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.

翻译：美国国家科学基金会资助的鲁棒性流行病监测与建模（RESUME）项目于2023年5月1日至2日在芝加哥大学成功举办了题为"服务于流行病学建模的高性能计算与大规模数据管理"的研讨会。这是旨在为预测智能与流行病预防培育可持续跨学科协同设计的一系列研讨会之一。本次会议汇集了流行病学建模、高性能计算（HPC）、HPC工作流及大规模数据管理领域的31位专家，共同构建计算流行病学更好支持流行病预防所需能力的共同愿景。通过研讨会，参会者确定了HPC能力可用于改进流行病学建模的关键领域，特别是在支持公共卫生决策方面，重点聚焦于HPC工作流、数据集成与HPC访问。研讨会探讨了当前应用于流行病学建模的新兴HPC工作流与大规模数据管理方法，并试图借鉴其他领域的使用实践，以确定哪些方法可最佳适配流行病学建模。本报告记录了研讨会的主要发现与经验总结。