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5c_sensitivity_twoway.R
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5c_sensitivity_twoway.R
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## Two-way sensitivity analyses: cost-scenario parameters
## Load packages ---------------------------------------------------------------
library(flumodels)
library(flumodelsutil)
library(tidyverse)
## Load equations and fixed input parameters -----------------------------------
hd_frac <- 0.75
source("0_simulation_functions.R")
source("1_call_simulation.R")
source("2_get_inputs.R")
source("5a_sensitivity_functions.R")
## Get parameter vectors -------------------------------------------------------
# Values for main text
pars_old <- c(hd_frac = 0.75, hd_frac_gain = 0.10, hdVE_mult_low = 1.225, hdVE_mult_high = 1.45)
# Values for supplement text
# pars_old <- c(hd_frac = 0.75, hd_frac_gain = 0.15, hdVE_mult_low = 1.225, hdVE_mult_high = 1.45)
nsim <- 11
pars_new <- expand.grid(hd_frac_miss = seq(0, 0.1, length.out = nsim),
ndelay = seq(0, 21 , length.out = nsim))
pars_new <- cbind(pars_new, t(pars_old))
pars_new <- check_pars(pars_new)
## Low severity season ---------------------------------------------------------
low_list <- list()
for (p in 1:nrow(pars_new)) {
parsample <- pars_new[p,]
low_list[[p]] <- get_comparison(season = "Low",
ndelay = as.numeric(parsample["ndelay"]),
pars = pars, diffpars = diffpars,
diff_fracs = diff_fracs,
chr = chr, hfr = hfr,
fracSympt_by_age = fracSympt_by_age,
hd_frac = as.numeric(parsample["hd_frac"]),
hd_frac_miss = as.numeric(parsample["hd_frac_miss"]),
hd_frac_gain = as.numeric(parsample["hd_frac_gain"]),
hdVE_mult = as.numeric(parsample["hdVE_mult_low"]),
rel_VE_by_age = rel_VE_by_age, rel_hdVE_by_age = rel_hdVE_by_age
) %>%
mutate(hd_frac = as.numeric(parsample["hd_frac"]),
hd_frac_miss = as.numeric(parsample["hd_frac_miss"]),
hd_frac_gain = as.numeric(parsample["hd_frac_gain"]),
hdVE_mult = as.numeric(parsample["hdVE_mult_low"]),
ndelay = as.numeric(parsample["ndelay"]),
nsim = p)
}
low_all <- bind_rows(low_list)
## High severity season --------------------------------------------------------
high_list <- list()
for (p in 1:nrow(pars_new)) {
parsample <- pars_new[p,]
high_list[[p]] <- get_comparison(season = "High",
ndelay = as.numeric(parsample["ndelay"]),
pars = pars, diffpars = diffpars,
diff_fracs = diff_fracs,
chr = chr, hfr = hfr,
fracSympt_by_age = fracSympt_by_age,
hd_frac = as.numeric(parsample["hd_frac"]),
hd_frac_miss = as.numeric(parsample["hd_frac_miss"]),
hd_frac_gain = as.numeric(parsample["hd_frac_gain"]),
hdVE_mult = as.numeric(parsample["hdVE_mult_high"]),
rel_VE_by_age = rel_VE_by_age, rel_hdVE_by_age = rel_hdVE_by_age
) %>%
mutate(hd_frac = as.numeric(parsample["hd_frac"]),
hd_frac_miss = as.numeric(parsample["hd_frac_miss"]),
hd_frac_gain = as.numeric(parsample["hd_frac_gain"]),
hdVE_mult = as.numeric(parsample["hdVE_mult_high"]),
ndelay = as.numeric(parsample["ndelay"]),
nsim = p)
}
high_all <- bind_rows(high_list)
all <- rbind(low_all, high_all)
## Calculate change in burden --------------------------------------------------
net_cases <- all %>%
filter(time == max(time)) %>%
select(season = Scenario, scenario, time, val = Discharged6, hd_frac_miss, ndelay) %>%
mutate(val = (val/chr[6])) %>%
group_by(`hd_frac_miss`, `ndelay`, season) %>%
mutate(val = round((val[1] - val) * popsize) ) %>%
summarize(val = sum(val)) %>%
ungroup()
net_hosps <- all %>%
filter(time == max(time)) %>%
select(season = Scenario, scenario, time, val = Discharged6, hd_frac_miss, ndelay) %>%
group_by(hd_frac_miss, ndelay, season) %>%
mutate(val = round((val[1] - val) * popsize) ) %>%
summarize(val = sum(val)) %>%
ungroup()
net_deaths <- all %>%
filter(time == max(time)) %>%
select(season = Scenario, scenario, time, val = Died6, hd_frac_miss, ndelay) %>%
group_by(hd_frac_miss, ndelay, season) %>%
mutate(val = round((val[1] - val) * popsize) ) %>%
summarize(val = sum(val)) %>%
ungroup()
burden_tsa <- list(cases_tsa = net_cases, hosps_tsa = net_hosps, deaths_tsa = net_deaths)
save(file = paste0("results/tsa_main.RData"), burden_tsa)
#save(file = paste0("results/tsa_supplement.RData"), burden_tsa)