## This script runs stochastic processing for each modelling group and disease
## There is a function for each year
## Before running check the in_path, output_path, pre_aggregation_path
## and log_file are set correctly
## This also uses dettl to create the db connection so you will need to
## set dettl_root to path to a local dettl repo.
continue_on_error <- function(expr) {
tryCatch(
withCallingHandlers(
eval(expr),
error = function(e) {
calls <- sys.calls()
calls <- calls[8:length(calls)] ## Ignore all try catch stuff from top of stack trace
message(paste(e$message, limitedLabels(calls), sep = "\n"))
}
),
error = function(e) {invisible(NULL)}
)
}
write_output_metadata <- function(touchstone, modelling_group, disease,
paths, metadata_csv) {
files <- data.frame(
touchstone = touchstone,
modelling_group = modelling_group,
disease = disease,
files = c(paths$all_u5_cal_file, paths$all_u5_coh_file,
paths$all_cal_file, paths$all_coh_file),
is_cohort = c(FALSE, TRUE, FALSE, TRUE),
is_under5 = c(TRUE, TRUE, FALSE, FALSE)
)
write.table(files, output_files, sep = ",", append = TRUE,
row.names = FALSE, col.names = FALSE)
}
do_stochastics_2021 <- function(con, test_run, in_path, out_path) {
aggregated_path <- file.path(out_path, "aggregated")
pre_aggregation_path <- file.path(out_path, "pre-aggregate")
log_file <- file.path(out_path, "log.txt")
output_files <- file.path(out_path, "output_files.csv")
files <- data.frame(
touchstone = character(0),
modelling_group = character(0),
disease = character(0),
files = character(0),
is_cohort = logical(0),
is_under5 = logical(0)
)
dir.create(aggregated_path, showWarnings = FALSE, recursive = TRUE)
dir.create(pre_aggregation_path, showWarnings = FALSE, recursive = TRUE)
write.csv(files, output_files, row.names = FALSE)
lines <- Inf
if (isTRUE(test_run)) {
lines <- 30
}
stub <- "Andromachi Karachaliou - stochastic-burden.202110gavi-2.MenA_Cambridge-Trotter_"
modelling_group <- "Cambridge-Trotter"
disease <- "MenA"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("mena-no-vaccination", "mena-campaign-default",
"mena-routine-default", "mena-booster-default",
"mena-campaign-ia2030_target", "mena-routine-ia2030_target"),
in_path = file.path(in_path, "Cambridge-Trotter"),
files = c(paste0(stub, "no_vaccination_:index.csv.xz"),
paste0(stub, "campaign_default_:index.csv.xz"),
paste0(stub, "routine_default_:index.csv.xz"),
paste0(stub, "booster_:index.csv.xz"),
paste0(stub, "campaign-ia2030_target_:index.csv.xz"),
paste0(stub, "routine-ia2030_target_:index.csv.xz")),
cert = "",
index_start = 1,
index_end = 26,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "Aniruddha Deshpande - stochastic_burden_est_lopman_"
modelling_group <- "Emory-Lopman"
disease <- "Rota"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("rota-no-vaccination",
"rota-routine-default",
"rota-routine-ia2030_target"),
in_path = file.path(in_path, "Emory-Lopman"),
files = c(paste0(stub, "no_vaccination_2022_01_31.csv.xz"),
paste0(stub, "routine_2022_01_31.csv.xz"),
paste0(stub, "ia2030_target_2022_01_31.csv.xz")),
cert = "",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
allow_missing_disease = TRUE,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "Allison Portnoy - stochastic-burden-est."
modelling_group <- "Harvard-Sweet"
disease <- "HPV"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("hpv-no-vaccination",
"hpv-campaign-default",
"hpv-campaign-ia2030_target",
"hpv-routine-default",
"hpv-routine-ia2030_target"),
in_path = file.path(in_path, "Harvard-Sweet"),
files = c(paste0(stub, "novacc_run_:index.csv.xz"),
paste0(stub, "coverage_202110gavi-3_hpv-campaign-default_run_:index.csv.xz"),
paste0(stub, "coverage_202110gavi-3_hpv-campaign-ia2030_target_run_:index.csv.xz"),
paste0(stub, "coverage_202110gavi-3_hpv-routine-default_run_:index.csv.xz"),
paste0(stub, "coverage_202110gavi-3_hpv-routine-ia2030_target_run_:index.csv.xz")),
cert = "",
index_start = 1,
index_end = 200,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
runid_from_file = TRUE,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "Keith Fraser - stochastic-burden-estimates.202110gavi-3_YF_IC-Garske_"
modelling_group <- "IC-Garske"
disease <- "YF"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("yf-no-vaccination",
"yf-preventive-default",
"yf-preventive-ia2030_target",
"yf-routine-default",
"yf-routine-ia2030_target"),
in_path = file.path(in_path, "IC-Garske"),
files = paste0(stub, ":scenario_:index.csv.xz"),
cert = "",
index_start = 1,
index_end = 200,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
modelling_group <- "IVI-Kim"
disease <- "Typhoid"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("typhoid-no-vaccination",
"typhoid-campaign-default", "typhoid-campaign-ia2030_target",
"typhoid-routine-default", "typhoid-routine-ia2030_target"),
in_path = file.path(in_path, "IVI-Kim-Typhoid"),
files = c("Jong-Hoon Kim - stoch_Typhoid_novacc_20211217T1.csv.xz",
"Jong-Hoon Kim - stoch_Typhoid_campaign-default_20211217T1.csv.xz",
