General methods and implementations of epidemic model inference on high-performance computing platforms.
Lead Research Organisation:
Lancaster University
Department Name: Mathematics and Statistics
Abstract
: 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.
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.
Organisations
People |
ORCID iD |
Christopher Jewell (Primary Supervisor) | |
Alin Morariu (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/W523811/1 | 30/09/2021 | 29/09/2025 | |||
2644858 | Studentship | EP/W523811/1 | 01/01/2022 | 31/12/2025 | Alin Morariu |