Spatio-temporal Methods in Environmental Epidemiology

ABS Schools

The Applied Bayesian Statistics summer school has been running since 2004. It is organised by IMATI CNR Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Milano.

The 2025 school is organized in cooperation with University of Genova, Department of Mathematics

The school aims to present state-of-the-art Bayesian applications, inviting leading experts in their field.
Each year a different topic is chosen.

Past editions were devoted to: Bayesian Phylogenetics and Infectious Diseases, Bayesian Causal Inference, Gene Expression Genomics, Decision Modelling in Health Care, Spatial Data in Environmental and Health Sciences, Bayesian Methods and Econometrics, Bayesian Decision Problems in Biostatistics and Clinical Trials, Bayesian Methodology for Clustering, Classification and Categorical Data Analysis, Bayesian Machine Learning with Biomedical Applications, Hierarchical Modeling for Environmental Processes, Stochastic Modelling for Systems Biology, Bayesian Methods for Variable Selection with Applications to High-Dimensional Data and Applied Bayesian Nonparametrics, Modern Bayesian Methods and Computing for the Social Sciences, Bayes Big Data and the Internet, Modeling Spatial And Spatio-Temporal Data With Environmental Applications, Bayesian Statistical Modelling and Analysis in Sport, Bayesian Demography.

The lecturer is Prof. Alexandra Schmidt (McGill University, Department of Epidemiology, Biostatistics and Occupational Health, Canada).
The practical lectures will be given by Dr. Carlo Zaccardi (University G. d'Annunzio Chieti-Pescara, Department of Economics, Italy).

The topic chosen for the 2025 school is
Spatio-temporal Methods in Environmental Epidemiology.

This course aims to explore the interface between environmental epidemiology (EE) and spatio-temporal modeling (ST). The aim of EE is to understand the adverse health effects of environmental hazards and to estimate the risks associated with those hazards. Such risks have traditionally been assessed either over time at a fixed point in space or over space at a fixed point in time. ST modeling characterizes the distribution of those hazards and associated risks over both geographical locations and time.
Understanding variation and exploiting dependencies over both space and time greatly increases the power to assess those relationships.
The course will cover a wide range of topics from an introduction to spatio-temporal and epidemiological principles along with the foundations of ST modeling, with specific focus on their application, to new directions for research.

Topics will cover

• types of epidemiological studies: cohort, case–control, ecological
• measures of risk: relative risks, odds ratios, absolute risk, sources of bias, assessing uncertainty
• Bayesian statistics and computational techniques: Markov Chain Monte Carlo (MCMC)
• regression models in epidemiology: Logistic and Poisson generalized linear models, hierarchical models
• dynamic linear models, temporal autocorrelation
• spatial models: area and point referenced methods, mapping, geostatistical methods, spatial regression
• spatial-temporal models: separable models, non-separable models, modeling exposures in space and time.