"Jong-Hoon Kim - stoch_Typhoid_campaign-ia2030_20211217T1.csv.xz",
"Jong-Hoon Kim - stoch_Typhoid_routine-default_20211217T1.csv.xz",
"Jong-Hoon Kim - stoch_Typhoid_routine-ia2030_20211217T1.csv.xz"),
cert = "",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#####################################################
stub <- "stochastic_burden_est_HepB-IC-Hallett_"
modelling_group <- "IC-Hallett"
disease <- "HepB"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("hepb-bd-default-hepb-routine-default",
"hepb-bd-routine-default",
"hepb-bd-routine-ia2030_target-hepb-routine-ia2030_target",
"hepb-bd-routine-ia2030_target",
"hepb-hepb-routine-default",
"hepb-hepb-routine-ia2030_target",
"hepb-no-vaccination"),
in_path = file.path(in_path, "IC-Hallett"),
files = paste0(stub, ":scenario_:index.csv.xz"),
cert = "Margaret de Villiers - cert115",
index_start = 1,
index_end = 200,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = "deaths",
cases = c("hepb_cases_acute_severe","hepb_cases_comp_cirrh",
"hepb_cases_hcc_no_cirrh"),
dalys = "dalys",
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
modelling_group <- "IVI-Kim"
disease <- "Cholera"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("cholera-no-vaccination", "cholera-campaign-default"),
in_path = file.path(in_path, "IVI-Kim-Cholera"),
files = c("Jong-Hoon Kim - stoch_Cholera_novacc_20211221T00.csv.xz",
"Jong-Hoon Kim - stoch_Cholera_campaign_20211222T212131.csv.xz"),
cert = "",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
modelling_group <- "JHU-Lee"
disease <- "Cholera"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("cholera-no-vaccination", "cholera-campaign-default"),
in_path = file.path(in_path, "JHU-Lee-Cholera"),
files = c("Kaiyue Zou - no-vaccination.csv.xz",
"Kaiyue Zou - campaign-default.csv.xz"),
cert = "",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "Amy Winter - stochastic_burden_est-rubella-"
modelling_group <- "JHU-Lessler"
disease <- "Rubella"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("rubella-routine-no-vaccination",
"rubella-campaign-default",
"rubella-rcv1-default",
"rubella-rcv2-default",
"rubella-rcv1-rcv2-default",
"rubella-campaign-ia2030_target",
"rubella-rcv1-ia2030_target",
"rubella-rcv2-ia2030_target",
"rubella-rcv1-rcv2-ia2030_target"),
in_path = file.path(in_path, "JHU-UGA-Winter-Rubella"),
files = c(paste0(stub, "routine-no-vaccination_:index.csv.xz"),
paste0(stub, "campaign-default_:index.csv.xz"),
paste0(stub, "rcv1-default_:index.csv.xz"),
paste0(stub, "rcv2-default_:index.csv.xz"),
paste0(stub, "rcv1-rcv2-default_:index.csv.xz"),
paste0(stub, "campaign-ia2030_target_:index.csv.xz"),
paste0(stub, "rcv1-ia2030_target_:index.csv.xz"),
paste0(stub, "rcv2-ia2030_target_:index.csv.xz"),
paste0(stub, "rcv1-rcv2-ia2030_target_:index.csv.xz")),
cert = "",
index_start = 1,
index_end = 11,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = "rubella_deaths_congenital",
cases = "rubella_cases_congenital",
dalys = "dalys",
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
list_params_hib_pcv <- data.frame(
outcome = c("cases_men", "cases_men", "cases_men", "cases_men", "cases_men",
"cases_pneumo", "cases_pneumo", "deaths_men", "deaths_pneumo"),
proportion = c(1, 0.014, 0.045, 0.021, 0.017, 1, 0.06, 1, 1),
average_duration = c(0.04,1000,1000,1000,1000,0.02,1000,1000,1000),
disability_weight = c(0.133, 0.043, 0.027, 0.552, 0.61, 0.051, 0.019, 1, 1)
)
hib_scenarios <- c("hib-no-vaccination-LiST",
"hib-routine-default-LiST",
"hib-routine-ia2030_target-LiST")
modelling_group <- "JHU-Tam"
disease <- "Hib"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = hib_scenarios,
in_path = file.path(in_path, "JHU-Tam-Carter-Hib"),
files = ":scenario.csv.xz",
cert = "",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = c("deaths_men", "deaths_pneumo"),
cases = c("cases_men", "cases_pneumo"),
dalys = list_params_hib_pcv,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
# And to sort out DALYs on the centrals:
for (hib_scenario in hib_scenarios) {
stoner::stoner_dalys_for_db(
con, list_params_hib_pcv, "JHU-Tam",
"Hib", "202110gavi-3", hib_scenario,
output_file = file.path(aggregated_path,
sprintf("%s_central_dalys.qs", hib_scenario)))
}
pcv_scenarios <- c("pcv-no-vaccination-LiST",
"pcv-routine-default-LiST",
"pcv-routine-ia2030_target-LiST")
modelling_group <- "JHU-Tam"
disease <- "PCV"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = pcv_scenarios,
in_path = file.path(in_path, "JHU-Tam-Carter-PCV"),
files = ":scenario.csv.xz",
cert = "",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = c("deaths_men", "deaths_pneumo"),
cases = c("cases_men", "cases_pneumo"),
dalys = list_params_hib_pcv,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
# And to sort out DALYs on the centrals:
for (pcv_scenario in pcv_scenarios) {
stoner::stoner_dalys_for_db(
con, list_params_hib_pcv, "JHU-Tam",
"PCV", "202110gavi-3", pcv_scenario,
output_file = file.path(aggregated_path,
