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.
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