The Research Fellow in Epidemic Analytics and Surveillance System Design will undertake a three-year integrated program of research in the mathematical sciences to study the design of infectious disease surveillance systems, with the aim of improving the public health utility of these systems. The appointee will be responsible for developing a simulation platform that couples mathematical models of disease transmission with models of surveillance (observation) processes. They will also develop a framework for evaluating the value of data generated by different surveillance system designs, drawing on methods from the field of information theory optimal design. Using the simulation platform and evaluation framework, the role of data from a range of surveillance methodologies (e.g., mass testing, sentinel GP surveillance, serological surveys, infection prevalence surveys, hospital-based surveillance) in generating evidence for public health decision-making (e.g., forecasting, intervention scenario modelling, estimating vaccine effectiveness) will be investigated.

We are looking for an early career researcher with a PhD, preferably in Infectious Disease Modelling, with a strong understanding of statistics and modelling using computing software such as R, Python, MATLAB, or C/C++. Candidates will ideally have experience working with public health data and an understanding of how public health systems observe cases of disease and be able to develop models to represent these processes.

The successful candidate will join the Infectious Disease Dynamics Unit at the Melbourne School of Population and Global Health (University of Melbourne).