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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
Computing Resources - R-INLA
Useful Links
R-INLA Homepage
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) 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)
WinBUGS
INLA
EnviroStat