sprintf("%s_central_dalys.qs", pcv_scenario)))
}
list_params_rota <- data.frame(
outcome = c("cases", "deaths"),
proportion = c(1, 1),
average_duration = c(0.01, 1000),
disability_weight = c(0.247, 1)
)
rota_scenarios <- c("rota-no-vaccination-LiST",
"rota-routine-default-LiST",
"rota-routine-ia2030_target-LiST")
modelling_group <- "JHU-Tam"
disease <- "Rota"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = rota_scenarios,
in_path = file.path(in_path, "JHU-Tam-Carter-Rota"),
files = ":scenario.csv.xz",
cert = "",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
dalys = list_params_rota,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
# And to sort out DALYs on the centrals:
for (rota_scenario in rota_scenarios) {
stoner::stoner_dalys_for_db(
con, list_params_rota, "JHU-Tam",
"Rota", "202110gavi-3", rota_scenario,
output_file = file.path(aggregated_path,
sprintf("%s_central_dalys.qs", rota_scenario)))
}
####################################################################################
stub <- "stochastic_burden_est_MenA_KPWA_"
modelling_group <- "KPW-Jackson"
disease <- "MenA"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("mena-booster-default",
"mena-campaign-default",
"mena-campaign-ia2030_target",
"mena-no-vaccination",
"mena-routine-default",
"mena-routine-ia2030_target"),
in_path = file.path(in_path, "KPW-Jackson-MenA"),
files = c(paste0(stub, "booster_default_:index.csv.xz"),
paste0(stub, "campaign_default_:index.csv.xz"),
paste0(stub, "campaign_ia2030_target_:index.csv.xz"),
paste0(stub, "none_default_:index.csv.xz"),
paste0(stub, "routine_default_:index.csv.xz"),
paste0(stub, "routine_ia2030_target_:index.csv.xz")),
cert = "",
index_start = 1,
index_end = 26,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
modelling_group <- "Li"
disease <- "HepB"
touchstone <- "202110gavi-2"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("hepb-bd-default-hepb-routine-default",
"hepb-bd-routine-default",
"hepb-bd-routine-ia2030_target-hepb-routine-ia2030_target",
"hepb-bd-routine-ia2030_target",
"hepb-hepb-routine-default",
"hepb-hepb-routine-ia2030_target",
"hepb-no-vaccination"
),
in_path = file.path(in_path, "Li"),
files = paste0(":scenario:index.csv.xz"),
cert = "cert105",
index_start = 1,
index_end = 200,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = c("hepb_deaths_acute", "hepb_deaths_total_cirrh", "hepb_deaths_hcc"),
cases = c("hepb_cases_acute_symp", "hepb_cases_fulminant",
"hepb_cases_chronic", "hepb_chronic_symptomatic_in_acute_phase"),
dalys = "dalys",
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "Kaja Abbas - PSA_202110gavi-3_"
modelling_group <- "LSHTM-Clark"
disease <- "Hib"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("hib-no-vaccination","hib-routine-default","hib-routine-ia2030_target"),
in_path = file.path(in_path, "LSHTM-Clark_Hib"),
files = c(paste0(stub, ":scenario.csv.xz")),
cert = "Kaja Abbas - hib_cert116",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "Kaja Abbas - PSA_202110gavi-3_"
modelling_group <- "LSHTM-Clark"
disease <- "Rota"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("rota-no-vaccination","rota-routine-default","rota-routine-ia2030_target"),
in_path = file.path(in_path, "LSHTM-Clark_Rota"),
files = c(paste0(stub, ":scenario.csv.xz")),
cert = "Kaja Abbas - rota_cert117",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "stochastic-burden-"
modelling_group <- "LSHTM-Jit"
disease <- "HPV"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("hpv-no-vaccination",
"hpv-campaign-default",
"hpv-routine-default",
"hpv-campaign-ia2030_target",
"hpv-routine-ia2030_target"),
in_path = file.path(in_path, "LSHTM-Jit_HPV"),
files = c(paste0(stub, "novaccination_all_202110gavi-3_hpv-no-vaccination.csv.xz"),
paste0(stub, "vaccination_all_202110gavi-3_hpv-campaign-default.csv.xz"),
paste0(stub, "vaccination_all_202110gavi-3_hpv-routine-default.csv.xz"),
paste0(stub, "vaccination_all_202110gavi-3_hpv-campaign-ia2030_target.csv.xz"),
paste0(stub, "vaccination_all_202110gavi-3_hpv-routine-ia2030_target.csv.xz")),
cert = "cert104",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "Han Fu - stochastic_burden_estimate_measles-LSHTM-Jit-"
modelling_group <- "LSHTM-Jit"
disease <- "Measles"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("measles-no-vaccination",
"measles-campaign-default",
"measles-campaign-only-default",
"measles-mcv1-default",
"measles-mcv2-default",
"measles-campaign-ia2030_target",
"measles-campaign-only-ia2030_target",
"measles-mcv1-ia2030_target",
"measles-mcv2-ia2030_target"),
in_path = file.path(in_path, "LSHTM-Jit_Measles"),
files = c(paste0(stub, "no-vaccination.csv.xz"),
paste0(stub, "campaign-default.csv.xz"),
paste0(stub, "campaign-only-default.csv.xz"),
paste0(stub, "mcv1-default.csv.xz"),
paste0(stub, "mcv2-default.csv.xz"),
paste0(stub, "campaign-ia2030_target.csv.xz"),
paste0(stub, "campaign-only-ia2030_target.csv.xz"),
paste0(stub, "mcv1-ia2030_target.csv.xz"),
paste0(stub, "mcv2-ia2030_target.csv.xz")),
