SBIR Phase I: Accelerating Understanding of COVID-19 Biology and Treatment Via Scaled Medical Record and Biosimulation Analytics

该小型企业创新研究（SBIR）项目的更广泛影响/商业潜力是通过快速整合描述病毒化学及其治疗方法的研究结果来满足COVID-19危机的信息需求。拟议的项目将在参与的医疗机构部署先进的计算方法，使患者记录立即可供研究，同时保持机构和患者的隐私。虽然最初的重点是改善COVID-19，但所提出的解决方案可以更广泛地应用于加速流行病学研究，提高科学知识和公共卫生，从而缩短时间，降低人员，计算能力和数据存储的成本。SBIR第一阶段项目旨在快速扩展和加速临床和计算数据的可访问性，以提高对COVID-19的理解。 拟议的创新将使用加密技术，特别是多方计算，以促进对COVID-19医疗记录的隐私保护跨机构查询。改进对PB级计算（模拟和模型）数据的访问将通过允许世界各地的研究人员探索数据来加速研究。 这项工作将调整和部署分散式计算技术，以便以可验证的方式在世界各地的计算机上分布式存储数PB的病毒分子动力学模拟数据，从而能够在数据位置进行数据分析。拟议的仪表板将允许对参与机构的组合数据集进行安全查询，以快速了解各种预先存在的疾病和药物对COVID-19的影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to address information needs of the COVID-19 crisis by rapidly integrating research findings describing the chemistry of the virus and its treatment. The proposed project will deploy advanced computational methods at participating medical institutions to make patient records immediately available for study while maintaining institutional and patient privacy. While the initial focus is on ameliorating COVID-19, the proposed solution can be applied more generally to accelerate epidemiological studies, improving scientific knowledge and public health with faster timelines and lowered costs for personnel, computing capabilities, and data storage. This SBIR Phase I project proposes to rapidly expand and accelerate the accessibility of clinical and computational data to improve understanding of COVID-19. The proposed innovation will use cryptographic techniques, notably multiparty computation, to facilitate privacy-preserving cross-institutional querying of COVID-19 medical records. Improved access to petabytes of computational (simulation and model) data will speed research by allowing researchers around the world to probe the data. The effort will adapt and deploy decentralized computation techniques to enable distributed storage of many petabytes of virus molecular dynamics simulation data across computers around the world, in a verifiable manner that enables data analysis at the data location. The proposed dashboard will allow for secure queries of a combined dataset of participating institutions to quickly yield insight about the effect of various pre-existing conditions and medications on COVID-19. The effort will include verification and validation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.