Model: Center for Disease Dynamics, Economics & Policy - CDDEP-SEIR_MCMC

    Name: SEIR_Markov_Chain_Monte_Carlo
    Abbreviation: CDDEP-SEIR_MCMC
    Owner: covid19hub
    Team name: Center for Disease Dynamics, Economics & Policy
    Description: County-level SEIR model forecasts of incident (weekly) cases number by fitting predicted cases to the observations.
    Contributors: Eili Klein < klein@cddep.org>, Gary Lin < glin15@jhu.edu>, Yupeng Yang <yang@cddep.org>
    License: Creative Commons Attribution 4.0
    Notes: primary
    Citation: Missing
    Methods: We break down the SEIR model into 7 compartments: Susceptible individuals (S), Exposed individuals (E), Contagious individuals who are asymptomatic or mildly symptomatic (C), Infected with moderate or non-severe symptoms (I_N), Infected with severe symptoms (I_S), Recovered (R) and Deaths (D). We fit our model using the Markov Chain Monte Carlo (MCMC) sampling method in each county to estimate the parameters set (alpha, beta, mu, h, gamma, theta, delta...). We start with the initial parameters set from other studies and iterate the set in the model by calculating and comparing the likelihood values between the real cases and projected cases, finally we get our best projected curve (with 95% confidence interval) which has minimum sum of squared residuals (SSR).
    Home: https://cddep.org/
    Auxiliary data: Missing

    Forecasts (7)

    Timezero Data Source Upload Time
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    2020-07-05 2020-07-05-CDDEP-SEIR_MCMC.csv 2020-07-15 17:21:30 EDT
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