Using Machine-Learning Combined with a New Dynamical Climate-Driven Aedes-Albopictus Model to Assess the Impacts of Heatwaves and Derive Optimal Intervention Strategies.

We introduce a modified version of the climate-driven VECTRI model that has been extended to model the climate-sensitive lifecycle of the Aedes albopictus mosquito, evaluating the model performance over Italy. A set of simulation experiments then will show how heat-waves can enhance populations of this nuisance vector in most locations, with the exception of the very warmest regions in southern Italy, where the model predicts the vector to become homodynamic in the future with a warming climate and where heatwaves can have a detrimental effect due to their impact on increased vector mortality. Lastly, we will show preliminary results of combining a particle-filter based, machine learning algorithm to derive climate-aware, optimal intervention strategies against the vector.