Tuesday June 3
Health-exposure models, examples of spatio-temporal epidemiological analyzes; introduction to modeling health risks and impacts: types of epidemiological studies; measures of risk; relative risk; odds ratio; standardized mortality ratios; GLMs; Poisson models for count data
Exchangeability; Bayes' theorem; conjugate priors; predictions; approaches to Bayesian computation, Markov chain Monte Carlo methods, INLA
Causal Mediation Analysis with Spatial Interference Chiara di Maria
Spatio-temporal Modeling of Obesity Rates in Italy: A Bayesian Perspective Luciano Rota
The Effect of Multi-Pollutant Exposure on the Risk of Small for Gestational Age Births: A Spatial Analysis in Milan Giulio Beltramin
Wednesday June 4
Stationarity; Isotropy; Variograms; Gaussian processes; Bayesian Kriging
Non-normal outcomes; Examples in Stan and R-INLA
Moran's Statistics; Conditional Autoregressive models; Besag, York and Mollié (BYM); BYM2; Model comparison
Thursday June 5
Introduction; random walk, trend, seasonal and dynamic regression. Bayesian Inference for DLMs
Friday June 6
Separable models, non-separable models, DLMs for space and time
Zero-inflated Markov-switching models for infectious diseases. Heavy-tailed spatio-temporal processes
IMPORTANT NOTE:
It is important to have your own PC for the practical lessons. Remember to take it with you before leaving. Please install the following software on your PC in advance to start your lessons smoothly:
- R (>= 4.0)
REFERENCES:
1. Shaddick, G., Zidek, J.V., & Schmidt, A.M. (2023). Spatio–Temporal Methods in Environmental Epidemiology with R (2nd ed.). Chapman and Hall/CRC (DOI: 10.1201/9781003352655).