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A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

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Published on: April 28, 2019

Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review.

Maritza Cabrera^1,2, Jason Leake³, José Naranjo-Torres⁴

¹Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Universidad Católica del Maule, Talca 3480094, Chile.

Tropical Medicine and Infectious Disease

|October 26, 2022

Summary

This study reviews dengue fever prediction using a One Health approach and Machine Learning. It highlights the need for large-scale comparisons of dengue risk factors to improve predictive models.

Keywords:

Latin America climate change dengue epidemiology machine learning one health prediction

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Area of Science:

Epidemiology
Public Health
Environmental Science

Background:

Dengue fever is a significant and escalating global health issue, particularly in Latin America.
Climate change and human mobility exacerbate dengue transmission.
Existing epidemiological prediction models often lack comprehensive analysis of localized risk factors.

Purpose of the Study:

To review epidemiological prediction approaches for dengue fever through a One Health lens.
To analyze the application of Machine Learning techniques in dengue prediction.
To identify key risk factors for dengue in Latin America and their impact on disease incidence.

Main Methods:

Literature review of One Health approaches for dengue prediction.
Analysis of Machine Learning applications in epidemiological modeling of dengue.
Focus on environmental and socio-economic risk factors in Latin America.

Main Results:

Multiple factors act as predictors for dengue outbreaks.
A gap exists in large-scale comparative studies of dengue predictors across diverse geographic areas.
Machine Learning offers promising techniques for enhancing predictive models.

Conclusions:

Integrating One Health principles and Machine Learning is crucial for effective dengue surveillance.
Further research is needed to validate and compare dengue predictors on a larger scale.
Developing robust Machine Learning workflows can improve future dengue outbreak predictions.