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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
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    • Spatio-temporal Methods in epidemiology
    • Advanced statistical modelling in space and time
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    • BUC2 (UNAM)
    • Detecting Pattens in Space and Time (CMM)
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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

Thinking Globally: The Role of Big Data

course outline

TThe following are the online resources for the course entitled 'Thinking Globally: The Role of Big Data', 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 was delivered at the Universidad Nacional Autónoma de México (UNAM) in México City, México between
23rd-24th February 2015.

This course is 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 Sessions

Lab Sheet

Lab Sheet Solutions


Data

Data.zip


Required R Packages

INLA

CARBayes

spdep

shapefiles

rgdal

gcmr

maptools

classInt

lattice

sp

Matrix

  • ©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