Automation and Collaboration in Complex Epidemiological Workflows with OSPREY
Argonne National Laboratory; University of Chicago; Sandia National Laboratories
Workshop Proceedings of the 54th International Conference on Parallel Processing, ICPP Workshops '25, pp. 159-166, 2025
Abstract
Complex epidemiological modeling requires integrating heterogeneous tools, data streams, and computational resources while preserving reproducibility and team coordination.
This paper presents OSPREY, a workflow-oriented framework for automating and coordinating end-to-end epidemiological analysis pipelines. OSPREY is designed to support collaboration across institutions and roles while reducing operational friction in iterative model studies.
The approach demonstrates how automation and structured orchestration can accelerate analysis cycles and improve reliability for computationally intensive public health workflows.
Key Contributions
Workflow Orchestration
Provides structured automation for multi-stage epidemiological pipelines spanning data ingestion, model execution, and post-processing.
Collaboration at Scale
Supports cross-team workflows so domain scientists, modelers, and computing experts can coordinate efficiently in shared analyses.
Reproducible Iteration
Improves reproducibility across repeated scenario runs and evolving model configurations through standardized pipeline execution.
HPC-Ready Design
Targets high-cost computational environments, enabling practical deployment for large-scale epidemiological studies and decision-support workflows.
Citation
BibTeX
@inproceedings{ozik2025osprey,
title={Automation and Collaboration in Complex Epidemiological Workflows with OSPREY},
author={Ozik, Jonathan and Collier, Nicholson and Fadikar, Arindam and Wozniak, Justin and Hayot-Sasson, Valerie and Conroy, Kyle and Chard, Kyle and Wentz, Jacqueline and Acquesta, Erin and Ray, Jaideep},
booktitle={Workshop Proceedings of the 54th International Conference on Parallel Processing},
pages={159--166},
year={2025},
publisher={ACM},
doi={10.1145/3750720.3757294}
}