Optimal Control of SEIR Epidemic Models

Neetash Pataria


Supervised by Frank C Langbein; Moderated by Matthew J W Morgan

SEIR(S) models are often used to model the spread of infectious disease, e.g. COVID19. Optimal control and related reinforcement learning strategies can be used to determine policies to limit the spread. Such policies include testing strategies, control of people movement, vaccination, etc. Constraints on resources, e.g. screening capacity, may be incorporated in the control target. The aim of this project is to investigate such optimal control techniques and the effectiveness of the resulting policies. It may be split into multiple projects, exploring different control strategies, from traditional optimal control to reinforcement learning or focus on gathering and improving data or simulations to inform the models. You must have excellent mathematical (control theory, differential equations, graph theory) and programming (likely Python, Matlab; differential equation solvers, optimisation, machine learning, dynamical system simulators) skills. Discuss with me for details.

Final Report (06/11/2021) [Zip Archive]

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