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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 Rv
Computing resources
WinBUGS
INLA
EnviroStat
Book's webpage @ CRC