Advancing arthropod vector management through integrated models

Vector-borne diseases present an increasing public health challenge, due to the expanding range and elevating activity of key vectors, such as mosquitoes and ticks. To address these challenges, we aim to integrate different modelling approaches, combining insights from correlative and mechanistic frameworks to enhance vector surveillance and risk prediction. Our efforts focus on developing predictive tools that are adaptable to various geographical contexts and can provide useful public health guidance in response to environmental and climate change. We present a range of initiatives currently under development that combine advanced Artificial Intelligence (AI) and machine learning with mechanistic models to forecast mosquito and tick activity. While AI models leverage high-resolution climate data, including satellite observations, to predict vector dynamics, mechanistic models provide complementary insights into vectors' seasonal dynamics and population trends thanks to their simulation of biological processes. Preliminary results highlight the strengths and limitations of individual modelling approaches and ensemble approaches demonstrate the potential for more accurate predictions, particularly in complex or data-scarce settings. The research outputs include a user-friendly, real-time dashboard featuring intuitive visualizations of vector activity and actionable recommendations to inform public health initiatives and personal protective behaviours. These tools empower local stakeholders by facilitating proactive interventions, such as targeted vector control measures and public awareness campaigns. Our work not only enhances local vector management but also establishes a scalable framework applicable to other regions facing similar challenges. By integrating multiple modelling paradigms and fostering collaboration between modellers and public health stakeholders, we aim to advance vector surveillance, mitigate public health risks, and improve quality of life in the face of evolving environmental pressures