team_name: Los Alamos National Labs
model_name: GrowthRate
model_abbr: LANL-GrowthRate
model_contributors:
  - name: Dave Osthus
    email: dosthus@lanl.gov
  - name: Sara Del Valle
  - name: Carrie Manore
  - name: Brian Weaver
  - name: Lauren Castro
  - name: Courtney Shelley
  - name: Manhong (Mandy) Smith
  - name: Julie Spencer
  - name: Geoffrey Fairchild
  - name: Travis Pitts
  - name: Dax Gerts
  - name: Lori Dauelsberg
  - name: Ashlynn Daughton
  - name: Morgan
  - name: Gorris
  - name: Beth Hornbein
  - name: Daniel Israel
  - name: Nidhi Parikh
  - name: Deborah Shutt
  - name: Amanda Ziemann
website_url: https://covid-19.bsvgateway.org/
license: other
team_model_designation: primary
methods: This model makes predictions about the future, unconditional on particular
  intervention strategies. Statistical dynamical growth model accounting for population
  susceptibility.
team_funding: U.S. Department of Energy
data_inputs: JHU (confirmed cases; reported fatalities), population
methods_long: 'This model makes predictions about the future, unconditional on particular  intervention
  strategies. The model consists of two processes. The first process  is a statistical
  model of how the  number of COVID-19 infections changes over time. The second process
  maps the number of  infections to the reported data. We model the growth of new
  cases as the product of a  dynamic growth parameter and the underlying numbers of
  susceptible and infected cases  in the population at the previous time step, scaled
  by the size of the state''s starting  susceptible population. Change 2020-10-29:
  The growth parameter can be thought of as the transmissibility of the virus in that
  state on that date and is a weighted regression between the   trend in the growth
  rate over the past 42 days and a growth rate that would keep  the number of new
  daily confirmed cases constant. The weights of these two components are dynamically
  tuned to the observed data. To model new deaths in the population, we assume that
  a fraction of the 1,2,3,4, or 5-week moving average of the daily  confirmed cases
  will die. The model learns both the moving average window and the case fatality
  fraction that best fits the historical observations.'