Predicting mosquito flight behavior using Bayesian dynamical systems learning

Mosquito-borne diseases cause several hundred thousand deaths worldwide every year. Deciphering mosquito host-seeking behavior is essential to prevent disease transmission through mosquito capture and surveillance. Despite recent substantial progress, we still lack a comprehensive quantitative understanding of how visual and other sensory cues guide mosquitoes to their targets. Here, we combined three-dimensional infrared tracking of Aedes aegypti mosquitoes with Bayesian dynamical systems inference to learn a quantitative biophysical model of mosquito host-seeking behavior. Trained on more than 20 million data points, each corresponding to an instantaneous position and velocity in mosquito free-flight trajectories recorded in the presence of visual and carbon dioxide cues, the model accurately predicts how mosquitoes respond to human targets. Our results provide a quantitative foundation for optimizing mosquito capture and control strategies, a key step toward mitigating the impact of mosquito-borne diseases.