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

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

Published on: April 28, 2019

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

William Hoyos¹, Jose Aguilar², Mauricio Toro³

¹Grupo 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.

Artificial Intelligence in Medicine

|September 17, 2021

Summary

This systematic literature review analyzes machine learning models for dengue control. Findings highlight the need for improved diagnostic models and secure data handling for better prediction and intervention strategies.

Keywords:

Dengue Diagnostic model Epidemic model Intervention model Machine learning

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Area of Science:

Epidemiology
Machine Learning
Public Health

Background:

Dengue modeling research is growing, with early prediction crucial for control.
Three main modeling approaches exist: diagnostic, epidemic, and intervention.
These models require prediction, prescription, and optimization capabilities.

Purpose of the Study:

To conduct a Systematic Literature Review (SLR) on dengue modeling using machine learning.
To establish the state-of-the-art in machine learning applications for dengue.
To identify challenges and opportunities in dengue modeling research.

Main Methods:

Searched multiple databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Analyzed sixty-four selected articles, detailing their strengths and limitations.
Identified research trends and common methodologies in dengue modeling.

Main Results:

Logistic regression is the dominant approach for dengue diagnosis (59.1%).
Linear regression is most common in spatial epidemic analysis (17.4%).
General Linear Model is the preferred method for intervention modeling (70%).

Conclusions:

Cause-effect models can enhance dengue diagnosis and understanding.
Models managing uncertainty are vital due to healthcare data quality issues.
Federated learning offers potential for decentralized data analysis, reducing costs and enhancing security.