Model: Johns Hopkins ID Dynamics COVID-19 Working Group - JHU_IDD-CovidSP

    Name: CovidScenarioPipeline
    Abbreviation: JHU_IDD-CovidSP
    Owner: covid19hub
    Team name: Johns Hopkins ID Dynamics COVID-19 Working Group
    Description: County-level metapopulation model with commuting and stochastic SEIR disease dynamics with social-distancing indicators.
    Contributors: Joseph C. Lemaitre (EPFL), Qifang Bi (Johns Hopkins Infectious Disease Dynamics), Juan Dent Hulse (Johns Hopkins Infectious Disease Dynamics), M. Kate Grabowski (Johns Hopkins Infectious Disease Dynamics), Kyra H. Grantz (Johns Hopkins Infectious Disease Dynamics), Joshua Kaminsky (Johns Hopkins Infectious Disease Dynamics), Stephen A. Lauer (Johns Hopkins Infectious Disease Dynamics), Elizabeth C. Lee (Johns Hopkins Infectious Disease Dynamics)<elizabeth.c.lee@jhu.edu>, Justin Lessler (Johns Hopkins Infectious Disease Dynamics)<justin@jhu.edu>, Hannah R. Meredith (Johns Hopkins Infectious Disease Dynamics), Javier Perez-Saez (Johns Hopkins Infectious Disease Dynamics), Shaun A. Truelove (Johns Hopkins Infectious Disease Dynamics), Lindsay T. Keegan (University of Utah), Kathryn Kaminsky, Sam Shah, Josh Wills, Pierre-Yves Aquilanti (Amazon Web Services), Karthik Raman (Amazon Web Services), Arun Subramaniyan (Amazon Web Services), Greg Thursam (Amazon Web Services), Anh Tran (Amazon Web Services)
    License: MIT
    Notes: primary
    Citation: https://www.medrxiv.org/content/10.1101/2020.06.11.20127894v1
    Methods: This is a county-level metapopulation model with stochastic SEIR disease dynamics. This model incorporates uncertainty in epidemiological parameters and the effectiveness of state-wide intervention policies on social distancing and stay-at-home orders. Infections are seeded stochastically with roughly 10 times the number of confirmed cases in the first five days of a county epidemic and R0 and the duration of the infectious period were drawn stochastically for each county and simulation. Disease follows SEIR dynamics and county commuting data modulates the spread of disease within counties. Stay-at-home orders and subsequent social distancing interventions are updated at the state-level according to recent policy documents. States that did not implement stay-at-home orders had a single social distancing intervention that began on the day of the first reported order and endured for the whole simulation period. Intervention effects are inferred where possible. To model deaths and hospitalizations in the population, we incorporated realistic but fixed time delays from infection to symptom onset to hospitalization to ICU to ventilator use to death and used age-specific demographics to perform a county-level age standardization of health outcome risk. Currently, we assume a 0.25% infection fatality rate and a 7:1 ratio of incident hospitalizations to deaths. For each county, we estimate the seeding time and amount, the baseline reproductive number, the case confirmation to infection ratio, and the effectiveness of interventions (from closing to phased reopenings). We use an MCMC-like inference procedure that calibrates model outputs to weekly county aggregations of incident confirmed cases and deaths as reported by USA Facts. For the inference of the baseline reproductive number and the case confirmation to infection ratio, hierarchical grouping terms enable sharing of strength among counties in the same state. Our procedure forecasts incident and cumulative deaths by incorporating the last reported data point (pre-forecast date) from USA Facts. The estimates reported by this model incorporate uncertainty in both epidemiological parameters (e.g., R0, infectious period, time delays to health outcomes) and the effectiveness of state-wide intervention policies on social distancing, stay-at-home orders, and phased reopenings. We submit a case-optimized calibration to forecast confirmed cases and hospitalizations and a deaths-optimized calibration to forecast deaths.
    Home: https://github.com/HopkinsIDD/COVIDScenarioPipeline
    Auxiliary data: Missing

    Forecasts (25)

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