inst/scripts/generateResults.R
In emmalouisedavis/TTI-branching-process: TTI COVID-19 branching process model
# Running the model and storing outputs
library(data.table)
library(tidyverse)
library(git2r)
library(tictoc)
library(ggplot2)
library(patchwork)
library(cowplot)
library(latex2exp)
library(furrr)
library(sn)
library(ggrepel)
library(testthat)
library(svglite)
#########################
# General results
rm(list = ls())
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# slow TTI (1 day each for test and trace)
# good implementation
scenarios1 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 1,
max_quar_delay = c(1),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.65),
test_delay = c(1), #time from isolation to test result
sensitivity = c(0.65,0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results1 <- ringbp::parameter_sweep(scenarios1,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results1, file = "data-raw/res_1.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
tic()
# medium speed TTI (1 day total for test and trace)
# good implementation
# testing asymptomatics: false
scenarios2 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.65),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.65,0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results2 <- ringbp::parameter_sweep(scenarios2,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results2, file = "data-raw/res_2.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
tic()
# medium speed TTI (1 day total for test and trace)
# good implementation
# testing asymptomatics: true
scenarios2b <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.65),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(TRUE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results2b <- ringbp::parameter_sweep(scenarios2b,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results2b, file = "data-raw/res_2b.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# fast TTI (instant testing and tracing)
# good implementation
scenarios3 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0,
max_quar_delay = c(0),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.65),
test_delay = c(0), #time from isolation to test result
sensitivity = c(0.65,0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed paramters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results3 <- ringbp::parameter_sweep(scenarios3,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut = FALSE)
toc()
# #+ writeout
saveRDS(sweep_results3, file = "data-raw/res_3.rds")
rm(list=ls())
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# slow TTI (1 day each for test and trace)
# poor adherence
scenarios4 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.182),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 1,
max_quar_delay = c(1),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.119),
iso_adhere = c(0.109),
test_delay = c(1), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed paramters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results4 <- ringbp::parameter_sweep(scenarios4,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut = FALSE)
toc()
# #+ writeout
saveRDS(sweep_results4, file = "data-raw/res_4.rds")
rm(list=ls())
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# medium speed TTI (1 day total for test and trace)
# poor adherence
# testing asymptomatics: false
scenarios5 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.182),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.119),
iso_adhere = c(0.109),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results5 <- ringbp::parameter_sweep(scenarios5,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results5, file = "data-raw/res_5.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# medium speed TTI (1 day total for test and trace)
# poor adherence
# testing asymptomatics: true
scenarios5b <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.182),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.119),
iso_adhere = c(0.109),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(TRUE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results5b <- ringbp::parameter_sweep(scenarios5b,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results5b, file = "data-raw/res_5b.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# fast TTI (instant testing and tracing)
# poor adherence
scenarios6 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.182),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0,
max_quar_delay = c(0),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.119),
iso_adhere = c(0.109),
test_delay = c(0), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results6 <- ringbp::parameter_sweep(scenarios6,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results6, file = "data-raw/res_6.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# slow TTI (1 day each for test and trace)
# good adherence with boosted traced isolation adherence
scenarios7 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 1,
max_quar_delay = c(1),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.9),
test_delay = c(1), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results7 <- ringbp::parameter_sweep(scenarios7,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results7, file = "data-raw/res_7.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# medium speed TTI (1 day total for test and trace)
# good adherence with boosted traced isolation adherence
# testing asymptomatics: false
scenarios8 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.9),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results8 <- ringbp::parameter_sweep(scenarios8,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results8, file = "data-raw/res_8.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# medium speed TTI (1 day total for test and trace)
