Education
Doctor of Philosophy (in progress) [2022 — current]
Adelaide University (formerly The University of Adelaide)
My PhD looked at modelling what happens once a virus enters a host and how this translates to outbreak dynamics. Within-host models seek to explain the dynamics of infectious agents inside an individual’s body, while between-host models explore transmission between individuals. Compared to between-host modelling, our understanding of within-host dynamics and methods for working with them is considerably less developed. This is in part due to the massive number of cells involved as stochastic simulations at this scale are computationally expensive, so simpler deterministic approximations are typically used instead. These tend to be piecewise linear models that obscure much of the underlying biology. The first paper from my PhD addresses this by incorporating stochasticity into a large population model without requiring full stochastic simulations. This improves computational efficiency while preserving the biological realism of within-host dynamics through a system of ODEs.
Papers arising from my PhD:
- Dylan J. Morris, Lauren Kennedy, Andrew J. Black, 2025. Bayesian inference for disease transmission models informed by viral dynamics. arXiv: arXiv:2604.20069v1.
- Dylan J. Morris, Lauren Kennedy, Andrew J. Black, 2025. Random time-shift approximation enables hierarchical Bayesian inference of mechanistic within-host viral dynamics models on large datasets. PLoS Computational Biology 20(12): e1013775. 10.1371/journal.pcbi.1013775.
- Dylan Morris, John Maclean and Andrew J. Black, 2024. Computation of random time-shift distributions for stochastic population models. Journal of Mathematical Biology, 89, 33, 10.1007/s00285-024-02132-6.
Master of Philosophy (completed) [2019 — 2021]
The University of Adelaide
I completed my M.Phil in 2021 with my thesis:
- Dylan J. Morris. Inference on historical Ebola outbreaks using hierarchical models: a particle filtering approach (2021). MPhil Thesis. hdl.handle.net/2440/132642
This work looked at how we can model outbreaks of Ebola efficiently by using importance sampling in particle filters. This methodology allows realisations of a continuous-time Markov chain to be simulated that are consistent with two time-series of (partial) observations of the outbreak. Ebola served as a great case study since it is a disease where it’s relatively easy to link when someone becomes infectious and subsequently dies (or recovers).
Bachelor of Mathematical Sciences (completed) [2016 — 2018]
The University of Adelaide
Majors:
- Applied Mathematics
- Statistics