team_name: UNIPG_UNIMIB_USI_UNINSUBRIA
model_name: MULTINOMIAL_BAYESIAN
model_abbr: UpgUmibUsi-MultiBayes
model_contributors:
  - name: Francesco Bartolucci
    affiliation: Università di Perugia
    email: francesco.bartolucci@unipg.it
  - name: Fulvia Pennoni
    affiliation: Università di Milano Bicocca
    email: fulvia.pennoni@unimib.it
  - name: Antonietta Mira
    affiliation: Università della Svizzera italiana and Università dell’Insubria
    email: antonietta.mira@usi.ch
website_url: https://github.com/francescobartolucci/ARMultinomial
license: cc-by-4.0
team_model_designation: primary
methods: Bayesian Dirichlet-Multinomial models for counts of patients in mutually
  exclusive and exhaustive categories such as hospitalized in regular wards and in
  intensive care units, deceased and recovered
methods_long: We us a Bayesian Dirichlet-Multinomial autoregressive models for time-series
  of counts of patients in mutually exclusive and exhaustive observational categories,
  defined according to the severity of the patient status and the required treatment.
  Categories include hospitalized in regular wards (H) and in intensive care units
  (ICU), together with deceased (D) and recovered (R). These models explicitly formulate
  assumptions on the transition probabilities between these categories across time,
  thanks to a flexible formulation based on parameters that a-priori follow Normal
  distributions, possibly truncated to incorporate specific hypotheses having an epidemiological
  interpretation. The posterior distribution of model parameters and the transition
  matrices are estimated by an efficient Markov Chain Monte Carlo algorithm that also
  provides predictions and allows us to compute the reproduction number Rt. All estimates
  and predictions are endowed with an accuracy measure obtained thanks to the Bayesian
  approach.