cert = "Han Fu - cert112_measles-LSHTM-Jit_202110gavi-3",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "stochastic_burden_est_"
modelling_group <- "NUS-Chen"
disease <- "PCV"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("pcv-no-vaccination","pcv-routine-default","pcv-routine-ia2030_target"),
in_path = file.path(in_path, "LSHTM-NUS-Chen_PCV"),
files = paste0(stub, ":scenario.csv.xz"),
cert = "Jemima Koh - cert121",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
modelling_group <- "PHE-Vynnycky"
disease <- "Rubella"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("rubella-routine-no-vaccination",
"rubella-campaign-default",
"rubella-rcv1-default",
"rubella-rcv2-default",
"rubella-rcv1-rcv2-default",
"rubella-campaign-ia2030_target",
"rubella-rcv1-ia2030_target",
"rubella-rcv2-ia2030_target",
"rubella-rcv1-rcv2-ia2030_target"),
in_path = file.path(in_path, "PHE-Vynnycky_Rubella"),
files = c("VIMC_NV_RCV1RCV2Camp_country:index.csv.xz",
"VIMC_DF_Camp_country:index.csv.xz",
"VIMC_DF_RCV1Camp_country:index.csv.xz",
"VIMC_DF_RCV1RCV2Camp_country:index.csv.xz",
"VIMC_DF_RCV1RCV2_country:index.csv.xz",
"VIMC_IA_Camp_country:index.csv.xz",
"VIMC_IA_RCV1Camp_country:index.csv.xz",
"VIMC_IA_RCV1RCV2Camp_country:index.csv.xz",
"VIMC_IA_RCV1RCV2_country:index.csv.xz"),
cert = "",
index_start = 1,
index_end = 112,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = "rubella_deaths_congenital",
cases = "rubella_cases_congenital",
dalys = "dalys",
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "coverage_202110gavi-3_"
modelling_group <- "PSU-Ferrari"
disease <- "Measles"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("measles-campaign-default",
"measles-campaign-ia2030_target",
"measles-campaign-only-default",
"measles-campaign-only-ia2030_target",
"measles-mcv1-default",
"measles-mcv1-ia2030_target",
"measles-mcv2-default",
"measles-mcv2-ia2030_target",
"measles-no-vaccination"),
in_path = file.path(in_path, "PSU-Ferrari-Measles"),
files = paste0(stub, ":scenario.csv.xz"),
cert = "",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "Sean Moore - stochastic_burden_est_JE_UND-Moore_"
modelling_group <- "UND-Moore"
disease <- "JE"
touchstone <- "202110gavi-2"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("je-routine-no-vaccination",
"je-campaign-default",
"je-routine-default",
"je-campaign-ia2030_target",
"je-routine-ia2030_target"
),
in_path = file.path(in_path, "UND-Moore-JE"),
files = paste0(stub, ":scenario.csv.xz"),
cert = "Sean Moore - cert108",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "stochastic_burden_est_YF_UND-Perkins_"
modelling_group <- "UND-Perkins"
disease <- "YF"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("yf-no-vaccination",
"yf-preventive-default",
"yf-preventive-ia2030_target",
"yf-routine-default",
"yf-routine-ia2030_target"),
in_path = file.path(in_path, "UND-Perkins-YF"),
files = paste0(stub, ":scenario_:index.csv.xz"),
cert = "",
index_start = 1,
index_end = 200,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
#############################################################################
stub <- "Holly Burrows - stochastic_burden_est_TF-Yale-Burrows"
modelling_group <- "Yale-Pitzer"
disease <- "Typhoid"
touchstone <- "202110gavi-3"
continue_on_error({
paths <- stone_stochastic_process(
con,
modelling_group = modelling_group,
disease = disease,
touchstone = touchstone,
scenarios = c("typhoid-no-vaccination",
"typhoid-campaign-default", "typhoid-campaign-ia2030_target",
"typhoid-routine-default", "typhoid-routine-ia2030_target"),
in_path = file.path(in_path, "Yale-Pitzer-Typhoid"),
files = c(paste0(stub, "-novacc_202110.csv.xz"),
paste0(stub, "_campaign-default_202110.csv.xz"),
paste0(stub, "_campaign-IA2030_202110.csv.xz"),
paste0(stub, "_routine-default_202110.csv.xz"),
paste0(stub, "_routine-IA2030_202110.csv.xz")),
cert = "",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines)
write_output_metadata(touchstone, modelling_group, disease,
paths, output_files)
})
}
do_stochastics_2019 <- function(con, test_run, in_path, out_path) {
aggregation_path <- file.path(out_path, "aggregated")
pre_aggregation_path <- file.path(out_path, "pre-aggregate")
log_file <- file.path(out_path, "log.txt")
lines <- Inf
if (isTRUE(test_run)) {
lines <- 30
}
#############################################################################
stub <- "Andromachi Karachaliou - stochastic-burden.201910gavi-4.MenA_Cambridge-Trotter_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "Cambridge-Trotter",
disease = "MenA",
touchstone = "201910gavi-5",
scenarios = c("mena-campaign-bestcase", "mena-campaign-default", "mena-no-vaccination",
"mena-routine-bestcase", "mena-routine-default"),
in_path = file.path(in_path, "Cambridge-Trotter"),
files = c(paste0(stub, "campaign-bestcase_:index.csv.xz"),
paste0(stub, "campaign-default_:index.csv.xz"),
paste0(stub, "no-vaccination_:index.csv.xz"),
paste0(stub, "routine-bestcase_:index.csv.xz"),
