Automation and Collaboration in Complex Epidemiological Workflows with OSPREY

Jonathan Ozik, Nicholson Collier, Arindam Fadikar, Justin Wozniak, Valerie Hayot-Sasson, Kyle Conroy, Kyle Chard, Jacqueline Wentz, Erin Acquesta, and Jaideep Ray

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

ACM PDF

OSPREY workflow automation for epidemiological pipelines — **Figure:** OSPREY supports automation, orchestration, and collaborative execution of epidemiological workflows.

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.

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}
}