# good adherence with boosted traced isolation adherence
# testing asymptomatics: true
scenarios8b <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.9),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(TRUE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results8b <- ringbp::parameter_sweep(scenarios8b,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results8b, file = "data-raw/res_8b.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
rm(list = ls())
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# fast TTI (instant testing and tracing)
# good adherence with boosted traced isolation adherence
scenarios9 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0,
max_quar_delay = c(0),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.9),
test_delay = c(0), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results9 <- ringbp::parameter_sweep(scenarios9,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results9, file = "data-raw/res_9.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
# Load in separate sets of scenarios and combine
sweep_results1 <- readRDS("data-raw/res_1.rds")
sweep_results2 <- readRDS("data-raw/res_2.rds")
sweep_results2b <- readRDS("data-raw/res_2b.rds")
sweep_results3 <- readRDS("data-raw/res_3.rds")
sweep_results4 <- readRDS("data-raw/res_4.rds")
sweep_results5 <- readRDS("data-raw/res_5.rds")
sweep_results5b <- readRDS("data-raw/res_5b.rds")
sweep_results6 <- readRDS("data-raw/res_6.rds")
sweep_results7 <- readRDS("data-raw/res_7.rds")
sweep_results8 <- readRDS("data-raw/res_8.rds")
sweep_results8b <- readRDS("data-raw/res_8b.rds")
sweep_results9 <- readRDS("data-raw/res_9.rds")
#
sweep_results <- as_tibble(rbind(sweep_results1,sweep_results2,sweep_results2b,sweep_results3,sweep_results4,sweep_results5,
sweep_results5b,sweep_results6,sweep_results7,sweep_results8,sweep_results8b,sweep_results9))
sweep_results$scenario <- 1:nrow(sweep_results)
# Clear redundant data frames from working memory
sweep_results1 = sweep_results2 = sweep_results3 = sweep_results4 = sweep_results5 = sweep_results5b = sweep_results6 = sweep_results7 = sweep_results8 = sweep_results8b = sweep_results9 = sweep_results2b = c()
# # # Save final combined results (DONE)
saveRDS(sweep_results, file = "data-raw/res_complete.rds")
emmalouisedavis/TTI-branching-process documentation built on Dec. 20, 2021, 5:17 a.m.
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# Running the model and storing outputs
library(data.table)
library(tidyverse)
library(git2r)
library(tictoc)
library(ggplot2)
library(patchwork)
library(cowplot)
library(latex2exp)
library(furrr)
library(sn)
library(ggrepel)
library(testthat)
library(svglite)
#########################
# General results
rm(list = ls())
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# slow TTI (1 day each for test and trace)
# good implementation
scenarios1 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 1,
max_quar_delay = c(1),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.65),
test_delay = c(1), #time from isolation to test result
sensitivity = c(0.65,0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results1 <- ringbp::parameter_sweep(scenarios1,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results1, file = "data-raw/res_1.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
tic()
# medium speed TTI (1 day total for test and trace)
# good implementation
# testing asymptomatics: false
scenarios2 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.65),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.65,0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results2 <- ringbp::parameter_sweep(scenarios2,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results2, file = "data-raw/res_2.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
tic()
# medium speed TTI (1 day total for test and trace)
# good implementation
# testing asymptomatics: true
scenarios2b <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.65),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(TRUE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results2b <- ringbp::parameter_sweep(scenarios2b,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results2b, file = "data-raw/res_2b.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# fast TTI (instant testing and tracing)
# good implementation
scenarios3 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0,
max_quar_delay = c(0),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.65),
test_delay = c(0), #time from isolation to test result
sensitivity = c(0.65,0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed paramters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results3 <- ringbp::parameter_sweep(scenarios3,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut = FALSE)
toc()
# #+ writeout
saveRDS(sweep_results3, file = "data-raw/res_3.rds")
rm(list=ls())
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# slow TTI (1 day each for test and trace)
# poor adherence
scenarios4 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.182),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 1,
max_quar_delay = c(1),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.119),
iso_adhere = c(0.109),
test_delay = c(1), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed paramters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results4 <- ringbp::parameter_sweep(scenarios4,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut = FALSE)
toc()
# #+ writeout
saveRDS(sweep_results4, file = "data-raw/res_4.rds")
rm(list=ls())
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# medium speed TTI (1 day total for test and trace)
# poor adherence
# testing asymptomatics: false
scenarios5 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.182),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.119),
