A Bayesian spatio-temporal statistical analysis of Out-of-Hospital Cardiac Arrests

Stefano Peluso, Antonietta Mira, Haavard Rue, Nicholas John Tierney, Claudio Benvenuti, Roberto Cianella, Maria Luce Caputo, Angelo Auricchio

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


We propose a Bayesian spatio-temporal statistical model for predicting Out-of-Hospital Cardiac Arrests (OHCA). Risk maps for Ticino, adjusted for demographic covariates, are built for explaining and forecasting the spatial distribution of OHCAs and their temporal dynamics. The occurrence intensity of the OHCA event in each area of interest, and the cardiac risk-based clustering of municipalities are efficiently estimated, through a statistical model that decomposes OHCA intensity into overall intensity, demographic fixed effects, spatially structured and unstructured random effects, time polynomial dependence and spatio-temporal random effect. In the studied geography, time evolution and dependence on demographic features are robust over different categories of OHCAs, but with variability in their spatial and spatio-temporal structure. Two main OHCA incidence-based clusters of municipalities are identified.
Original languageEnglish (US)
JournalBiometrical Journal
StatePublished - Feb 3 2020


Dive into the research topics of 'A Bayesian spatio-temporal statistical analysis of Out-of-Hospital Cardiac Arrests'. Together they form a unique fingerprint.

Cite this