The Univeristy of Melbourne The Royal Melbourne Hopspital

A joint venture between The University of Melbourne and The Royal Melbourne Hospital


Research Projects

Project: Sample size determination for within-host animal studies of infection

McVernon Group

Within-host animal studies are routinely used to determine the efficacy of a treatment for clearing a bacterial infection in a host. Determining adequate sample sizes for powering these studies is often complicated, given the various dynamics at play which are often not well understood. Instead, in designing an experiment, we are typically limited by the amount of resources we have available, and attempt to allocate these resources in the “best” way, to “learn” the most about the within-host dynamics.

In this project, we will explore the use of an approximate, likelihood-free inference method with simulations from the model to evaluate the optimal experimental designs. These tools will subsequently be applied to these within-host experimental studies, to derive the optimal allocation of resources based on different experimental aims.

This project will be best suited to someone with strong coding skills, and studying a degree in mathematics or statistics eg MSc (Mathematics and Statistics), in applied mathematics, stochastic processes or statistics

Contact project supervisor for further
information and application enquiries

Project Supervisor

Dr David Price

Project availability
Master of Biomedical Science

McVernon Group

[email protected]

1 vacancies

Viral Infectious Diseases
Cross Cutting Disciplines

Professor Jodie McVernon is a physician with subspecialty qualifications in public health and vaccinology. She has extensive expertise in clinical vaccine trials, epidemiologic studies and mathematical modelling of infectious diseases, gained at the University of Oxford, Health Protection Agency London and the University of Melbourne. Her work focuses on the application of a range of cross-disciplinary methodological approaches, including mathematical and computational models, to synthesise insights from basic biology, epidemiological data and sociological research. These models advance understanding of the observed epidemiology of infectious diseases and inform understanding of optimal interventions for disease control.