paste0(stub, "routine-default_:index.csv.xz")),
cert = "cert60",
index_start = 1,
index_end = 52,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
#############################################################################
stub <- "Ivane Gamkrelidze - stochastic-burden-template.201910gavi-4.HepB_CDA-Razavi_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "CDA-Razavi",
disease = "HepB",
touchstone = "201910gavi-5",
senarios = c("hepb-bd-default-hepb-routine-default",
"hepb-bd-routine-bestcase-hepb-routine-bestcase",
"hepb-no-vaccination",
"hepb-stop",
"hepb-bd-routine-bestcase",
"hepb-bd-routine-default",
"hepb-hepb-routine-bestcase",
"hepb-hepb-routine-default"
),
in_path = file.path(in_path, "CDA-Razavi"),
files = c(paste0(stub, "all_:scenario.csv.xz"),
paste0(stub, "all_:scenario.csv.xz"),
paste0(stub, "all_:scenario.csv.xz"),
paste0(stub, "all_:scenario.csv.xz"),
paste0(stub, "bd_:scenario.csv.xz"),
paste0(stub, "bd_:scenario.csv.xz"),
paste0(stub, "non_bd_:scenario.csv.xz"),
paste0(stub, "non_bd_:scenario.csv.xz")),
cert = "Ivane Gamkrelidze - cert68",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = c("hepb_deaths_acute","hepb_deaths_dec_cirrh","hepb_deaths_hcc"),
cases = c("hepb_cases_acute_severe","hepb_cases_dec_cirrh","hepb_cases_hcc"),
dalys = "dalys",
lines = lines))
#############################################################################
stub <- "Molly Steele - stochastic-burden.201910gavi-4.Rota_Emory-Lopman_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "Emory-Lopman",
disease = "Rota",
touchstone = "201910gavi-5",
scenarios = c("rota-no-vaccination",
"rota-routine-bestcase",
"rota-routine-default"),
in_path = file.path(in_path, "Emory-Lopman"),
files = paste0(stub, ":scenario.csv.xz"),
cert = "Molly Steele - cert66",
index_start = NA,
index_end = NA,
out_path= aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
allow_missing_disease = TRUE,
lines = lines))
#############################################################################
stub <- "stochastic-burden-est.201910gavi-5.HPV_Harvard-Sweet_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "Harvard-Sweet",
disease = "HPV",
touchstone = "201910gavi-5",
sceanrios = c("hpv-campaign-bestcase",
"hpv-campaign-default",
"hpv-no-vaccination",
"hpv-routine-bestcase",
"hpv-routine-default"),
in_path = file.path(in_path, "Harvard-Sweet"),
files = c(paste0(stub, "campaign-bestcase_run_:index.csv.xz"),
paste0(stub, "campaign-default_run_:index.csv.xz"),
paste0(stub, "novacc_run_:index.csv.xz"),
paste0(stub, "routine-bestcase_run_:index.csv.xz"),
paste0(stub, "routine-default_run_:index.csv.xz")),
cert = "",
index_start = 1,
index_end = 200,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
runid_from_file = TRUE,
bypass_cert_check = TRUE,
lines = lines))
#############################################################################
stub <- "stochastic-burden-estimates.201910gavi-4_YF_IC-Garske_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "IC-Garske",
disease = "YF",
touchstone = "201910gavi-5",
scenarios = c("yf-no-vaccination",
"yf-preventive-bestcase",
"yf-preventive-default",
"yf-routine-bestcase",
"yf-routine-default",
"yf-stop"),
in_path = file.path(in_path, "IC-Garske2"),
files = paste0(stub, ":scenario_:index.csv.xz"),
cert = "Katy Gaythorpe - cert62",
index_start = 1,
index_end = 200,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
#############################################################################
stub <- "stochastic_burden_est_HepB-IC-Hallett_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "IC-Hallett",
disease = "HepB",
touchstone = "201910gavi-5",
scenarios = c("hepb-bd-default-hepb-routine-default",
"hepb-bd-routine-bestcase-hepb-routine-bestcase",
"hepb-no-vaccination",
"hepb-stop",
"hepb-bd-routine-bestcase",
"hepb-bd-routine-default",
"hepb-hepb-routine-bestcase",
"hepb-hepb-routine-default"),
in_path = file.path(in_path, "IC-Hallett"),
files = paste0(stub, ":scenario_:index.csv.xz"),
cert = "Margaret de Villiers - cert73",
index_start = 1,
index_end = 200,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = "deaths",
cases = c("hepb_cases_acute_severe","hepb_cases_comp_cirrh",
"hepb_cases_hcc_no_cirrh"),
dalys = "dalys",
lines = lines))
#############################################################################
continue_on_error(stone_stochastic_process(
con,
modelling_group = "IVI-Kim",
disease = "Cholera",
touchstone = "201910gavi-5",
scenarios = c("cholera-no-vaccination", "cholera-campaign-default"),
in_path = file.path(in_path, "IVI-Kim-Cholera"),
files = c("Jong-Hoon Kim - stoch_output_Cholera_novacc_20210902.csv.xz",
"Jong-Hoon Kim - stoch_output_Cholera_campaign_20210902.csv.xz"),
cert = "Jong-Hoon Kim - cert89",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
#############################################################################
continue_on_error(stone_stochastic_process(
con,
modelling_group = "IVI-Kim",
disease = "Typhoid",
touchstone = "201910gavi-5",
