AI & Multi-agent Systems for Data-centric Epidemic Forecasting

Epidemic forecasting is a crucial tool for public health decision making and planning. There is, however, a limited understanding of how epidemics spread, largely due to other complex dynamics, most notably social and pathogen dynamics. 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. In this regard, my work brings a data-centric perspective to public health via methodological advances in AI at the intersection of time series analysis, spatiotemporal mining, scientific ML, and multi-agent systems. This extended abstract focuses on our new techniques for end-to-end learning with mechanistic epidemiological models-based on differential equations and agentbased models-that bridge ML advances and traditional domain knowledge to leverage individual merits. I finalize discussing some future directions for my work. CCS CONCEPTS • Computing methodologies → Machine learning; Agent / discrete models; • Applied computing → Epidemiology.