GeoAI and spatiotemporal epidemiology to forecast flavivirus transmission

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#### **Integrating geospatial artificial intelligence (GeoAI) and spatiotemporal epidemiology to forecast flavivirus transmission risk across the Continental US** **Objective:** To integrate deep learning, Bayesian inference, high‐performance computing (HPC), and a suite of continental‐scale, high‐dimensional datasets to improve viral outbreak forecasts by more accurately predicting vector‐host associations, West Nile virus (WNV) epidemic size (number of cases), and virus prevalence at fine spatial and temporal resolutions. A secondary goal is to develop accessible and intuitive training resources and user tutorials, so that other researchers can apply newly developed methods and workflows to their own projects.

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