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  • Resources by chapter
    • Chapter 1: Why spatio-temporal epidemiology?
    • Chapter 2: Modelling health risks
    • Chapter 3: The importance of uncertainty
    • Chapter 4: Embracing uncertainty: the Bayesian approach
    • Chapter 5: The Bayesian approach in practice
    • Chapter 6: Strategies for modelling
    • Chapter 7: Is `real' data always quite so real?
    • Chapter 8: Spatial patterns in disease
    • Chapter 9: From points to fields: Modelling environmental hazards over space
    • Chapter 10: Why time also matters
    • Chapter 11: The interplay between space and time in exposure assessment
    • Chapter 12: Roadblocks on the way to causality: exposure pathways, aggregation and other sources of bias
    • Chapter 13: Better exposure measurements through better design
    • Chapter 14: New frontiers
  • Courses
    • Statistical methods in epidemiology
    • Spatio-temporal Methods in epidemiology
    • Advanced statistical modelling in space and time
    • BUC1 (CIMAT) When populations and hazards collide: modelling exposures
      and health
    • BUC2 (UNAM) Thinking Globally: The Role of Big Data
    • Detecting Pattens in Space and Time (CMM)
    • BUC4 (Bath) New Frontiers: Advanced Modelling in Space and Time
    • Big Data in Environmental Research
    • Statistics and Data Science in Research: unlocking the power of your data
    • Bayesian Hierarchical Models
    • BUCX (UNAM) Quantifying the Health Impacts of Air Pollution
    • Environmental Health Impact Assessment using R (IOM)
  • Computing resources
    • WinBUGS
    • INLA
    • EnviroStat
  • Book's webpage @ CRC

Spatio-Temporal Methods in Environmental Epidemiology

Spatio-Temporal Methods in Epidemiology

course outline

The following is an example of a structure for a course in spatio-temporal epidemiology. This follows the structure of
a thirteen week graduate level course that was given at the University of British Columbia in 2013 in which there were
two 1.5 hour lectures per week. The participants in that course were statistics graduate students and students of public
health who had a strong statistical background.

Reference is given to the material in the chapters in the book together with suggested times that might be dedicated to
that material.

Chapter
Sections
Suggested Timing
Chapter 1: Why spatio-temporal epidemiology?
‌
All
0.5 week plus
background reading
Chapter 2: Modelling health risks
‌
All, excluding
2.6 & 2.7
1 Week
Chapter 3: The importance of uncertainty
‌
3.1–3.4
inclusive
0.5 Week
Chapter 4: Embracing uncertainty: the Bayesian
approach
4.1–4.6
inclusive
2 Weeks
Chapter 5: The Bayesian approach in practice
‌
All
2 Weeks
Chapter 6: Strategies for modelling
‌
6.1–6.7
inclusive
1 Week
Chapter 7: Is `real' data always quite so real?
‌
7.1–7.3
inclusive
1 Week
Chapter 8: Spatial patterns in disease
‌
All excluding
8.2
1.5 Weeks
Chapter 9: From points to fields: Modelling
environmental hazards over space
9.1–9.14
inclusive
1.5 Weeks
Chapter 10: Why time also matters
‌
10.1–10.4
inclusive & 10.7
1 Week
Chapter 11: The interplay between space and time
in exposure assessment
11.1, 11.2,
11.3.1, 11.4
1 Week

PDF Course Outline

  • ©Gavin Shaddick and James V. Zidek 2015