Outline

1. Introduction to spatial areal data
   1. Applications: infant mortality in the UK, lung cancer in Mexico
   2. Observed and expected counts
   3. Spatial autoregressions and BYM
2. Fitting and interpreting the BYM model
   1. Bayesian inference and INLA
   2. The bym function in the diseasemapping package
   3. Understanding and visualizing results
   4. Prior distributions
3. Simple(r) spatio-temporal models
   1. Additive spatial and temporal models
   2. Independent space-time effects
   3. Prior distributions
4. Space-time interaction
   1. Separable spatio-temporal processes
   2. . . . and specifying them in INLA
5. Bonus material: beyond the BYM model
   1. Area-level data as spatial censoring
   2. The aggregated log-Gaussian Cox process
   3. The EMS algorithm

Background material

1. Bayesian inference
2. Poisson regression

Software

1. R
2. The diseasemapping package on CRAN
3. INLA from r-inla.org