MSE Research Project Database

Modelling the spread and control of infectious diseases in complex populations


Project Leader: Nic Geard
Student: Thiri Vino
Collaborators: James McCaw (Mathematics & Statistics); Mark Davies, Steve Tong, Jodie McVernon (Doherty Institute); Martin Tomko (Infrastructure Engineering)
Sponsors: NHMRC, ARC
Primary Contact: Nic Geard (nicholas.geard@unimelb.edu.au)
Keywords: agent based systems; complex systems; computational biology; health and bioinformatics
Disciplines: Computing and Information Systems
Domains: Convergence of engineering and IT with the life sciences, Networks and data in society

Infectious diseases such as flu, measles and Ebola impose a significant health and economic burden upon individuals and societies. Mathematical and computational modelling are increasingly used to understand how these diseases spread through populations, and to inform decisions about their control.

Traditional compartmental approaches to modelling infectious diseases, while powerful, often require simplifying assumptions about the characteristics of both pathogens and populations. However, reality is complex: pathogens with multiple strains present a challenge for vaccine design, and optimal use of healthcare resources requires interventions tailored to the characteristics of specific populations. Agent-based and network models can enable us to capture important aspects of pathogen and population complexity.

This project involves the development and application of computer simulation models that incorporate more complex population and pathogen structure to one of a range of disease scenarios, including:

  • Effects of demographic change on infectious disease dynamics in developed and developing countries
  • Optimal control of skin pathogens in remote Australian indigenous communities
  • Antental immunisation as a strategy to protect very young infants against respiratory infection
  • Impacts of population mobility on disease transmission, surveillance and control
  • Implications of population structure for the dynamics and control of multi-strain pathogens
  • Interactions between population structure and the emergence of drug resistance

Projects can involve design, implementation and application of models, integration of novel data sources (eg mobility data, pathogen genome sequence data), new approaches to learn model parameters from data, and application of techniques from AI to explore model-based decision support.

Projects on these topics may also be suitable for 75pt MSc (Computing) projects.

Further information: http://prism.edu.au