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Baltimore_20200405.R
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Baltimore_20200405.R
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## Setup ####
require(socialmixr)
require(magrittr)
require(stringr)
require(reshape2)
require(dplyr)
require(ggplot2)
require(truncnorm)
source("asyptomatic_age.R")
age.limits <- c(0,5,10,15,20,25,35,45,55,60,65,75,85,90)
prop_symptomatic <- c(0.141, 0.106, 0.074, 0.184, 0.293, 0.387, 0.438,
0.535, 0.693, 0.816, 0.765, 0.749, 0.535, 0.535)
delta.t <- 1/1
time <- seq(1,300,by = delta.t)
t_March19 <- as.numeric(as.Date("2020-03-19") - as.Date("2020-03-01"))
t_May14 <- as.numeric(as.Date("2020-05-14") - as.Date("2020-03-01"))
nsim <- 500
start_index <- seq(1, nsim*length(time)+1, by = length(time))
all_prelim_info <- setup_seir_model(stoch = TRUE,
R0 = 2,
c_scale_vec = 1,
prop_symptomatic = prop_symptomatic,
sd.dw = 0.1)
Ncomp = all_prelim_info$Ncomp
ICs = all_prelim_info$ICs
params = list(C = all_prelim_info$C,
W = all_prelim_info$W,
beta0 = all_prelim_info$beta0,
beta1 = all_prelim_info$beta1,
phase = all_prelim_info$phase,
mu = all_prelim_info$mu,
v = all_prelim_info$v,
N=all_prelim_info$N,
gamma=all_prelim_info$gamma,
sigma = all_prelim_info$sigma,
prop_symptomatic=all_prelim_info$prop_symptomatic,
sd.dw = all_prelim_info$sd.dw)
cnames.allsim <- c('run_index', 'time',
paste0("S", 1:Ncomp),
paste0("E", 1:Ncomp),
paste0("A", 1:Ncomp),
paste0("I", 1:Ncomp),
paste0("R", 1:Ncomp),
paste0("incid_A", 1:Ncomp),
paste0("incid_I", 1:Ncomp),
"R0")
## Scenario 1A: R0 1.5 - 2.5, uncontrolled ####
all_sim <- matrix(NA,1,(Ncomp*7)+3)
colnames(all_sim) <- cnames.allsim
for(n in 1:nsim){
R0vec <- runif(length(time), min = 1.5, max = 2.5)
tmp <- sair_step_variableR0(stoch = TRUE, stoch.init = TRUE,
R0vec, Ncomp, ICs, params, time, delta.t)
run_index = rep(n, nrow(tmp))
tmp <- cbind(run_index, tmp)
all_sim <- rbind(all_sim, tmp)
}
all_sim <- all_sim[-1,]
write.csv(all_sim, file="output_20200405/scenario1A_lowR0_uncontrolled.csv")
## Scenario 1B: R0 1.5 - 2.5, 30-40% reduction March 19 - May 14 ####
all_sim <- matrix(NA,1,(Ncomp*7)+3)
colnames(all_sim) <- cnames.allsim
for(n in 1:nsim){
R0vec <- runif(length(time), min = 1.5, max = 2.5)
R0vec[t_March19:t_May14] <- runif(length(t_March19:t_May14), 1.5*(1-0.4), 2.5*(1-0.3))
tmp <- sair_step_variableR0(stoch = TRUE, stoch.init = TRUE,
R0vec, Ncomp, ICs, params, time, delta.t)
run_index = rep(n, nrow(tmp))
tmp <- cbind(run_index, tmp)
all_sim <- rbind(all_sim, tmp)
}
all_sim <- all_sim[-1,]
write.csv(all_sim, file="output_20200405/scenario1B_lowR0_MildDistancing.csv")
## Scenario 1C: R0 1.5 - 2.5, 45-65% reduction March 19 - May 14, 48-76% May 15 - Dec 26 ####
all_sim <- matrix(NA,1,(Ncomp*7)+3)
colnames(all_sim) <- cnames.allsim
for(n in 1:nsim){
R0vec <- runif(length(time), min = 1.5, max = 2.5)
R0vec[t_March19:t_May14] <- runif(length(t_March19:t_May14), 1.5*(1-0.65), 2.5*(1-0.45))
R0vec[(t_May14+1):length(time)] <- runif(length((t_May14+1):length(time)), 1.5*(1-0.76), 2.5*(1-0.48))
tmp <- sair_step_variableR0(stoch = TRUE, stoch.init = TRUE,
R0vec, Ncomp, ICs, params, time, delta.t)
run_index = rep(n, nrow(tmp))
tmp <- cbind(run_index, tmp)
all_sim <- rbind(all_sim, tmp)
}
all_sim <- all_sim[-1,]
write.csv(all_sim, file="output_20200405/scenario1C_lowR0_ModDistancing.csv")
## Scenario 2A: R0 2.5 - 3.5, uncontrolled ####
all_sim <- matrix(NA,1,(Ncomp*7)+3)
colnames(all_sim) <- cnames.allsim
for(n in 1:nsim){
R0vec <- runif(length(time), min = 2.5, max = 3.5)
tmp <- sair_step_variableR0(stoch = TRUE, stoch.init = TRUE,
R0vec, Ncomp, ICs, params, time, delta.t)
run_index = rep(n, nrow(tmp))
tmp <- cbind(run_index, tmp)
all_sim <- rbind(all_sim, tmp)
}
all_sim <- all_sim[-1,]
write.csv(all_sim, file="output_20200405/scenario2A_highR0_uncontrolled.csv")
## Scenario 2B: R0 2.5 - 3.5, 30-40% reduction March 19 - May 14 ####
all_sim <- matrix(NA,1,(Ncomp*7)+3)
colnames(all_sim) <- cnames.allsim
for(n in 1:nsim){
R0vec <- runif(length(time), min = 2.5, max = 3.5)
R0vec[t_March19:t_May14] <- runif(length(t_March19:t_May14), 2.5*(1-0.4), 3.5*(1-0.3))
tmp <- sair_step_variableR0(stoch = TRUE, stoch.init = TRUE,
R0vec, Ncomp, ICs, params, time, delta.t)
run_index = rep(n, nrow(tmp))
tmp <- cbind(run_index, tmp)
all_sim <- rbind(all_sim, tmp)
}
all_sim <- all_sim[-1,]
write.csv(all_sim, file="output_20200405/scenario2B_highR0_MildDistancing.csv")
## Scenario 2C: R0 2.5 - 3.5, 45-65% reduction March 19 - May 14, 48-76% May 15 - Dec 26 ####
all_sim <- matrix(NA,1,(Ncomp*7)+3)
colnames(all_sim) <- cnames.allsim
for(n in 1:nsim){
R0vec <- runif(length(time), min = 2.5, max = 3.5)
R0vec[t_March19:t_May14] <- runif(length(t_March19:t_May14), 2.5*(1-0.65), 3.5*(1-0.45))
R0vec[(t_May14+1):length(time)] <- runif(length((t_May14+1):length(time)), 2.5*(1-0.76), 3.5*(1-0.48))
tmp <- sair_step_variableR0(stoch = TRUE, stoch.init = TRUE,
R0vec, Ncomp, ICs, params, time, delta.t)
run_index = rep(n, nrow(tmp))
tmp <- cbind(run_index, tmp)
all_sim <- rbind(all_sim, tmp)
}
all_sim <- all_sim[-1,]
write.csv(all_sim, file="output_20200405/scenario2C_highR0_ModDistancing.csv")