274 - Climate Change and Mosquito Threats: Strategies for Modern Public Protection

Abstract: Climate change is accelerating ecological shifts that exacerbate the burden of mosquito-borne diseases globally. In the Americas, predicting when and where Aedes aegypti arboviral outbreaks will occur remains a critical challenge for public health and mosquito control agencies. This study aims to address that gap by developing robust, geospatial early-warning tools to forecast mosquito hotspot emergence based on over 40 years of integrated environmental and epidemiological data.

Leveraging Mexico as a geographically and climatically representative proxy for U.S. conditions, we assemble municipality-level datasets on dengue incidence, vector abundance, land use transitions, climate anomalies, and agricultural practices from 1980 to 2023. Using this rich dataset, we assess how land cover transformations interact with drought indices, vegetation stress, and pesticide exposure to influence the spatial and temporal dynamics of vector proliferation. A central innovation of our approach is the fusion of artificial intelligence (AI) and remote sensing to quantify and predict environmental conditions that heighten mosquito breeding potential and disease transmission.

By applying machine learning models to satellite-derived indicators and historical health data, we generate fine-scale predictive risk maps and identify high-priority zones for intervention. The resulting tools provide actionable insights for mosquito control programs by enabling proactive resource allocation, tailored larvicide/adulticide deployment, and longer-term land management planning. This project not only advances the scientific understanding of climate-agriculture-disease linkages, but also delivers a scalable framework that can be adapted across diverse agro-ecological settings threatened by climate-driven disease emergence.