Model: University of Chicago - UChicago-CovidIL_60

    Name: CovidIL_60
    Abbreviation: UChicago-CovidIL_60
    Owner: covid19hub
    Team name: University of Chicago
    Description: This is a compartmental, age-structured SEIR model that infers past SARS-CoV-2 transmission rates and forecasts mortality under current and hypothetical public health interventions.
    Contributors: Sarah Cobey <cobey@uchicago.edu>, Philip Arevalo, Ed Baskerville, Spencer Carran, Katelyn Gostic, Laugen McGough, Sylvia Ranjeva, Frank Wen
    License: Creative Commons Attribution Non-Commercial 4.0
    Notes: other
    Citation: Missing
    Methods: The model is age-structured to capture both age-specific contact patterns and age-specific differences in the biological dynamics, such as the probability of asymptomatic infection. Each model compartment is sub-divided into nine different age categories. To model the state of Illinois, we divide the state into 3 regions based on similarities in epidemic dynamics. We infer a common statewide pre-intervention transmission rate, and a separate post-intervention transmission rate for each of the three regions. We also infer a region-specific initial number of infected individuals. Other model parameters are informed by Illinois-specific data or best estimates from existing literature. We fit the model to incident in-hospital deaths and confirmed COVID-19 ICU cases prior to May 8, 2020. For inference, we use maximum likelihood inference via sequential Monte Carlo for partially observed Markov process models (POMP). We assume that hospitalized deaths are reported with probability 0.8075. Because we do not know what might cause underreporting of non-hospitalized deaths, our simulations integrate over that uncertainty by assuming that the reporting rate for non-hospitalized deaths is uniformly distributed from 0.25 to 0.75. The model can be adapted to investigate different types of interventions. We currently model shelter-in-place as a reduction in the overall transmission rate, which is shared by all infected individuals, and reductions in age- and location-specific contact rates. To model the relaxing of shelter-in-place interventions, we project four different scenarios. 1) The inferred post-intervention transmission rate remains unchanged. We assume that this is maximum reduction in transmission and call this model CovidIL_100. At our current estimates, this corresponds to a 78% reduction in pre-intervention transmission. 2) Transmission rates increase to 80% of the maximum reduction in transmission according to a logistic function centered on May 29. We call this model CovidIL_80. At our current estimates, this corresopnds to a 74% reduction in pre-intervention transmission. 3) Transmission rates increase to 60% of the maximum reduction in transmission according to a logistic function centered on May 29. We call this model CovidIL_60. At our current estimates, this corresopnds to a 69% reduction in pre-intervention transmission. 4) Transmission rates increase to 40% of the maximum reduction in transmission according to a logistic function centered on May 29. We call this model CovidIL_40. At our current estimates, this corresopnds to a 65% reduction in pre-intervention transmission.
    Home: https://github.com/cobeylab/covid_IL
    Auxiliary data: Missing

    Forecasts (2)

    Timezero Data Source Upload Time Issued at Version
    2020-05-05 2020-05-05-UChicago-CovidIL_60.csv 2020-05-13 17:06:26 UTC 2020-05-13 12:00:00 UTC
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    2020-05-11 2020-05-11-UChicago-CovidIL_60.csv 2020-05-13 17:06:30 UTC 2020-05-13 12:00:00 UTC
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