scenarios = c("typhoid-no-vaccination", "typhoid-campaign-default", "typhoid-routine-default"),
in_path = file.path(in_path, "IVI-Kim-Typhoid"),
files = c("Jong-Hoon Kim - stoch_Typhoid_novacc.csv.xz",
"Jong-Hoon Kim - stoch_Typhoid_campaign.csv.xz",
"Jong-Hoon Kim - stoch_Typhoid_routine.csv.xz"),
cert = "Jong-Hoon Kim - cert90",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
#############################################################################
stub <- "Amy Winter - stochastic_burden_est-"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "JHU-Lessler",
disease = "Rubella",
touchstone = "201910gavi-5",
scenarios = c("rubella-campaign-bestcase",
"rubella-campaign-default",
"rubella-routine-no-vaccination",
"rubella-rcv1-bestcase",
"rubella-rcv1-default",
"rubella-rcv1-rcv2-bestcase",
"rubella-rcv1-rcv2-default",
"rubella-rcv2-bestcase",
"rubella-rcv2-default",
"rubella-stop"),
in_path = file.path(in_path, "JHU-Lessler"),
files = c(rep(paste0(stub, ":scenario_:index.csv.xz"), 2),
paste0(stub, "rubella-no-vaccination_:index.csv.xz"),
rep(paste0(stub, ":scenario_:index.csv.xz"), 7)),
cert = "Amy Winter - cert70",
index_start = 1, index_end = 12, out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = "rubella_deaths_congenital",
cases = "rubella_cases_congenital",
dalys = "dalys",
lines = lines))
#############################################################################
stub <- "Michael Jackson - stochastic_burden_est_MenA_KPWA_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "KPW-Jackson",
disease = "MenA",
touchstone = "201910gavi-5",
sceanrios = c("mena-routine-bestcase",
"mena-routine-default",
"mena-campaign-bestcase",
"mena-campaign-default",
"mena-no-vaccination"),
in_path = file.path(in_path, "KPW-Jackson"),
files = c(paste0(stub, "both_bestcase_:index.csv.xz"),
paste0(stub, "both_default_:index.csv.xz"),
paste0(stub, "campaign_bestcase_:index.csv.xz"),
paste0(stub, "campaign_default_:index.csv.xz"),
paste0(stub, "none_default_:index.csv.xz")),
cert = "cert61",
index_start = 1,
index_end = 26,
out_path = aggregated_path,
lines = lines))
#############################################################################
continue_on_error(stone_stochastic_process(
con,
modelling_group = "JHU-Lee",
disease = "Cholera",
touchstone = "201910gavi-5",
scenarios = c("cholera-no-vaccination", "cholera-campaign-default"),
in_path = file.path(in_path, "JHU-Lee"),
files = c("Kaiyue Zou - stochastic-burden-template.201910gavi-5.Cholera_no-vaccination.csv.xz",
"Kaiyue Zou - stochastic-burden-template.201910gavi-5.Cholera_campaign-default.csv.xz"),
cert = "",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines))
#############################################################################
list_params_hib_pcv <- data_frame(
outcome = c("cases_men", "cases_men", "cases_men", "cases_men", "cases_men",
"cases_pneumo", "cases_pneumo", "deaths_men", "deaths_pneumo"),
proportion = c(1, 0.014, 0.045, 0.021, 0.017, 1, 0.06, 1, 1),
average_duration = c(0.04,1000,1000,1000,1000,0.02,1000,1000,1000),
disability_weight = c(0.133, 0.043, 0.027, 0.552, 0.61, 0.051, 0.019, 1, 1)
)
continue_on_error(stone_stochastic_process(
con,
modelling_group = "JHU-Tam",
disease = "Hib",
touchstone = "201910gavi-5",
scenarios = c("hib-no-vaccination-LiST", "hib-routine-default-LiST", "hib-routine-bestcase-LiST"),
in_path = file.path(in_path, "JHU-Tam-Hib"),
files = c("novac:index.csv.xz", "default:index.csv.xz", "best:index.csv.xz"),
cert = "",
index_start = 1,
index_end = 14,
out_path = aggregated_path,
deaths = c("deaths_men", "deaths_pneumo"),
cases = c("cases_men", "cases_pneumo"),
dalys = list_params_hib_pcv,
bypass_cert_check = TRUE,
lines = lines))
# And to add DALYs to the existing
continue_on_error(stone_stochastic_process(
con,
modelling_group = "JHU-Tam",
disease = "PCV",
touchstone = "201910gavi-5",
scenarios = c("pcv-no-vaccination-LiST", "pcv-routine-default-LiST", "pcv-routine-bestcase-LiST"),
in_path = file.path(in_path, "JHU-Tam-PCV"),
files = c("novac:index.csv.xz", "default:index.csv.xz", "best:index.csv.xz"),
cert = "",
index_start = 1,
index_end = 14,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = c("deaths_men", "deaths_pneumo"),
cases = c("cases_men", "cases_pneumo"),
dalys = list_params_hib_pcv,
bypass_cert_check = TRUE,
lines = lines))
list_params_rota <- data_frame(
outcome = c("cases", "deaths"),
proportion = c(1, 1),
average_duration = c(0.01, 1000),
disability_weight = c(0.247, 1)
)
continue_on_error(stone_stochastic_process(
con,
modelling_group = "JHU-Tam",
disease = "Rota",
touchstone = "201910gavi-5",
scenarios = c("rota-no-vaccination-LiST", "rota-routine-default-LiST", "rota-routine-bestcase-LiST"),
in_path = file.path(in_path, "JHU-Tam-Rota"),
files = c("novac:index.csv.xz", "default:index.csv.xz", "best:index.csv.xz"),
cert = "",
index_start = 1,
index_end = 14,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
dalys = list_params_rota,
bypass_cert_check = TRUE,