iso_adhere = c(0.109),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results5 <- ringbp::parameter_sweep(scenarios5,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results5, file = "data-raw/res_5.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# medium speed TTI (1 day total for test and trace)
# poor adherence
# testing asymptomatics: true
scenarios5b <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.182),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.119),
iso_adhere = c(0.109),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(TRUE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results5b <- ringbp::parameter_sweep(scenarios5b,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results5b, file = "data-raw/res_5b.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# fast TTI (instant testing and tracing)
# poor adherence
scenarios6 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.182),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0,
max_quar_delay = c(0),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.119),
iso_adhere = c(0.109),
test_delay = c(0), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results6 <- ringbp::parameter_sweep(scenarios6,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results6, file = "data-raw/res_6.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# slow TTI (1 day each for test and trace)
# good adherence with boosted traced isolation adherence
scenarios7 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 1,
max_quar_delay = c(1),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.9),
test_delay = c(1), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results7 <- ringbp::parameter_sweep(scenarios7,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results7, file = "data-raw/res_7.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# medium speed TTI (1 day total for test and trace)
# good adherence with boosted traced isolation adherence
# testing asymptomatics: false
scenarios8 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.9),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results8 <- ringbp::parameter_sweep(scenarios8,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results8, file = "data-raw/res_8.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# medium speed TTI (1 day total for test and trace)
# good adherence with boosted traced isolation adherence
# testing asymptomatics: true
scenarios8b <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0.5,
max_quar_delay = c(0.5),
index_R0 = c(1.3),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.9),
test_delay = c(0.5), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(TRUE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results8b <- ringbp::parameter_sweep(scenarios8b,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results8b, file = "data-raw/res_8b.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
rm(list = ls())
devtools::load_all()
options(future.rng.onMisuse="ignore")
set.seed(210115)
no.samples <- 5000
# fast TTI (instant testing and tracing)
# good adherence with boosted traced isolation adherence
scenarios9 <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Adherence"),
delay_shape = c(0.7),
delay_scale = 1
)),
inc_meanlog = 1.434065,
inc_sdlog = 0.6612,
inf_shape = 17.773185,
inf_rate = 1.388388,
inf_shift = 12.978985,
min_quar_delay = 0,
max_quar_delay = c(0),
index_R0 = c(1.3,1.5),
prop.asym = c(0.31),
control_effectiveness = seq(0, 0.8, 0.2),
self_report = c(0.5),
iso_adhere = c(0.9),
test_delay = c(0), #time from isolation to test result
sensitivity = c(0.95), #percent of cases detected
precaution = c(7), #this could be between 0 and 7? Number of days stay in isolation if negative test
num.initial.cases = c(5),
test_asym = c(FALSE)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenario = 1:dplyr::n())
cap_cases <- 2000
max_days <- 300
## Parameterise fixed parameters
sim_with_params <- purrr::partial(ringbp::scenario_sim,
cap_max_days = max_days,
cap_cases = cap_cases,
r0isolated = 0,
disp.iso = 1,
disp.com = 0.23,
quarantine = TRUE)
future::plan("multicore")
#+ full_run
tic()
## Run parameter sweep
sweep_results9 <- ringbp::parameter_sweep(scenarios9,
sim_fn = sim_with_params,
samples = no.samples,
show_progress = TRUE,
earlyOut=FALSE)
toc()
# #+ writeout
saveRDS(sweep_results9, file = "data-raw/res_9.rds")
rm(list=ls())
Sys.sleep(120)
##################################################################
# Load in separate sets of scenarios and combine
sweep_results1 <- readRDS("data-raw/res_1.rds")
sweep_results2 <- readRDS("data-raw/res_2.rds")
sweep_results2b <- readRDS("data-raw/res_2b.rds")
sweep_results3 <- readRDS("data-raw/res_3.rds")
sweep_results4 <- readRDS("data-raw/res_4.rds")
sweep_results5 <- readRDS("data-raw/res_5.rds")
sweep_results5b <- readRDS("data-raw/res_5b.rds")
sweep_results6 <- readRDS("data-raw/res_6.rds")
sweep_results7 <- readRDS("data-raw/res_7.rds")
sweep_results8 <- readRDS("data-raw/res_8.rds")
sweep_results8b <- readRDS("data-raw/res_8b.rds")
sweep_results9 <- readRDS("data-raw/res_9.rds")
#
sweep_results <- as_tibble(rbind(sweep_results1,sweep_results2,sweep_results2b,sweep_results3,sweep_results4,sweep_results5,
sweep_results5b,sweep_results6,sweep_results7,sweep_results8,sweep_results8b,sweep_results9))
sweep_results$scenario <- 1:nrow(sweep_results)
# Clear redundant data frames from working memory
sweep_results1 = sweep_results2 = sweep_results3 = sweep_results4 = sweep_results5 = sweep_results5b = sweep_results6 = sweep_results7 = sweep_results8 = sweep_results8b = sweep_results9 = sweep_results2b = c()
# # # Save final combined results (DONE)
saveRDS(sweep_results, file = "data-raw/res_complete.rds")
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