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# Building Covid 19 tool to help forecast hospital beds

What a frilling week. Market turmoil, oil crashes, and Covid arrived to come kick our asses. I chose to not sit idly by as a spectator, and instead forge a weapon (web app) that will help hospitals around the world predict the wave IN THEIR AREA and tell them how many beds they need and if they will be swamped.

It needs to predict when the wave arrives, and how many of each type of beds will be needed. Standard, ICU, and Critical. Plus, as the data for Covid gets better, the user can change the inputs easily on the webpage.

I haven’t named it officially yet, maybe call it COBE Tool – COvid Bed Estimator Tool. I will put in in Shiny at shiny.io hosted.

Early results show what will happen if Covid19 burns without social distancing. 72% of America catches it.

Tools used : R, with packages Epimdr, Desolve, Shiny. The core code for generating the S E I R table is below.

``````require(deSolve)
times = seq(0, 450, by=1)
#paras = c(mu = 0, N = 1, beta = 1000, sigma = 365/15, gamma = 365/5)
sigma<- 1/5 # this is     The rate at which an exposed person becomes infective. Inverse of
gamma<-1/15 #  The rate at which an exposed person becomes resistant (recovers)
paras = c(mu = 0, N = 1, beta = .1173, sigma = sigma, gamma = gamma)
start = c(S=(.999) , E=0, I=.001, R = 0.0)
#start = c(S=0.06, E=0, I=0.001, R = 0.939)
out=ode(y=start, times=times, func=seirmod, parms=paras)
#init       <- c(S = 1-1e-6, I = 1e-6, R = 0.0)
#parameters <- c(beta = 1.4247, gamma = 0.14286)
#out <- ode(y = init, times = times, func = sir, parms = parameters)
out <- as.data.frame(out)