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Minisymposium Presentation
Enhanced Uncertainty Quantification in Air Pollution Models and Impact on Epidemiological Risk
Presenter
Dr. Habre is an Associate Professor of Environmental Health and Spatial Sciences at the University of Southern California (USC). Her research aims to understand the effects of complex air pollution mixtures and climate-related exposures on the health of vulnerable populations across the life course. She leads the CLIMA (CLIMAte-Related Exposures, Adaptation and Health Equity) Center at USC and co-leads the NEXUS: Network for Exposomics in the U.S. Center aiming to advance precision environmental health. Habre received an Sc.D. in environmental health from the Harvard T.H. Chan School of Public Health.
Description
Advancements in remote sensing, geospatial data, physicochemical and source apportionment models, citizen science networks and machine learning have greatly improved our ability to predict air pollution at high spatiotemporal resolution and over large domains and time periods. Air pollution models with high predictive performance and low uncertainty are critical for estimating population and individual level human exposures and their health risks, in retrospective studies and for forecasting. This talk will present advances in air pollution modeling that integrate multi-modal data and deep learning and estimate uncertainty in predictions which can then inform or be integrated into epidemiological analyses. Current challenges and future needs for advancing these models that are currently being pursued by our team for biopreparedness and health risk applications will also be discussed.