Dynamics and Control of Infectious Diseases in Structured Populations

Project description

Traditional models of infectious disease tranmission make the assumption that populations are composed of homogeneous individuals who interact in a well-mixed fashion.  This is clearly not a realistic assumption: individuals in a population can vary in age, behaviour, health status, geographic location, and numerous other characteristics.  Population heterogeneity can affect how infectious diseases spread and how this spread can be most effectively controlled.  Further, many questions of relevance to health policy concern the assessment and control of risk among defined subpopulations.

This project involves the development and application of computer simulation models that incorporate more complex population structure (eg, agent/individual-based models, metapopulation models, network models) to 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

 

Project team

Leader: Nic Geard

Students: Thiri Vino

Collaborators: James McCaw (Mathematics & Statistics) Jodie McVernon (Doherty Institute)

Sponsors: NHMRC, ARC

Other projects

Convergence of engineering and IT with the life sciences projects

Networks and data in society projects

Disciplines

Computing and Information Systems

Domains

Convergence of engineering and IT with the life sciences, Networks and data in society

Keywords

agent based systems; complex systems; computational biology; health and bioinformatics