Epidemic Forecasting on Networks: Bridging Local Samples with Global Outcomes
Epidemics of all kinds, from infectious diseases to technologies and ideas, spread through the hidden network of our social interactions. The structure of this underlying network determines the patterns of the epidemic spread, but mapping this network is expensive, and modeling it accurately is difficult.
In this talk, I will introduce a data-driven and model-free approach to predict the time evolution of epidemics that requires surprisingly few local network samples to forecast epidemic spread accurately. I will establish theoretical guarantees for the precision of our local estimator for a general class of networks, supporting these claims with concrete empirical evidence. The technical tools discussed in the talk can provide new perspectives on various applications of network data, beyond the scope of epidemics.
Yeganeh is a final-year Ph.D. candidate in Management Science and Engineering at Stanford University, where she is advised by Amin Saberi. During her PhD, she was also a research fellow at UC Berkeley's Simons Institute for the Theory of Computing. Her research focuses on analyzing large-scale networks and stochastic systems, employing tools from applied probability and algorithm design to address operations challenges.