AI for Public Health: Epidemic Forecasting Through a Data-Centric Lens

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. Toward realizing the potential of AI in public health, I addressed multiple challenges stemming from the domain such as data scarcity, distributional changes, and issues arising from real-time deployment to enable our support of CDC’s COVID-19 response. This talk will cover methods to address these challenges with novel deep learning architectures for real-time response to disease outbreaks and new techniques for end-to-end learning with mechanistic epidemiological models—based on differential equations and agent-based models—that bridge ML advances and traditional domain knowledge to leverage individual merits. I will conclude by discussing challenges and opportunities in public health for data and computer scientists.

Alexander Rodríguez is a PhD candidate in the College of Computing at Georgia Tech, advised by Prof. B. Aditya Prakash. His research is at the intersection of machine learning, time series, and scientific modeling, and his main application domains are public health and community resilience. He has published at top venues such as AAAI, NeurIPS, ICLR, KDD, WWW, AAMAS, PNAS and has organized workshops and tutorials at AAAI and KDD. His work won the best paper award at ICML AI4ABM 2022 and was awarded the 1st place in the Facebook/CMU COVID-19 Challenge and the 2nd place in the C3.ai COVID-19 Grand Challenge. He was also invited to the Heidelberg Laureate Forum in 2022, and named a ‘Rising Star in Data Science’ by the University of Chicago Data Science Institute in 2021 and a ‘Rising Star in ML & AI’ by the University of Southern California in 2022.