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Related Experiment Video

Updated: May 22, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Advances in mosquito-borne disease surveillance using machine learning.

Mariana Geffroy^1,2,3, Juan Vicente Bogado Machuca⁴, Gerardo Suzán^1,2

¹Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico.

New Microbes and New Infections

|May 21, 2026

Summary

Machine learning aids mosquito-borne disease surveillance, offering new tools for forecasting and risk mapping. Further research is needed for validation and implementation in low-resource settings.

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A Multi-detection Assay for Malaria Transmitting Mosquitoes

A Multi-detection Assay for Malaria Transmitting Mosquitoes

Published on: February 28, 2015

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Last Updated: May 22, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

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Published on: October 27, 2023

A Multi-detection Assay for Malaria Transmitting Mosquitoes

A Multi-detection Assay for Malaria Transmitting Mosquitoes

Published on: February 28, 2015

Area of Science:

Public Health
Infectious Diseases
Artificial Intelligence

Background:

Mosquito-borne diseases (MBDs) pose a significant global health threat, with resurgence driven by environmental and societal changes.
Traditional control methods are insufficient, necessitating innovative surveillance strategies.

Purpose of the Study:

To systematically review the application of machine learning (ML) in MBD surveillance.
To identify trends, common ML algorithms, and challenges in the field.

Main Methods:

Systematic review adhering to PRISMA guidelines.
Analysis of 81 studies published between 2010 and 2024.
Focus on MBDs including malaria, dengue, Zika, chikungunya, and yellow fever.

Main Results:

ML techniques are increasingly used for MBD forecasting, risk mapping, and real-time monitoring.
Support vector machines, random forests, decision trees, and logistic regression are frequently employed ML algorithms.
Model performance is contingent on data quality, availability, and contextual relevance.

Conclusions:

ML shows promise for enhancing MBD surveillance, but implementation challenges persist.
Gaps exist in model validation, low-resource setting application, and integration of One Health data.
Future strategies should focus on addressing these gaps for effective MBD control.