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Advances in mosquito-borne disease surveillance using machine learning
Mariana Geffroy1,2,3, Juan Vicente Bogado Machuca4, Gerardo Suzán1,2
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.
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.
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