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COVID-19 had an unprecedented impact on scientific collaboration. Thepandemic and its broad response from the scientific community has forged newrelationships among domain experts, mathematical modelers, and scientificcomputing specialists. Computationally, however, it also revealed critical gapsin the ability of researchers to exploit advanced computing systems. Thesechallenging areas include gaining access to scalable computing systems, portingmodels and workflows to new systems, sharing data of varying sizes, andproducing results that can be reproduced and validated by others. Informed byour team's work in supporting public health decision makers during the COVID-19pandemic and by the identified capability gaps in applying high-performancecomputing (HPC) to the modeling of complex social systems, we present thegoals, requirements, and initial implementation of OSPREY, an open scienceplatform for robust epidemic analysis. The prototype implementationdemonstrates an integrated, algorithm-driven HPC workflow architecture,coordinating tasks across federated HPC resources, with robust, secure andautomated access to each of the resources. We demonstrate scalable andfault-tolerant task execution, an asynchronous API to support fasttime-to-solution algorithms, an inclusive, multi-language approach, andefficient wide-area data management. The example OSPREY code is made availableon a public repository.

Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis