Biostatistics: Spatial-temporal Models in Epidemiology

Gavin Shaddick, Department of Mathematical Sciences, University of Bath, UK

Jim Zidek, Professor Emeritus of Statistics, University of British Columbia

One-Day Short Course, 25 May 2014, 9:00am room MS 3163, Medical Sciences building, University of Toronto

Pre-workshop information

Software installation instructions


This course provides an introduction to epidemiological analysis where data has structure both over space and time. The course covers basic concepts of epidemiology, methods for temporal and spatial analysis and the practical application of such methods using commonly available computer packages. It will have an applied focus with both lectures and practical “hands-on” computer session in which participants will be guided through analyses of epidemiological data.


  • Introduction to epidemiology
  • Spatio-temporal methods and models
  • Further topics: time-series studies, measurement error, ecological bias, monitoring designs, Bayesian analysis, disease mapping
  • Computational implementation: R, WinBUGS, INLA

Teaching methods & Course format:

  • A mixture of lectures, worked examples and computer-based practicals.
  • Delegates will need to bring a laptop to the course with R, R-INLA and WinBUGS software installed (details of how to access these materials will be provided on registration)

Learning outcomes:

  • An understanding of the basic principles of multilevel modelling
  • An understanding of the need to account for spatial and temporal correlation in epidemiological analyses
  • Knowledge of how to apply methods for spatial and temporal modelling using real data sets.
  • An appreciation of advanced aspects of spatial-temporal modelling in epidemiology

Target audience:

Postgraduate students, postdoctoral researchers, individuals working in industry or government in the field of epidemiology or statistics. Those who have found a need for implementing spatial methods in their research / work.

Prequisite knowledge:

Participants will be expected to have a basic understanding and experience of applying and interpreting multiple regression models. Familiarity with the R software will be assumed.

Full description