Model: JHU_UNC_GAS - JHU_UNC_GAS-StatMechPool

    Name: StatMechPool
    Abbreviation: JHU_UNC_GAS-StatMechPool
    Owner: covid19hub
    Team name: JHU_UNC_GAS
    Description: This model jointly trains over locations, fitting to both mechanistic and statistical models. The statistical model enforces similar places having similar mechanistic coefficients.
    Contributors: Justin Lessler (JHU) <justin@jhu.edu>, Jess Edwards (UNC) <jkedwar@email.unc.edu>, Keya Joshi (JHU/Harvard), Claire Smith (JHU), Paul Zivich (JHU) , Marc Coram (Google) <mcoram@google.com>, Ellen Klein (Google), Michael Brenner (Harvard/Google), Lusann Yang (Google), Anton Geraschenko (Google), Stephan Hoyer (Google), Dmitri Kochkov (Google), Jamie Smith (Google)
    License: Apache license 2.0
    Notes: primary
    Citation: Missing
    Methods: The model uses a detailed list of demographic parameters for US Counties, with demographic parameters taken from https://github.com/Yu-Group/covid19-severity-prediction/ and other variables (weather, mobility, etc) taken from the Opencovid Data Repo. The prediction rollouts from the model are generated from the mechanistic model, where the allowed range of mechanistic parameters is fit on historical data for coverage. To arrive at the best model, we experimented with a large range of mechanistic and statistical models and competed these models against each other. For mechanistic models, we tried SEIR models and curve fitting models. For the statistical model, we tried linear model and a simple multilayer perceptron. We experimented with statistical models enforcing constraints among different numbers of the mechanistic parameters. We also tried models with priors and without. We also varied how much of the trajectories are fitted with the model (last 4 week, 6 week, and so forth). Including all combinatorial choices, we ended up with 450 different models. We evaluated model performance with split dates trained in the past and chose the best models. The best models ended up being (i) a multiplicative growth model for the mechanistic term with a linear statistical model in which the statistical model enforces constraints on all mechanistic parameters. (ii) A curve fitting mode [CITE paper] with a pseudolikelihood prior, together with a linear statistical model on all mechanistic parameters. The predictions here ensemble these two models.
    Home: https://github.com/HopkinsIDD/EpiForecastStatMech
    Auxiliary data: https://github.com/HopkinsIDD/EpiForecastStatMech

    Forecasts (56)

    Timezero Data Source Upload Time Issued at Version
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