From e3229a56ae8a8e081f6b17f2d46b22d4cdf9355d Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Mon, 27 Apr 2026 16:12:14 +0100
Subject: [PATCH 01/14] Create/use metadata for stochastic folder

---
 R/stochastic_files.R | 46 ++++++++++++++++++++++++++++++
 inst/app/utils.R     | 68 +++++++++++++++++++-------------------------
 2 files changed, 76 insertions(+), 38 deletions(-)

diff --git a/R/stochastic_files.R b/R/stochastic_files.R
index 70cd9bd..31c13c6 100644
--- a/R/stochastic_files.R
+++ b/R/stochastic_files.R
@@ -257,3 +257,49 @@ stone_stochastic_central <- function(base, touchstone, disease, group,
   outfile <- sprintf("%s_%s_central.pq", group, scenario)
   arrow::write_parquet(central, file.path(path, outfile))
 }
+
+
+
+stone_stochastic_make_meta <- function(path) {
+
+  explore_files <- function(touchstone, folder, disease, group) {
+    files <- list.files(file.path(path, touchstone, folder))
+    first <- file.path(path, touchstone, folder, files[1])
+    ds <- arrow::open_dataset(first)
+    outcomes <- ds$schema$names
+    outcomes <- outcomes[tolower(outcomes) %in%
+                           c("cases", "deaths", "dalys", "yll")]
+
+    files <- strsplit(list.files(file.path(path, touchstone, folder)), "_")
+
+    scenarios <- unique(unlist(lapply(files, `[[`, 2)))
+    df <- data.frame()
+    for (scenario in scenarios) {
+      matches <- files[unlist(lapply(files, `[[`, 2)) == scenario]
+      countries <- unique(unlist(lapply(matches, `[[`, 3)))
+      countries <- gsub(".pq", "", countries)
+      df <- rbind(df, data.frame(
+        touchstone = touchstone,
+        disease = disease,
+        group = group,
+        scenario = scenario,
+        countries = paste0(countries, collapse = ";"),
+        outcomes = paste0(outcomes, collapse = ";")
+      ))
+    }
+    df
+  }
+
+  touchstone_meta <- function(touchstone) {
+    entries <- list.files(file.path(path, touchstone))
+    data.table::rbindlist(lapply(entries, function(x) {
+      xs <- strsplit(x, "_")[[1]]
+      explore_files(touchstone, x, xs[1], xs[2])
+    }))
+  }
+
+  touchstones <- basename(list.dirs(paste0(path, "/"), recursive = FALSE))
+  res <- data.table::rbindlist(lapply(touchstones, touchstone_meta))
+  write.csv(res, file.path(path, "meta.csv"),
+            row.names = FALSE, quote = FALSE)
+}
diff --git a/inst/app/utils.R b/inst/app/utils.R
index daaa9cc..26ce773 100644
--- a/inst/app/utils.R
+++ b/inst/app/utils.R
@@ -7,42 +7,35 @@
 # Functions ending in _mg will find things common
 # between to modelling groups, for comparisons.
 
+meta <- read.csv(file.path(data_dir, "meta.csv"))
+
 get_touchstones <- function() {
-  sort(unique(basename(list.dirs(data_dir, recursive = FALSE))))
+  sort(unique(meta$touchstone), decreasing = TRUE)
 }
 
-get_diseases <- function(touchstone1, touchstone2) {
-  lookup <- function(touchstone, res = NULL) {
-    if (is.null(touchstone)) return(res)
-    path <- file.path(data_dir, touchstone)
-    dirs <- basename(list.dirs(path, recursive = FALSE))
-    unique(unlist(lapply(strsplit(dirs, "_"), `[[`, 1)))
+get_diseases <- function(touchstone1, touchstone2 = NULL) {
+  res <- meta$disease[meta$touchstone %in% touchstone1]
+  if (!is.null(touchstone2)) {
+    res <- intersect(res, meta$disease[meta$touchstone %in% touchstone2])
   }
-  res <- lookup(touchstone1)
-  sort(unique(res[res %in% lookup(touchstone2, res)]))
+  sort(unique(res))
 }
 
 get_groups <- function(touchstone1, touchstone2, disease) {
-  lookup <- function(touchstone, disease, res = NULL) {
-    if (is.null(touchstone)) return(res)
-    path <- file.path(data_dir, touchstone)
-    dirs <- basename(list.dirs(path, recursive = FALSE))
-    dirs <- dirs[substr(dirs, 1, nchar(disease) + 1) == paste0(disease, "_")]
-    unique(substring(dirs, nchar(disease) + 2))
+  m <- meta[meta$disease %in% disease, ]
+  res <- m$group[m$touchstone %in% touchstone1]
+  if (!is.null(touchstone2)) {
+    res <- intersect(res, m$group[m$touchstone %in% touchstone2])
   }
-  res <- lookup(touchstone1, disease)
-  sort(unique(res[res %in% lookup(touchstone2, disease, res)]))
+  sort(unique(res))
 }
 
 get_scenarios <- function(touchstone1, touchstone2, disease, group1, group2) {
   lookup <- function(touchstone, disease, group, res = NULL) {
     if ((is.null(touchstone)) || (is.null(group))) return(res)
-    path <- file.path(data_dir, touchstone, paste(disease, group, sep = "_"))
-    files <- basename(list.files(path, recursive = FALSE))
-    files <- gsub(".pq", "", files)
-    files <- substring(files, nchar(group) + 2)
-    ends <- unlist(lapply(gregexpr("_", files), `[[`, 1)) - 1
-    unique(substring(files, 1, ends))
+    sort(unique(meta$scenario[(meta$touchstone %in% touchstone) &
+                              (meta$disease %in% disease) &
+                              (meta$group %in% group)]))
   }
 
