Epidemiological Modelling

We apply cutting-edge research methodologies to project the burden of disease across time, space and demographic groups, including predicting the likely future trajectory of diseases.

Our Unit has been built on communicable disease modelling, with major research themes currently including tuberculosis, HIV and emerging infections (including Ebola). However, we are actively looking to expand our activities in non-communicable and demographic modelling. Specific methodologies employed by the Unit include calculus, linear algebra, software engineering, health economics, statistics, epidemiology and Bayesian inference.

Within the School, we are closely linked to Biostatistics, CCRET and Infectious Diseases Epidemiology, with extensive overlap in both the methodology and content of the research of these Units. Active external collaborations include James Cook University, The Peter Doherty Institute for Infection and Immunity, the Alfred Hospital, Melbourne Sexual Health Centre, The Global Fund to Fight AIDS, Tuberculosis and Malaria, the NHMRC Centre of Research Excellence in Tuberculosis Control, the TB Modelling and Analysis Consortium (TB-MAC), the Burnet Institute and RMIT. The Unit works actively as part of the 'AuTuMN' collaboration to provide modelling reports for high-burden countries to combat their TB epidemic, and has worked with Fiji, the Philippines, Bulgaria, Papua New Guinea, Uzbekistan and Bhutan to date.

Head of Unit

Dr James Trauer

Contact: James.Trauer@monash.edu

Our research

Our overall aim is to develop epidemiological models to relate non-communicable and communicable disease risk factors to disease onset and its subsequent course and impact. The research is intended to inform:

  • patient understanding of the relationship between modifiable risk factors and disease outcome
  • clinical practice, and in particular clinicians' understanding of this relationship and their ability to use this understanding in helping their patients to modify their lifestyles and associated risk factors
  • health policy development, both by assisting health policy makers to devise optimal health promotion and disease prevention strategies and by providing lobby and advocate groups and interested individuals with relevant, evidence-based information on risk factors and disease outcomes
  • disease forecasting methods, for both communicable and non-communicable diseases
  • translation and incorporation of cutting-edge data analytical methods into decision support tools

Associated staff

Research staff

PhD students

  • Yayehirad Alemu Melsew
  • Neal Smith
  • Richard Ofori-Asenso