Bridging AI, Data, and Epidemiological Models
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Meeting ID: 982 3814 7278
With the increasing availability of real-time multimodal data, a new opportunity has emerged for capturing previously unobservable facets of the spatiotemporal dynamics of epidemics. Epidemic forecasting is a crucial tool for public health decision making and planning. However, our comprehension of how epidemics spread remains limited, primarily due to the intricate interplay of various dynamics, particularly social and pathogen-related complexities. In this talk I will present our research at the intersection of time series analysis, spatiotemporal data mining, scientific ML, and multi-agent systems to enable the integration of data, representation learning, and theoretical knowledge from mechanistic epidemiological models.