A Hierarchical Bayesian Spatio-Temporal Model to Estimate the Short-term Effects of Air Pollution on Human Health

We introduce a hierarchical spatio-temporal regression model to study the spatial and temporal association existing between health data and air pollution. The model is developed for handling measurements belonging to the exponential family of distributions and allows the spatial and temporal components to be modelled conditionally independently via random variables for the (canonical) transformation of the measurements mean function. A temporal autoregressive convolution with spatially correlated and temporally white innovations is used to model the pollution data. This modelling strategy allows to predict pollution exposure for each district and afterwards these predictions are linked with the health outcomes through a spatial dynamic regression model.