Epidemic and pandemic preparedness with rapid outbreak response rely on timely, trustworthy evidence. Mathematical models are crucial for supporting timely and reliable evidence generation for public health decision-making with models spanning approaches from compartmental and metapopulation models to detailed agent-based simulations. Yet, the accompanying software ecosystem remains fragmented across model types, spatial resolutions, and computational targets, making models harder to compare, extend, and deploy at scale. Here we present MEmilio, a modular, high-performance framework for epidemic simulation that harmonizes the specification and execution of diverse dynamic epidemiological models within a unified and harmonized architecture. MEmilio couples an efficient C++ simulation core with coherent model descriptions and a user-friendly Python interface, enabling workflows that run on laptops as well as high-performance computing systems. Standardized representations of space, demography, and mobility support straightforward adaptations in resolution and population size, facilitating systematic inter-model comparisons and ensemble studies. The framework integrates readily with established tools for uncertainty quantification and parameter inference, supporting a broad range of applications from scenario exploration to calibration. Finally, strict software-engineering practices, including extensive unit and continuous integration testing, promote robustness and minimize the risk of errors as the framework evolves. By unifying implementations across modeling paradigms, MEmilio aims to lower barriers to reuse and generalize models, enable principled comparisons of implicit assumptions, and accelerate the development of novel approaches that strengthen modeling-based outbreak preparedness.

翻译：流行病与大规模传染病防控及快速疫情响应依赖于及时、可信的证据。数学模型对于为公共卫生决策提供及时可靠证据至关重要，这些模型涵盖从分室模型、元种群模型到详细基于个体模拟等多种方法。然而，相应的软件生态系统在模型类型、空间分辨率和计算目标方面仍然存在碎片化，使得模型更难以比较、扩展和大规模部署。本文提出MEmilio——一个用于流行病模拟的模块化高性能框架，该框架在统一协调的架构内整合了多种动态流行病学模型的规范描述与执行过程。MEmilio将高效的C++仿真内核与连贯的模型描述及用户友好的Python接口相结合，支持从笔记本电脑到高性能计算系统的全流程工作。空间结构、人口统计学特征与流动模式的标准化表征，使得模型能够便捷地适应不同分辨率与人口规模，从而促进系统化的模型间比较与集成研究。该框架可无缝整合成熟的不确定性量化与参数推断工具，支持从情景推演到参数校准的广泛应用。最后，严格的软件工程实践（包括全面的单元测试与持续集成测试）保障了框架的稳健性，并在演进过程中最大程度降低错误风险。通过统一不同建模范式的实现方式，MEmilio旨在降低模型复用与泛化的门槛，实现对隐含假设的规范化比较，并加速新型方法的开发，从而加强基于建模的疫情应对能力建设。