Uncertainty Quantification for Forecasting COVID-19 Dynamics
Investigator: Rose Yu
Affiliation: University of California, San Diego
A research team led by the University of California, San Diego, is developing a fast and accurate epidemic modeling tool that can automatically learn from data collected from online platforms and global simulations, and make real-time predictions.
The core technological development is uncertainty quantification (UQ) for graph neural networks (GNNs), a novel DL framework that can learn flexible models on graph-structured data with built-in confidence intervals. In particular, they are focused on developing quantile-regression techniques with physics-guided GNNs. The resulting method will lead to efficient probabilistic spatiotemporal forecasts for disease spreading such as COVID-19.
About NERSC and Berkeley Lab
The National Energy Research Scientific Computing Center (NERSC) is a U.S. Department of Energy Office of Science User Facility that serves as the primary high-performance computing center for scientific research sponsored by the Office of Science. Located at Lawrence Berkeley National Laboratory, the NERSC Center serves more than 7,000 scientists at national laboratories and universities researching a wide range of problems in combustion, climate modeling, fusion energy, materials science, physics, chemistry, computational biology, and other disciplines. Berkeley Lab is a DOE national laboratory located in Berkeley, California. It conducts unclassified scientific research and is managed by the University of California for the U.S. Department of Energy. »Learn more about computing sciences at Berkeley Lab.