lines = lines))
#############################################################################
continue_on_error(stone_stochastic_process(
con,
modelling_group = "Li",
disease = "HepB",
touchstone = "201910gavi-5",
scenarios = c("hepb-bd-default-hepb-routine-default",
"hepb-bd-routine-bestcase-hepb-routine-bestcase",
"hepb-no-vaccination",
"hepb-stop",
"hepb-bd-routine-bestcase",
"hepb-bd-routine-default",
"hepb-hepb-routine-bestcase",
"hepb-hepb-routine-default"
),
in_path = file.path(in_path, "Li"),
files = paste0(":scenario:index.csv.xz"),
cert = "cert74",
index_start = 1,
index_end = 200,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = c("hepb_deaths_acute", "hepb_deaths_total_cirrh", "hepb_deaths_hcc"),
cases = c("hepb_cases_acute_symp", "hepb_cases_fulminant",
"hepb_cases_chronic", "hepb_chronic_symptomatic_in_acute_phase"),
dalys = "dalys",
lines = lines))
#############################################################################
stub <- "VIMC_Hib_PSA_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "LSHTM-Clark",
disease = "Hib",
touchstone = "201910gavi-5",
scenarios = c("hib-no-vaccination","hib-routine-bestcase","hib-routine-default"),
in_path = file.path(in_path, "LSHTM-Clark_Hib"),
files = c(paste0(stub, "NoVax.csv.xz"),
paste0(stub, "Best.csv.xz"),
paste0(stub, "Default.csv.xz")),
cert = "cert81",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
stub <- "VIMC_Sp_PSA_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "LSHTM-Clark",
disease = "PCV",
touchstone = "201910gavi-5",
scenarios = c("pcv-no-vaccination","pcv-routine-bestcase","pcv-routine-default"),
in_path = file.path(in_path, "LSHTM-Clark_PCV"),
files = c(paste0(stub, "NoVax.csv.xz"),
paste0(stub, "Best.csv.xz"),
paste0(stub, "Default.csv.xz")),
cert = "cert82",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
stub <- "Hira Tanvir - VIMC_Rota_PSA_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "LSHTM-Clark",
disease = "Rota",
touchstone = "201910gavi-5",
scenarios = c("rota-no-vaccination","rota-routine-bestcase","rota-routine-default"),
in_path = file.path(in_path, "LSHTM-Clark_Rota"),
files = c(paste0(stub, "NoVax.csv.xz"),
paste0(stub, "Best.csv.xz"),
paste0(stub, "Default.csv.xz")),
cert = "",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
#############################################################################
stub <- "stochastic-burden-"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "LSHTM-Jit",
disease = "HPV",
touchstone = "201910gavi-5",
scenarios = c("hpv-campaign-bestcase",
"hpv-campaign-default",
"hpv-no-vaccination",
"hpv-routine-bestcase",
"hpv-routine-default"),
in_path = file.path(in_path, "LSHTM-Jit_HPV"),
files = c(paste0(stub, "vaccination_201910gavi-4_hpv-campaign-bestcase.csv.xz"),
paste0(stub, "vaccination_201910gavi-4_hpv-campaign-default.csv.xz"),
paste0(stub, "novaccination_201910gavi-4_hpv-no-vaccination.csv.xz"),
paste0(stub, "vaccination_201910gavi-4_hpv-routine-bestcase.csv.xz"),
paste0(stub, "vaccination_201910gavi-4_hpv-routine-default.csv.xz")),
cert = "Kaja Abbas - stochastic_parameters_certificate_HPV_LSHTM-Jit_201910gavi-4",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
bypass_cert_check = TRUE,
lines = lines))
#############################################################################
stub <- "stochastic_burden_estimate_measles-LSHTM-Jit-"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "LSHTM-Jit",
disease = "Measles",
touchstone = "201910gavi-5",
scenarios = c("measles-no-vaccination",
"measles-campaign-default","measles-campaign-bestcase",
"measles-campaign-only-default","measles-campaign-only-bestcase",
"measles-mcv1-default","measles-mcv1-bestcase",
"measles-mcv2-default","measles-mcv2-bestcase",
"measles-stop"),
in_path = file.path(in_path, "LSHTM-Jit_Measles"),
files = c(paste0(stub, "no-vaccination_Portnoy.csv.xz"),
paste0(stub, "campaign-default_Portnoy.csv.xz"), paste0(stub, "campaign-bestcase_Portnoy.csv.xz"),
paste0(stub, "campaign-only-default_Portnoy.csv.xz"), paste0(stub, "campaign-only-bestcase_Portnoy.csv.xz"),
paste0(stub, "mcv1-default_Portnoy.csv.xz"), paste0(stub, "mcv1-bestcase_Portnoy.csv.xz"),
paste0(stub, "mcv2-default_Portnoy.csv.xz"), paste0(stub, "mcv2-bestcase_Portnoy.csv.xz"),
paste0(stub, "stop_Portnoy.csv.xz")),
cert = "cert83",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
#############################################################################
stub <- "Template_Stochastic_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "OUCRU-Clapham",
disease = "JE",
touchstone = "201910gavi-5",
scenarios = c("je-campaign-bestcase",
"je-campaign-default",
"je-routine-no-vaccination",
"je-routine-bestcase",
"je-routine-default"),
in_path = file.path(in_path, "OUCRU-Clapham"),
files = c(paste0(stub, "Campaign_Best4_correcting_:index.csv.xz"),
paste0(stub, "Campaign_Default4_correcting_:index.csv.xz"),
paste0(stub, "Naive4_correcting_:index.csv.xz"),
paste0(stub, "Routine_Best4_correcting_:index.csv.xz"),
