We propose statistical autoregressive models to analyze the observed time series of count data referred to different categories. The main assumption is that observed frequencies correspond to margins of a sequence of unobserved contingency tables. Inference is based on a Bayesian approach and a suitable Markov chain Monte Carlo (MCMC) algorithm. We apply the proposal to Italian COVID-19 data (at national level and for Lombardy) considering different categories of patients further to susceptible individuals and deaths.

Pennoni, F., Bartolucci, F., Mira, A. (2021). A multivariate statistical approach to predict COVID-19 count data with epidemiological interpretation and uncertainty quantification. In Bayesian Methods for Biomedical Research (pp.1-26).

A multivariate statistical approach to predict COVID-19 count data with epidemiological interpretation and uncertainty quantification

Pennoni F.;
2021

Abstract

We propose statistical autoregressive models to analyze the observed time series of count data referred to different categories. The main assumption is that observed frequencies correspond to margins of a sequence of unobserved contingency tables. Inference is based on a Bayesian approach and a suitable Markov chain Monte Carlo (MCMC) algorithm. We apply the proposal to Italian COVID-19 data (at national level and for Lombardy) considering different categories of patients further to susceptible individuals and deaths.
abstract + slide
data augmented MCMC algorithm, diffuse prior distribution, Dirichlet multinomial distribution, Multinomial distribution, posterior predictive p-values
English
World Meeting of the International Society for Bayesian Analysis
2021
Bayesian Methods for Biomedical Research
2021
1
26
https://events.stat.uconn.edu/ISBA2021/programs.html
open
Pennoni, F., Bartolucci, F., Mira, A. (2021). A multivariate statistical approach to predict COVID-19 count data with epidemiological interpretation and uncertainty quantification. In Bayesian Methods for Biomedical Research (pp.1-26).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/323155
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