General methods and implementations of epidemic model inference on high-performance computing platforms.

目前，文献中存在随机流行病模型的参数推理算法，具体实现通常针对每种应用重新编码。正如sars - cov -2疫情所证明的那样，在最需要有效模型拟合的紧急情况下，这使得最先进的推断无法实现。本项目旨在将这些特定应用的方法整合到流行病模型推断的通用工具包中。因此，它将围绕随机状态转移模型发展理论，提供改进的基于mcmc的拟合方法，并找到可以反映在可持续高性能软件库中的底层过程模型的新抽象。

: Currently, parameter inference algorithms for stochastic epidemic models exist in the literature, with concrete implementations typically coded anew for each application. As demonstrated by theSARS-CoV-2 outbreak, this puts state-of-the-art inference out of reach of emergency situations when effective model fitting is required the most. This project seeks to bring together these application-specific methodologies into a general toolkit for epidemic model inference. As such, it will develop theory around stochastic state-transition models, provide improved MCMC-based fitting methods, and find novel abstractions of the underlying process models which can be reflected in a sustainable high-performance software library.