team_name: Los Alamos National Labs
model_name: GrowthRate
model_abbr: LANL-GrowthRate
model_contributors: Dave Osthus , Sara Del Valle, Carrie Manore, Brian
Weaver, Lauren Castro, Courtney Shelley, Manhong (Mandy) Smith, Julie Spencer, Geoffrey
Fairchild, Travis Pitts, Dax Gerts, Lori Dauelsberg, Ashlynn Daughton, Morgan
Gorris, Beth Hornbein, Daniel Israel, Nidhi Parikh, Deborah Shutt, 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."