Spatio-temporal Modelling (4.42)

This research Project investigates the best way to represent the spatial disparity of diseases, to enable decision makers the best access to localised spatial health data. The initial focus of the Project is on cancer, with the results then to be applied to other diseases.

Better management of disease will be achieved through the development and application of new ways of

  • small area disease mapping
  • modelling disease and potential risk factors
  • visualising the results of these models linking this new information on the spatial and temporal distribution of disease and associated risk factors with health service utilisation

These research results will be more easily communicated through the visualisation tool developed in Project 4.4.1.


In this quarter work has focussed on the inclusion of both individual and spatial-level frailties, allowing for greater insight into how much spatial inequalities relate to individual, as opposed to place-characteristics. This work has been accepted as a contributed talk at the Australian Statistics Conference in July 2014.

The impact of the choice of temporal scale, for instance using quarters versus years, in spatio-temporal models for individual disease outcomes, has been investigated with recommendations made on the optimal choice of spatial scale.

Using data from BreastScreen Queensland, research has been completed on spatio-temporal models of health service participation.. Unlike previous studies of health service participation, this work focuses on identifying regions with unusual temporal trends, such as lower screening rates, with implications for health service management.

Improving outcomes

Issues in Queensland cancer incidence and how modelling might improve outcomes (Susanna Cramb's PhD research)

The cost to our society of cancer is measured not only by the cost of providing health services, but also by the overall burden on society. To address this huge burden we must have reliable information about where the current and predicted areas of high cancer risk are together with, cancer risk factors and health service utilisation – a scenario that spatial technology and models are built to handle.

Project Participants

Research & Education   -   Curtin University   -   QUT   -   TICHR

Government   -   Dept Health WA   -   Cancer Council Queensland