The development of novel antivirals against influenza virus is an important strategy to prepare for future pandemics. Mathematical modelling can provide insights into the effectiveness of new antivirals, and maximise the information gained from experiments in the preclinical stage. For example, mathematical models have the potential to extrapolate infection dynamics from in vitro (cell culture) to in vivo (animal/human clinical) studies. In the within-host context, mathematical models can also predict the effects of different drug regimes (such as timing of receiving treatment); on the other hand, at the population level, mathematical models can predict how treatment-induced reduction in viral load in the infected person leads to reduced onward transmission. However, many of these models have not been verified for specific viruses and antivirals.

The PhD candidate will be based at the School of Mathematics and Statistics at The University of Melbourne, supervised by Dr Ada Yan and Dr Pengxing Cao. The project will develop mathematical models for influenza infection within the host, incorporating the effects of antiviral treatment. The candidate will have the opportunity to analyse the behaviour of these models, and infer model parameters using Bayesian statistics. Data for existing antivirals and/or antivirals under development will be generated by collaborators at the WHO Centre for Reference and Research on Influenza. While the exact aims of the research will depend on the candidate’s interests, potential questions to be answered include:

• What models are required to extrapolate antiviral effectiveness from cell culture experiments to animals/humans? How accurate is such extrapolation?
• How do different treatment timings and doses affect the effectiveness of the antiviral drug?
• Can we predict how antiviral drugs reduce onward transmission, through their suppression of viral load?

We welcome applicants with a background in mathematics/statistics, or other related disciplines such as epidemiology, public health, bioinformatics, physics or computer science. The candidate must have a strong interest in working in an interdisciplinary setting, as this is essential to tailoring mathematical models to the biology of influenza infection. Computer programming experience is an advantage.

More information on the PhD program can be found here: https://study.unimelb.edu.au/find/courses/graduate/doctor-of-philosophy-science/ Funding for tuition fees and a stipend for general living costs are available. Domestic and international applicants are welcome.

Interested applicants should send their CV to Dr. Ada Yan and Dr. Pengxing Cao at ayan1@unimelb.edu.au and pengxing.cao@unimelb.edu.au.