CEPH-Rtrend_covid.yml

team_name: "CEPH Lab at Indiana University"
team_abbr: "CEPH"
model_name: "Rtrend COVID"
model_abbr: "Rtrend_covid"
model_contributors: [
  {
    "name": "Marco Ajelli",
    "affiliation": "Indiana University Bloomington",
    "email": "majelli@iu.edu",
  },
  {
    "name": "Paulo C. Ventura",
    "affiliation": "Indiana University Bloomington",
    "email": "pventura@iu.edu",
  },
  {
    "name": "Maria Litvinova",
    "affiliation": "Indiana University Bloomington",
    "email": "malitv@iu.edu",
  },
  {
    "name": "Allisandra G. Kummer",
    "affiliation": "Indiana University Bloomington",
    "email": "alkummer@iu.edu",
  },
  {
    "name": "Alessandro Vespignani",
    "affiliation": "Northeastern University",
    "email": "a.vespignani@northeastern.edu",
  }, 
]
license: "CC-BY-4.0"
designated_model: true
data_inputs: "Weekly incident COVID-19 hospitalizations from the National Healthcare Safety Network (NHSN)"
methods: "A renewal equation method based on Bayesian estimation of Rt from hospitalization data."
methods_long: "Model forecasts are obtained by using a renewal equation based on the estimated net reproduction number Rt. We apply a lowpass filter to the time series of weekly hospitalizations, then interpolate it to daily resolution. We then use MCMC Metropolis-Hastings sampling to estimate the posterior distribution of Rt based on the filtered data, considering an informed prior on Rt based on COVID-19 literature. The estimated Rt in the last weeks of available data is used to forecast Rt in the upcoming weeks, with a drift term proportional to the current incidence. Finally, we use the renewal equation with the posterior distribution and trend of the estimated Rt in the most recent weeks of hospitalization data."
ensemble_of_models: false
ensemble_of_hub_models: false
website_url: https://publichealth.indiana.edu/research/faculty-directory/profile.html?user=majelli
designated_github_users: ["paulocv"]