team_name: UNIPV Periscope Working Group
model_name: Bayesian Time-varying INGARCH with NPI covariates
model_abbr: UNIPV-BayesINGARCHX
model_contributors:
  - name: Paolo Giudici
    affiliation:  University of Pavia, Department of Economics and Management
    email: giudici@unipv.it
  - name: Barbara Tarantino
    affiliation:  University of Pavia, Department of Economics and Management
    email: barbara.tarantino@unipv.it
website_url: https://periscopeproject.eu/home
license: cc-by-4.0
team_model_designation: primary
methods: Bayesian estimation of time-dependent models with time-varying coefficients
  to predict COVID-19 positive counts.
team_funding: Pan European Response to the ImpactS of COvid-19 and future Pandemics
  and Epidemics (PERISCOPE)
data_inputs: JHU (confirmed cases), OxCGRT (NPI covariates)
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_long: 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.