NSF RESUME HPC Workshop: High-Performance Computing and Large-Scale Data Management in Service of Epidemiological Modeling

TL;DR

This paper reports on a workshop that brought together experts to identify how high-performance computing and large-scale data management can enhance epidemiological modeling for better pandemic prevention and public health decision-making.

Contribution

It provides a shared vision and identifies key HPC capabilities and workflows needed to advance computational epidemiology and pandemic response strategies.

Findings

Identified HPC workflows crucial for epidemiological modeling

Explored data integration techniques for large-scale epidemiological data

Recommended best practices from other domains for HPC in epidemiology

Abstract

The NSF-funded Robust Epidemic Surveillance and Modeling (RESUME) project successfully convened a workshop entitled "High-performance computing and large-scale data management in service of epidemiological modeling" at the University of Chicago on May 1-2, 2023. This was part of a series of workshops designed to foster sustainable and interdisciplinary co-design for predictive intelligence and pandemic prevention. The event brought together 31 experts in epidemiological modeling, high-performance computing (HPC), HPC workflows, and large-scale data management to develop a shared vision for capabilities needed for computational epidemiology to better support pandemic prevention. Through the workshop, participants identified key areas in which HPC capabilities could be used to improve epidemiological modeling, particularly in supporting public health decision-making, with an emphasis on HPC workflows, data integration, and HPC access. The workshop explored nascent HPC workflow and large-scale data management approaches currently in use for epidemiological modeling and sought to draw from approaches used in other domains to determine which practices could be best adapted for use in epidemiological modeling. This report documents the key findings and takeaways from the workshop.