Model: UNIPV Periscope Working Group - UNIPV-BayesINGARCHX

    Name: Bayesian Time-varying INGARCH with NPI covariates
    Abbreviation: UNIPV-BayesINGARCHX
    Owner: epiforecasts
    Team name: UNIPV Periscope Working Group
    Description: Bayesian estimation of time-dependent models with time-varying coefficients to predict COVID-19 positive counts.
    Contributors: Paolo Giudici (University of Pavia) giudici@unipv.it, Barbara Tarantino (University of Pavia) barbara.tarantino@unipv.it.
    License: Creative Commons license family
    Notes: primary
    Citation: Giudici, P., Tarantino, B., A Bayesian time-dependent framework to assess the effectiveness of policy measures on COVID-19 counts. Working paper 2021.
    Methods: Our model accounts for uncertainty via a Bayesian framework, for time-dependence on past COVID-19 counts via an INGARCH structure and non-linearity via time-varying coefficients. In addition, time-lagged NPI covariates have been coded and incorporated into the Bayesian framework to assess whether policy measures can effectively reduce positive counts.
    Home: https://periscopeproject.eu/home
    Auxiliary data: Missing

    Forecasts (37)

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