Model: Columbia_UNC - Columbia_UNC-SurvCon

    Name: Survival-Convolution Models
    Abbreviation: Columbia_UNC-SurvCon
    Owner: covid19hub
    Team name: Columbia_UNC
    Description: A survival-convolution model with piece-wise transmission rates that incorporates latent incubation period and provides time-varying effective reproductive number.
    Contributors: Yuanjia Wang <yw2016@cumc.columbia.edu>, Donglin Zeng <dzeng@email.unc.edu>, Qinxia Wang <qw2223@cumc.columbia.edu>, Shanghong Xie <sx2168@cumc.columbia.edu>
    License: Apache license 2.0
    Notes: primary
    Citation: https://www.frontiersin.org/articles/10.3389/fpubh.2020.00325/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Public_Health&id=561170
    Methods: We predict COVID-19 daily new cases using a robust and parsimonious survival-convolution model. We account for transmission during the pre-symptomatic incubation period, use a time-varying effective reproductive number (Rt) to reflect the temporal trend of transmission and change in response to a public health intervention, and quantify uncertainty by permutation. We use a regression model to predict cumulative deaths from estimated time-lagged cumulative total number of COVID-19 cases.
    Home: https://github.com/COVID19BIOSTAT/covid19_prediction
    Auxiliary data: Missing

    Forecasts (32)

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