The need to use AI and statistical methods in GeoHealth is constantly growing. The unique nature of GeoHealth is the need to pull data from disparate sources and search for signals often in noisy or incomplete data. Researchers are often fusing datasets from the environment, human population and demographics, and disease surveillance programs—each with their own uncertainties and spatiotemporal scales. Often, researchers are seeking the environmental drivers behind health outcomes, leading to analyzing numerous datasets leading to a large data problem. This amount of data and merging of multiple data streams sets the stage for advanced statistical and AI methods in GeoHealth. We invite talks that use cross-disciplinary statistical methods, AI techniques, or other exploratory approaches for analyzing big data in any discipline of GeoHealth.

• IN - Informatics