Model: US Army Engineer Research and Development Center - USACE-ERDC_SEIR

    Name: ERDC_SEIR
    Abbreviation: USACE-ERDC_SEIR
    Owner: covid19hub
    Team name: US Army Engineer Research and Development Center
    Description: The ERDC SEIR model makes predictions of several variables (e.g., reported new/cumulative cases per day, etc.). Model parameters are estimated using historical data using Bayesian inference.
    Contributors: Michael L. Mayo <Michael.L.Mayo@erdc.dren.mil>, Michael A. Rowland <Michael.A.Rowland@erdc.dren.mil>, Matthew D. Parno <Matthew.D.Parno@erdc.dren.mil>, Ian D. Detwiller <Ian.D.Dettwiller@erdc.dren.mil>, Matthew W. Farthing <Matthew.W.Farthing@erdc.dren.mil>, William P. England <William.P.England@erdc.dren.mil>, Glover E. George <glover.e.george@erdc.dren.mil>
    License: Other License
    Notes: primary
    Citation: Missing
    Methods: The ERDC SEIR model is a process-based model that mathematically describes the virus dynamics in a population center (e.g., state, CBSA) using assumptions that are common in compartmental models: (i) modeled populations are large enough that fluctuations in the disease states grow slower than averages (i.e., coefficient of variation < 1) (ii) recovered individuals are neither infectious nor become susceptible to further infection. The model is similar to classic SEIR models but with additional compartments for unreported infections and isolated individuals. Model parameters describing reporting rates and transmission rates are allowed to vary in time over over the historical record as social and policy factors change. Forecasts into the future, however, are made under the assumption that the parameters are held constant in time. The model parameters are estimated from observations of the cumulative number of cases using a Bayesian approach. Information from subject matter experts (SMEs) is used to develop a prior probability distribution over the model parameters. This then combined with a statistical model of the model-data mismatch to produce a posterior probability distribution over the model parameters that incorporates both the cumulative observations and subject matter expertise. The parameters that maximize the posterior probability density are used to make forecasts into the future. The effective reproduction number is computed using the next-generation matrix method. Deaths are estimated from this model through a procedure which leverages state-level data on recoveries and fatalities.
    Home: https://github.com/erdc-cv19/seir-model
    Auxiliary data: Missing

    Forecasts (36)

    Timezero Data Source Upload Time
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    2020-05-14 2020-05-14-USACE-ERDC_SEIR.csv 2020-05-25 19:57:34 EDT
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