The project focuses on the development of computational and data-driven methods for epidemiological modelling and analysis. A central component is the development of stochastic models and Bayesian methods for combining epidemiological models with different types of data, with the aim of enabling reliable estimates and situational awareness. The project offers several possible research directions, including statistical validation and diagnostics for simulation-based inference, the use of neural networks to construct informative prior distributions, and game-theoretic modelling of epidemics and the value of information in decisions concerning public health interventions. The common foundation is computational modelling and Bayesian inference, while the specific research direction can be adapted to the doctoral candidate’s background and interests. The project thus offers opportunities to work at the intersection of applied mathematics, statistics, machine learning and computational science, with epidemiological applications providing a common focus.

More details about the project can be found here: https://stefanengblom.github.io/files/docs/Comput_Modeling_Epidemics.pdf