metadata-JHU_IDD-CovidSP.txt

team_name: Johns Hopkins ID Dynamics COVID-19 Working Group
model_name: CovidScenarioPipeline
model_abbr: JHU_IDD-CovidSP
model_contributors: Erica Carcelen (Johns Hopkins Infectious Disease Dynamics), Alison Hill (Johns Hopkins Infectious Disease Dynamics), Sung-mok Jung (University of North Carolina), Joseph C. Lemaitre (University of North Carolina), Joshua Kaminsky (Johns Hopkins Infectious Disease Dynamics), Elizabeth C. Lee (Johns Hopkins Infectious Disease Dynamics), Justin Lessler (Johns Hopkins Infectious Disease Dynamics & University of North Carolina), Sara Loo (Johns Hopkins Infectious Disease Dynamics), Clif McKee (Johns Hopkins Infectious Disease Dynamics), Koji Sato (Johns Hopkins Infectious Disease Dynamics), Claire Smith (Johns Hopkins Infectious Disease Dynamics), Shaun Truelove (Johns Hopkins Infectious Disease Dynamics) <shauntruelove@jhu.edu>
website_url: https://github.com/HopkinsIDD/COVIDScenarioPipeline
license: mit
team_model_designation: primary
ensemble_of_hub_models: false
methods: State-level single strain metapopulation model with commuting and stochastic SEIR disease dynamics
  with social-distancing and vaccination with parameter adjustments for multiple strains.
team_funding: State of California and US Government
data_inputs: JHU CSSE (confirmed cases; reported fatalities), US Census (population; mobility)
citation: https://doi.org/10.1038/s41598-021-86811-0
methods_long: "This is a state-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,\
  \ stay-at-home orders, and vaccine effects that reduce suspectibility and severity.\
  \ Infections are seeded stochastically with roughly 10\
  \ times the number of confirmed cases in the first five days of the 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. \nStay-at-home orders and \
  \ subsequent social distancing interventions are updated at the state-level according\
  \ to recent policy documents. Vaccination rates and reductions to the hospitalization\
  \ and mortality rates are state-specific by week. Ongoing NPIs continued through the remainder \
  \ of the simulation period, while vaccination coverage continues to increase slowly in\
  \ subsequent months until it reaches a maximum 90% coverage per group. Intervention effects are inferred\
  \ where  possible.\nTo 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 0.5% infection fatality rate and a 3.5:1 ratio of incident hospitalizations\
  \ to deaths. The model assumes that vaccinated individuals have both reductions in susceptibility\
  \ and reductions in hospitalization and mortality. For the two-dose vaccines, one dose has\
  \ an assumed 75% reduction and two doses have a 95% reduction. The one dose J&J vaccine is assumed
  \ to have 70% efficacy.  \nFor 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.\n\
  Our procedure forecasts incident and cumulative deaths by incorporating the last\
  \ reported data point (pre-forecast date) from USA Facts.\nThe estimates reported\
  \ by this model incorporate uncertainty in both epidemiological parameters (e.g.,\
  \ R0, nationwide spread of more transmissible variants, 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 single calibration to forecast deaths, cases,\
  \ and hospitalizations."