Project title: Using mathematical and statistical modelling to develop efficient disease surveillance systems

Infectious disease outbreaks represent a substantial threat to public health. Monitoring trends in the number of new infections (known as infection incidence) over time is essential for assessing the effectiveness of intervention strategies and making informed predictions of future epidemic activity. However, the underlying infection incidence can not be directly measured — to do so would require that all infections are identified, and the timing of infections known. Instead, infection incidence and other key epidemiological and biological quantities that drive infection dynamics, must be inferred from epidemic surveillance data. A diverse set of infectious disease surveillance systems are/have been implemented, but the design of these systems is highly nontrivial and requires a multitude of decisions to be made regarding design parameters such as sample sizes and timing. In this project, we aim to leverage mathematical modelling to make significant contributions to the design of efficient disease surveillance systems.

This PhD project will develop new statistical approaches and surveillance strategies for inferring epidemic and biological quantities, including trends in infection incidence. The student will take an iterative approach: (1) proposing new surveillance strategies and simulating the data that systems would collect during an outbreak; and (2) developing statistical models for best inferring epidemic and biological quantities from the simulated data. Through this iterative process, the student will develop their skills in statistical modelling, computer simulation, and infectious disease epidemiology. The focus and direction of the project will depend on the interests and strengths of the student, and can be adjusted in collaboration with the supervisory team.

The PhD student will be a member of Melbourne Mathematical Biology based in the School of Mathematics and Statistics, and the Infectious Disease Dynamics Unit at the Centre for Epidemiology and Biostatistics at the University of Melbourne (Parkville Campus). The project will be supervised by Dr Oliver Eales, A/Prof Freya Shearer, and Dr Richard Creswell.

Essential criteria:

• Meets the University of Melbourne’s entry requirements for a Doctor of Philosophy (PhD) in Science. (https://study.unimelb.edu.au/find/courses/graduate/doctor-of-philosophy-science/entry-requirements/)
• A master's degree or four-year bachelor's degree with first-class honours in mathematics, statistics, biostatistics, biology, epidemiology, physics, computer science, or equivalent STEM subject area with demonstrated quantitative skills.
• Some experience in programming (e.g., R or python).
• Able to meet the University of Melbourne’s Graduate English Language requirements. (https://study.unimelb.edu.au/how-to-apply/english-language-requirements/graduate-english-language-requirements)

Desirable criteria

• Demonstrated experience with statistical modelling and Bayesian inference.
• Demonstrated experience in the mathematical modelling of infectious diseases, or related methods (e.g. dynamical systems, stochastic processes, computational methods).

How to apply

Interested candidates who satisfy the essential criteria should apply by emailing {oliver.eales@unimelb.edu.au} with a single pdf file attached that contains the following:

• A maximum one page cover letter that outlines your interest in your project, your skills and experience, and how you meet the selection criteria.
• A formal transcript of your academic record from your university that details the subjects taken, the grades you received, and what the grading scheme means if it is not obvious. Transcripts should be translated into English, if applicable. You should include a transcript for all relevant degrees (i.e., if you have both a bachelors and a masters degree, include a transcript for both).
• A brief CV.

Applications will be assessed on an ongoing basis until 16 August 2025, with shortlisted candidates invited for a brief discussion over Zoom.

The successful applicant will apply for admission and a scholarship through the regular University of Melbourne graduate research admission and scholarship round supported by the supervision team, in September 2025. If the applicant were successful, the scholarship would cover tuition and a living allowance of AU$38,500 per annum.