Submitted by the MVCAC VVBD Committee
Zuo et al., Sci. Adv. 12, eadz7063 (2026) March.
Abstract. 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.