with special emphasis on the state of Zulia, Venezuela. This region, composed of 21 municipalities, is highly vulnerable to dengue transmission. Given this scenario, it is essential to have tools that allow early detection of outbreaks and, thus, optimize prevention and public health intervention strategies. The main objective is to develop an early warning system for dengue outbreaks using machine learning (ML) techniques. Materials and methods: Several data sources are integrated: epidemiological information, meteorological parameters, El Niño and La Niña (Niño 3.4 Index), socioeconomic and demographic variables. Two ML models were used: Support Vector Machine for regression (SVM-R) and Gaussian Process Regression (GPR). Results: The predictions obtained showed remarkable agreement with the actual dates on which the outbreaks were recorded, warning of the onset of dengue 2 to 3 weeks in advance, depending on the locality. However, in certain municipalities the predictions were less accurate, a finding that agrees with previous studies. Conclusions: In conclusion, the integration of epidemiological, climatological, and socioeconomic variables using ML techniques is presented as a promising tool for establishing.
        Introducción: El presente estudio surge como respuesta al sostenido aumento de los brotes de dengue en América Latina, con especial énfasis en el estado Zulia, Venezuela. Esta región, compuesta por 21 municipios, enfrenta una alta vulnerabilidad frente a la transmisión del dengue. Ante este escenario, resulta fundamental contar con herramientas que permitan la detección temprana de los brotes y, de esta forma, optimizar las estrategias de prevención e intervención en salud pública. El objetivo principal es desarrollar un sistema de alerta temprana para brotes de dengue utilizando técnicas de machine learning (ML). Materiales y métodos: Se integran diversas fuentes de datos: información epidemiológica, parámetros meteorológicos, El Niño and La Niña (Niño 3.4 Indice), variables socioeconómicas y demográficas. Se emplearon dos modelos de ML: Support Vector Machine para regresión (SVM-R) y el Gaussian Process Regression (GPR). Resultados: Las predicciones obtenidas mostraron una notable concordancia con las fechas reales en que se registraron los brotes, advirtiendo la aparición del dengue con una anticipación de 2 a 3 semanas, dependiendo de la localidad. Sin embargo, en ciertos municipios las predicciones fueron menos precisas, hallazgo que concuerda con estudios previos. Conclusiones: En conclusión, la integración de variables epidemiológicas, climatológicas y socioeconómicas mediante técnicas de ML se presenta como una herramienta prometedora para el establecimiento de sistemas de alerta temprana, aunque se recomienda continuar afinando los modelos de manera específica según las particularidades de cada municipio.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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