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Detecting Dengue in Flight: Leveraging Machine Learning to Analyze Mosquito Flight Patterns for Infection Detection.

Nouman Javed¹, Adam J López-Denman², Prasad N Paradkar²

¹Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Victoria, 3216, Australia.

Advanced Biology

|August 28, 2025

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Machine learning accurately detects dengue-infected mosquitoes using 3D flight patterns, offering a faster alternative to traditional methods for disease surveillance and outbreak prediction.

Area of Science:

Vector-borne disease surveillance
Computational biology
Machine learning applications

Background:

Mosquito-borne diseases pose a significant global health threat.
Current surveillance methods (traps, PCR, ELISA) are slow and resource-intensive.
Need for automated, rapid assessment of mosquito infection status.

Purpose of the Study:

To develop and evaluate machine learning models for detecting dengue-infected mosquitoes based on flight patterns.
To compare the performance of various classification algorithms.
To assess the impact of flight data characteristics on prediction accuracy.

Main Methods:

Utilized convolutional neural networks (CNN) and cubic spline interpolation for 3D flight trajectory tracking.
Classified infected mosquitoes using multiple machine learning models: CNN, XGBoost, AdaBoost, Random Forest, Decision Tree, Naive Bayes, Logistic Regression, MLP, and a hybrid CNN+XGBoost.

Keywords:

3D flight patterns disease outbreak machine learning algorithms mosquito‐borne diseases vector monitoring

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Employed 5-fold cross-validation to evaluate model performance.

Main Results:

XGBoost achieved the highest mean accuracy (81.43%), and Random Forest yielded the best mean F1 Score (82.80%).
Exceptional performance was observed in specific validation folds, with AdaBoost reaching 95.85% accuracy and Random Forest achieving 97.77% recall.
Model accuracy improved with longer mosquito flight sequences.

Conclusions:

Machine learning analysis of 3D mosquito flight patterns provides a rapid and efficient method for infection status assessment.
This approach supports real-time vector monitoring and enhances early detection of disease outbreaks.
Flight pattern analysis offers a promising alternative to conventional, labor-intensive surveillance techniques.