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

: Currently, parameter inference algorithms for stochastic epidemic models exist in the literature, with concrete implementations typically coded anew for each application. As demonstrated by the
SARS-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.