CovidNearTerm is a bootstrap-based method based on an autoregressive model to estimate at the county level the expected number of COVID-19 patients that will hospitalized 2-4 weeks into the future. It is based on the work of researchers at UCSF (Adam Olshen), Stanford (Kristopher Kapphahn, Ariadna Garcia, Isabel Wang and Manisha Desai) and Memorial Sloan-Kettering (Mithat Gonen).
Our projections are based on the call simulateAR(vec,wsize=14,method="unweighted",pdays=28,nsim=10000,seed=12345,output_type="predictions",lambda=seq(0,1,0.05)) where
vec-hospitalization data times series\ wsize-the number of days going into the kernel\ method-the type of kernel\ pdays-the number of days in the future to make predictions\ nsim-the number of simulations\ seed-the random seed\ output_type-the format of the output\ lambda-the choices for the shrinkage parameters
A detailed explanation of the methdology is coming soon.