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    • 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

Quantifying the Health Impacts of Air Pollution

course outline

The following are the online resources for the course entitled 'Quantifying the Health Impacts of Air Pollution', which is
part of a series of workshops, organised and funded as a collaboration between University of Bath, UNAM and CIMAT.
These workshops have been termed the BUC series. More details can be found here.

This course will be delivered at the Universidad Nacional Autónoma de México (UNAM) in México City, México between
7th-10th August 2017.

The slides, problem sheets, solutions and other relevant resources can be found below.

Slides

Day 1

Day 2

Day 3


Lab Sessions

Day 1

Day 2

Day 3


Data

Day 1

Day 2

Day 3


Required R Packages

foreign

spdep

shapefiles

CARBayes

rgdal


Other Resources

How to install R and RStudio

  • ©Gavin Shaddick and James V. Zidek 2015