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Current surveillance relies on manual mosquito traps and labor\u2010intensive lab tests like polymerase chain reaction (PCR) and enzyme\u2010linked immunosorbent assay (ELISA), which are time\u2010consuming and resource\u2010intensive. In this study, machine learning algorithms are applied to detect dengue\u2010infected mosquitoes based on their 3D flight patterns. Using a convolutional neural network (CNN) and cubic spline interpolation, mosquito flight trajectories are tracked, followed by classification with models including CNN, eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Random Forest, Decision Tree, Naive Bayes, Logistic Regression, Multi\u2010Layer Perceptron (MLP), and a hybrid CNN + XGBoost model. The 5\u2010fold cross\u2010validation results showed that XGBoost achieved the highest mean accuracy (81.43%), while Random Forest has shown the best mean F1 Score (82.80%). Some validation folds demonstrated outstanding performance, with AdaBoost reaching 95.85% accuracy and Random Forest achieving 97.77% recall in Fold 1. The study also analyzed the impact of flight sequence size on models' performance, observing that longer sequences provided more accurate predictions. This approach offers a faster and more efficient method for assessing infection status, supporting real\u2010time vector monitoring, and improving early disease outbreak detection.<\/jats:p>","DOI":"10.1002\/adbi.202400847","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T14:25:57Z","timestamp":1756391157000},"source":"Crossref","is-referenced-by-count":0,"title":["Detecting Dengue in Flight: Leveraging Machine Learning to Analyze Mosquito Flight Patterns for Infection Detection"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0520-3504","authenticated-orcid":false,"given":"Nouman","family":"Javed","sequence":"first","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation Deakin University  Geelong Victoria 3216 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam J.","family":"L\u00f3pez\u2010Denman","sequence":"additional","affiliation":[{"name":"CSIRO Health &amp; Biosecurity Australian Centre for Disease Preparedness  Geelong Victoria 3220 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prasad N.","family":"Paradkar","sequence":"additional","affiliation":[{"name":"CSIRO Health &amp; Biosecurity Australian Centre for Disease Preparedness  Geelong Victoria 3220 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asim","family":"Bhatti","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation Deakin University  Geelong Victoria 3216 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"e_1_2_10_1_1","doi-asserted-by":"publisher","DOI":"10.2987\/8756-971X(2007)23[133:BTSIAB]2.0.CO;2"},{"key":"e_1_2_10_2_1","unstructured":"World Health Organization A global brief on vector\u2010borne diseases World Health Organization Geneva 2014."},{"key":"e_1_2_10_3_1","unstructured":"W. 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