Model: Los Alamos National Labs - LANL-GrowthRate

    Name: GrowthRate
    Abbreviation: LANL-GrowthRate
    Owner: epiforecasts
    Team name: Los Alamos National Labs
    Description: This model makes predictions about the future, unconditional on particular intervention strategies. Statistical dynamical growth model accounting for population susceptibility.
    Contributors: Dave Osthus <dosthus@lanl.gov>, Sara Del Valle, Carrie Manore, Brian Weaver, Lauren Castro, Courtney Shelley, Manhong (Mandy) Smith, Julie Spencer, Geoffrey Fairchild, Travis Pitts, Dax Gerts, Lori Dauelsberg, Ashlynn Daughton, Morgan Gorris, Beth Hornbein, Daniel Israel, Nidhi Parikh, Deborah Shutt, Amanda Ziemann
    License: Other License
    Notes: primary
    Citation: Missing
    Methods: This model makes predictions about the future, unconditional on particular intervention strategies. The model consists of two processes. The first process is a statistical model of how the number of COVID-19 infections changes over time. The second process maps the number of infections to the reported data. We model the growth of new cases as the product of a dynamic growth parameter and the underlying numbers of susceptible and infected cases in the population at the previous time step, scaled by the size of the state's starting susceptible population. Change 2020-10-29: The growth parameter can be thought of as the transmissibility of the virus in that state on that date and is a weighted regression between the trend in the growth rate over the past 42 days and a growth rate that would keep the number of new daily confirmed cases constant. The weights of these two components are dynamically tuned to the observed data. To model new deaths in the population, we assume that a fraction of the 1,2,3,4, or 5-week moving average of the daily confirmed cases will die. The model learns both the moving average window and the case fatality fraction that best fits the historical observations.
    Home: https://covid-19.bsvgateway.org/
    Auxiliary data: Missing

    Forecasts (33)

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