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.

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

Fontanella Lara;Ippoliti Luigi;Valentini Pasquale
2018

Abstract

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.
9788891910233
File in questo prodotto:
File Dimensione Formato  
1322-3496-1-PB.pdf

accesso aperto

Tipologia: Documento in Pre-print
Dimensione 251.72 kB
Formato Adobe PDF
251.72 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11564/700475
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact