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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)
    • BUC2 (UNAM)
    • Detecting Pattens in Space and Time (CMM)
    • BUC4 (University of Bath)
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    • Statistics and Data Science in Research: unlocking the power of your data
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Spatio-Temporal Methods in Environmental Epidemiology

When populations and hazards collide:
modelling exposures and health risks

course outline

The following are the online resources for the course entitled 'When populations and hazards collide: modelling exposures and
health risks'.

This course was given at Centro de Investigación de Matematicas (CIMAT) in Guanajuato, México between 12th-14th
November 2015.

This course was designed for graduates/students who have an interest in spatio-temporal methods and how they might be
applied in epidemiological analyses.

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

Slides

Course Slides

Lab Class Slides


Lab Sessions

Lab Sheet

Lab Sheet Solutions

Lab Sheet (WinBUGS)

Lab Sheet (WinBUGS) Solutions


Data

Data Package (.zip)


Required R Packages

maps

geoR

MASS

SpatialEpi

XQUARTZ

  • ©Gavin Shaddick and James V. Zidek 2015
  • 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
  • Statistical methods in epidemiology
  • Spatio-temporal Methods in epidemiology
  • Advanced statistical modelling in space and time
  • BUC1 (CIMAT)
  • BUC2 (UNAM)
  • Detecting Pattens in Space and Time (CMM)
  • BUC4 (University of Bath)
  • Big Data in Environmental Research
  • Statistics and Data Science in Research: unlocking the power of your data
  • Bayesian Hierarchical Models
  • WinBUGS
  • INLA
  • EnviroStat