metadata-JHU_IDD-CovidSP.txt

team_name: Johns Hopkins ID Dynamics COVID-19 Working Group
model_name: CovidScenarioPipeline
model_abbr: JHU_IDD-CovidSP
model_version: "1.0"
model_contributors: Joseph C. Lemaitre (UNC), Joshua Kaminsky (Johns Hopkins Infectious Disease Dynamics), Claire P. Smith (Johns Hopkins Infectious Disease Dynamics), Sara Loo (Johns Hopkins Infectious Disease Dynamics), Clif McKee (Johns Hopkins Infectious Disease Dynamics), Alison Hill (Johns Hopkins Infectious Disease Dynamics), Sung-mok Jung (UNC), Erica Carcelen (Johns Hopkins Infectious Disease Dynamics), Koji Sato (Johns Hopkins Infectious Disease Dynamics), Elizabeth C. Lee (Johns Hopkins Infectious Disease Dynamics), Justin Lessler (UNC), Shaun Truelove (Johns Hopkins Infectious Disease Dynamics) <shauntruelove@jhu.edu>
website_url: https://github.com/HopkinsIDD/COVIDScenarioPipeline
license: mit
methods: State-level metapopulation model with commuting and stochastic SEIR disease dynamics
  with social-distancing indicators.
modeling_NPI: "Stay-at-home orders and 
  \ subsequent social distancing interventions are updated at the state-level according \
  \ to recent policy documents. Ongoing interventions continued through \
  \ the remainder of the simulation period. Intervention effects are inferred \
  \ where  possible."
compliance_NPI: Not applicable
contact_tracing: Not applicable
testing: Not applicable
vaccine_efficacy_transmission: "Vaccine efficacy is assumed to be against infection, not specifically \
  \ transmission. Vaccine efficacy estimates are assumed based on the scenarios."
vaccine_efficacy_delay: Immunity is assumed to develop exactly 14 days after vaccination.
vaccine_hesitancy: Hesitancy is not assumed other than specifically indicated by the scenarios.
vaccine_immunity_duration: Not applicable
natural_immunity_duration: Not applicable
case_fatality_rate: Not applicable
infection_fatality_rate: Age-adjusted based on population age distribution and age-distribution of those 
  \ who have received at least one vaccine dose. US national CFR set at 5%.
asymptomatics: Not applicable
age_groups: Not applicable
importations: Functionality in place, but not currently in use.
confidence_interval_method: We compute confidence intervals as quantiles across 300 simulations.
calibration: "We use an  MCMC-like inference procedure that calibrates model outputs to weekly \       
  \ 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."
spatial_structure: state-level metapopulation accounting for spatial mobility using community data between states.
citation: https://www.medrxiv.org/content/10.1101/2020.06.11.20127894v1
methods_long: "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. \nStay-at-home orders and \
  \ subsequent social distancing interventions are updated at the state-level according \
  \ to recent policy documents. Ongoing interventions continued through \
  \ the remainder of the simulation period. Intervention effects are inferred \
  \ where possible.\n
  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 0.5% infection fatality rate and a 3.5:1 ratio of incident hospitalizations\
  \ to deaths. Infection fatality rates and hospitalization rates are adjusted to\
  \ account for the age distribution of individuals who have received at least one
  \ dose of the vaccine. Weekly adjustments to R0 are made to account for the growing\
  \ prevalence of the B.1.1.7 variant, which is assumed to be 50% more transmissible\
  \ and to reach 100% dominance in the US by May.\n 
  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.\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, 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."