Dengue models based on machine learning techniques: A systematic literature review.

Journal: Artificial intelligence in medicine
PMID: 34531010

Abstract

BACKGROUND: Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years.

Authors

  • William Hoyos
    Grupo de Investigaciones Microbiológicas y Biomédicas de Córdoba, Universidad de Córdoba, Montería, Colombia; Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia. Electronic address: whoyos@correo.unicordoba.edu.co.
  • Jose Aguilar
    Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia; Centro de Estudios en Microelectrónica y Sistemas Distribuidos, Universidad de Los Andes, Mérida, Venezuela; Universidad de Alcalá, Depto. de Automática, Alcalá de Henares, Spain.
  • Mauricio Toro
    Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia.

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