paste0(stub, "Routine_Default4_correcting_:index.csv.xz")),
cert = "cert76",
index_start = 1,
index_end = 200,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
#############################################################################
stub <- "stochastic_burden_est_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "PHE-Vynnycky",
disease = "Rubella",
touchstone = "201910gavi-5",
scenarios = c("rubella-campaign-bestcase",
"rubella-campaign-default",
"rubella-routine-no-vaccination",
"rubella-rcv1-bestcase",
"rubella-rcv1-default",
"rubella-rcv1-rcv2-bestcase",
"rubella-rcv1-rcv2-default",
"rubella-rcv2-bestcase",
"rubella-rcv2-default",
"rubella-stop"),
in_path = file.path(in_path, "PHE-Vynnycky"),
files = paste0(stub, ":scenario_country:index.csv.xz"),
cert = "", index_start = 1,
index_end = 112,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
deaths = "rubella_deaths_congenital",
cases = "rubella_cases_congenital",
dalys = "dalys",
lines = lines))
#############################################################################
stub <- "Heather Santos - "
continue_on_error(stone_stochastic_process(
con,
modelling_group = "PSU-Ferrari",
disease = "Measles",
touchstone = "201910gavi-5",
scenarios = c("measles-no-vaccination",
"measles-mcv1-bestcase",
"measles-mcv2-bestcase",
"measles-campaign-bestcase",
"measles-mcv1-default",
"measles-mcv2-default",
"measles-campaign-default",
"measles-stop",
"measles-campaign-only-bestcase",
"measles-campaign-only-default"),
in_path = file.path(in_path, "PSU-Ferrari"),
files = c(paste0(stub, "novax_stochastic:index_burden_Measles-PSU-Ferrari.csv.xz"),
paste0(stub, "bestcase_mcv1_stochastic:index_burden_Measles-PSU-Ferrari.csv.xz"),
paste0(stub, "bestcase_mcv2_stochastic:index_burden_Measles-PSU-Ferrari.csv.xz"),
"stochastic:index_burden_Measles-PSU-Ferrari.csv.xz",
paste0(stub, "default_mcv1_stochastic:index_burden_Measles-PSU-Ferrari.csv.xz"),
paste0(stub, "default_mcv2_stochastic:index_burden_Measles-PSU-Ferrari.csv.xz"),
paste0(stub, "default_campaign_stochastic:index_burden_Measles-PSU-Ferrari.csv.xz"),
paste0(stub, "stop_stochastic:index_burden_Measles-PSU-Ferrari.csv.xz"),
paste0(stub, "bestcase_campaign_only_stochastic:index_burden_Measles-PSU-Ferrari.csv.xz"),
paste0(stub, "default_campaign_only_stochastic:index_burden_Measles-PSU-Ferrari.csv.xz")),
cert = "Heather Santos - cert80",
index_start = 1,
index_end = 8,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
#############################################################################
stub <- "Sean Moore - stochastic_burden_est_JE_UND-Moore_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "UND-Moore",
disease = "JE",
touchstone = "201910gavi-5",
scenarios = c("je-campaign-bestcase",
"je-campaign-default",
"je-routine-no-vaccination",
"je-routine-bestcase",
"je-routine-default"),
in_path = file.path(in_path, "UND-Moore"),
files = c(paste0(stub, ":scenario.csv.xz"),
paste0(stub, ":scenario.csv.xz"),
paste0(stub, "je-no-vaccination.csv.xz"),
paste0(stub, ":scenario.csv.xz"),
paste0(stub, ":scenario.csv.xz")),
cert = "Sean Moore - cert58",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
#############################################################################
stub <- "stochastic_burden_est_YF_UND-Perkins_"
continue_on_error(stone_stochastic_process(
con,
modelling_group = "UND-Perkins",
disease = "YF",
touchstone = "201910gavi-5",
scenarios = c("yf-no-vaccination",
"yf-preventive-bestcase",
"yf-preventive-default",
"yf-routine-bestcase",
"yf-routine-default",
"yf-stop"),
in_path = file.path(in_path, "UND-Perkins"),
files = paste0(stub, ":scenario_:index.csv.xz"),
cert = "John Huber - cert85",
index_start = 1,
index_end = 200,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
#############################################################################
continue_on_error(stone_stochastic_process(
con,
modelling_group = "Yale-Pitzer",
disease = "Typhoid",
touchstone = "201910gavi-5",
scenarios = c("typhoid-no-vaccination", "typhoid-campaign-default", "typhoid-routine-default"),
in_path = file.path(in_path, "Yale-Pitzer"),
files = c("Virginia Pitzer - 2021-02-18 17.00.26 - stochastic_output_TF-Yale-Pitzer_novacc.csv.xz",
"Virginia Pitzer - 2021-02-18 16.58.03 - stochastic_output_TF-Yale-Pitzer_campaign.csv.xz",
"Virginia Pitzer - 2021-02-18 16.59.14 - stochastic_output_TF-Yale-Pitzer_camproutine.csv.xz"),
cert = "Virginia Pitzer - cert88",
index_start = NA,
index_end = NA,
out_path = aggregated_path,
pre_aggregation_path = pre_aggregation_path,
log_file = log_file,
lines = lines))
}
library(stoner)
## Change dettl root to local (just using dettl to get a db connection to prod)
dettl_root <- "~/projects/montagu-imports/"
con <- dettl:::db_connect("production", dettl_root)
in_path <- "Z:/File requests/latest/202110gavi/"
out_path <- "Z:/stochastic_2021_output/"
do_stochastics_2021(con, test_run = TRUE,
in_path = in_path,
out_path = out_path)
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