Capturing the impact of patient variability in a novel cancer treatment using Bayesian inference

Using an ordinary differential equation model, the effects of an oncolytic virus as a cancer treatment can be modelled, however, this type of model doesn’t account for the potential
result a patient experiences. Using Bayesian inference, estimates for the parameters in the ode model can be made that would allow us to capture the predispositions a patient may
have causing adverse effects on tumour size and provides the potential to personalise treatment based on these factors and parameters.

Mikaela Westlake

Queensland University of Technology

Mikaela Westlake is an undergradute student at Queensland University of Technology studying a Bachelor of Applied and Computational Mathematics and Medical Engineering. Her research interests include how mathematics can be used to improve our understanding of diseases such as cancer and multiple sclerosis and how this information can shape engineering solutions for patients. Mikaela is enjoying research and hopes to pursue this in her Honours year and potentially after.

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