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

目前，文献中存在用于随机流行病模型的参数推断算法，具体实现通常针对每个应用重新编码。正如SARS-CoV-2疫情所表明的那样，在最需要有效的模型拟合的紧急情况下，这使得最先进的推断变得遥不可及。该项目试图将这些特定于应用的方法结合成一个通用工具包，用于流行病模型推断。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.