Predicting mosquito flight behavior using Bayesian dynamical systems learning

TL;DR

This study develops a Bayesian dynamical systems model based on extensive 3D tracking data to quantitatively predict mosquito host-seeking behavior, aiding disease control efforts.

Contribution

It introduces a novel biophysical model trained on large-scale flight data to accurately simulate mosquito responses to visual and chemical cues.

Findings

Model accurately predicts mosquito responses to human targets.

Quantitative understanding of sensory-guided flight behavior.

Provides foundation for improved mosquito control strategies.

Abstract

Mosquito-borne diseases cause several hundred thousand deaths every year. Deciphering mosquito host-seeking behavior is essential to prevent disease transmission through mosquito capture and surveillance. Despite recent substantial progress, we currently lack a comprehensive quantitative understanding of how visual and other sensory cues guide mosquitoes to their targets. Here, we combined 3D 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 20,000,000 data points from 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 towards mitigating the impact of mosquito-borne diseases.