AI 中文总结
该研究针对传染病管理中人工流程导致延迟的问题，提出结合软件技术与超级计算的完整软件栈，需支持HPC工作流、安全管理及FAIR原则，以提升大流行缓解能力。
AI 中文摘要
传染病仍然是人类社会的重大威胁。在最近的COVID-19大流行期间，数学建模和大规模计算机模拟被证明在支持公共卫生专家和决策者方面非常有效。尽管取得了这些进展，但现代建模方法和数字技术的全部潜力尚未得到实现。许多关键任务——包括专家咨询、模型执行、情景分析、报告准备和结果传达——仍然严重依赖人工驱动的流程，每次人工交互都会引入可避免的延迟，并限制疫情快速演变期间的响应能力。大流行防范应选择自动化工作流程和无缝集成的软件模块，这些模块可通过大幅缩短响应时间来提高大流行缓解能力。为此，我们需要强大且灵活的计算基础设施，能够支持异构硬件和不断演进的传染病模型。此外，数据源需要动态集成。管理这些需求需要支持自动化高性能计算（HPC）工作流程的基础设施。除了计算性能外，软件基础设施还必须确保安全的用户和数据管理，以符合数据保护法规，并向决策者和公众提供清晰、透明的结果展示。应对上述挑战需要将最先进的科学软件与现代可扩展基础设施紧密集成，该基础设施可在必要时利用超级计算资源。为了在未来的流行病或大流行场景中快速部署，遵守研究软件的FAIR原则对于确保可重用性和可持续性至关重要。
英文摘要
Infectious diseases remain a major threat to human societies. During the recent COVID-19 pandemic, mathematical modeling and extensive computer simulations proved highly effective in supporting public health experts and decision makers. Despite these advances, the full potential of modern modeling approaches and digital technologies has not yet been realized. Many critical tasks -- including expert consultations, model execution, scenario analyses, report preparation, and result communication -- still relied heavily on manual, human-driven processes with each manual interaction introducing avoidable delays and limiting responsiveness during rapidly evolving outbreaks. Pandemic preparedness should opt for automated workflows and seamlessly integrated software modules that can improve pandemic mitigation capabilities by substantially reducing response times. For this step, we require robust and flexible computational infrastructure capable of supporting heterogeneous hardware and continuously evolving infectious-disease models. In addition, data sources need to be dynamically integrated. Managing such demands needs infrastructure that supports automated high-performance computing (HPC) workflows. Beyond computational performance, software infrastructure must ensure secure user and data management to comply with data-protection regulations and provide clear, transparent presentation of results to both decision makers and the public. Meeting the aforementioned challenges requires tight integration of state-of-the-art scientific software with modern, scalable infrastructure that can leverage supercomputing resources when necessary. For rapid deployment in future epidemic or pandemic scenarios, adherence to the FAIR principles for research software is critical to ensure reusability and sustainability.
Comments32 pages, 17 figures