   res <- lookup(touchstone1, disease, group1)
@@ -57,14 +50,11 @@ get_countries <- function(touchstone1, touchstone2, disease, group1, group2,
     if ((is.null(touchstone)) || (is.null(group)) || (is.null(scenario))) {
       return(res)
     }
-
-    path <- file.path(data_dir, touchstone, paste(disease, group, sep = "_"))
-    files <- basename(list.files(path, recursive = FALSE))
-    files <- gsub(".pq", "", files)
-    files <- substring(files, nchar(group) + 2)
-    files <- files[substring(files, 1, nchar(scenario) + 1) == paste0(scenario, "_")]
-    ends <- unlist(lapply(gregexpr("_", files), `[[`, 1)) + 1
-    unique(substring(files, ends))
+    cts <- meta$countries[(meta$touchstone %in% touchstone) &
+                          (meta$disease %in% disease) &
+                          (meta$group %in% group) &
+                          (meta$scenario %in% scenario)]
+    sort(unique(strsplit(cts, ";")[[1]]))
   }
   res <- lookup(touchstone1, disease, group1, scenario1)
   res <- res[res %in% lookup(touchstone2, disease, group1, scenario1, res)]
@@ -78,17 +68,18 @@ get_countries <- function(touchstone1, touchstone2, disease, group1, group2,
 
 get_outcomes <- function(touchstone1, touchstone2, disease, group1, group2,
                          scenario1, scenario2, country) {
+
   lookup <- function(touchstone, disease, group, scenario, country, res = NULL) {
     if ((is.null(touchstone)) || (is.null(group)) || (is.null(scenario))) {
       return(res)
     }
-    path <- file.path(data_dir, touchstone, paste(disease, group, sep = "_"))
-    thefile <- sprintf("%s_%s_%s.pq", group, scenario, country)
-    tbl <- arrow::read_parquet(file.path(path, thefile), col_select = NULL)
-    cols <- names(tbl)
-    sort(cols[!cols %in% c("disease", "run_id", "year", "age",
-                           "country", "cohort_size")])
+    outs <- meta$outcomes[(meta$touchstone %in% touchstone) &
+                          (meta$disease %in% disease) &
+                          (meta$group %in% group) &
+                          (meta$scenario %in% scenario)]
+    sort(unique(strsplit(outs, ";")[[1]]))
   }
+
   res <- lookup(touchstone1, disease, group1, scenario1, country)
   res <- res[res %in% lookup(touchstone2, disease, group1, scenario1, country, res)]
   res <- res[res %in% lookup(touchstone1, disease, group2, scenario1, country, res)]
@@ -96,7 +87,8 @@ get_outcomes <- function(touchstone1, touchstone2, disease, group1, group2,
   res <- res[res %in% lookup(touchstone1, disease, group1, scenario2, country, res)]
   res <- res[res %in% lookup(touchstone2, disease, group1, scenario2, country, res)]
   res <- res[res %in% lookup(touchstone1, disease, group2, scenario2, country, res)]
-  sort(unique(res[res %in% lookup(touchstone2, disease, group2, scenario2, country, res)]))
+  res <- sort(unique(res[res %in% lookup(touchstone2, disease, group2, scenario2, country, res)]))
+  res
 }
 
 # GUI helpers.

From 1b75e1debc78aa6708cc1e1176dfca3ba261e313 Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Mon, 27 Apr 2026 16:26:15 +0100
Subject: [PATCH 02/14] Toggle stochastic lines

---
 R/stochastic_graphs.R | 20 ++++++++++++++------
 inst/app/app.R        |  7 ++++---
 2 files changed, 18 insertions(+), 9 deletions(-)

diff --git a/R/stochastic_graphs.R b/R/stochastic_graphs.R
index 172369f..7158804 100644
--- a/R/stochastic_graphs.R
+++ b/R/stochastic_graphs.R
@@ -35,6 +35,8 @@ age_string <- function(ages) {
 ##' @param packit_file Used with packit_id to specify the filename of an RDS
 ##' file providing burden estimates. We expect to find scenario, year, age,
 ##' country, burden_outcome and value fields in the table.
+##' @param include_stochastics Default TRUE, select whether to draw the
+##' individual stochastic lines.
 ##' @param include_quantiles Default TRUE, select whether to plot the
 ##' 5% and 95% quantile lines.
 ##' @param include_mean Default TRUE, select whether to plot the mean.
@@ -49,6 +51,7 @@ stone_stochastic_graph <- function(base, touchstone, disease, group, country,
                                    scenario, outcome, ages = NULL,
                                    by_cohort = FALSE, log = FALSE,
                                    packit_id = NULL, packit_file = NULL,
+                                   include_stochastics = TRUE,
                                    include_quantiles = TRUE,
                                    include_mean = TRUE,
                                    include_median = TRUE,
@@ -76,6 +79,8 @@ stone_stochastic_graph <- function(base, touchstone, disease, group, country,
   runs <- max(d$run_id)
   miny <- max(1, min(d[[outcome]]))
   maxy <- max(d[[outcome]])
+  minx <- min(d$year)
+  maxx <- max(d$year)
   log <- if (log) "y" else ""
 
   if (!is.null(packit_id)) {
@@ -85,12 +90,15 @@ stone_stochastic_graph <- function(base, touchstone, disease, group, country,
   }
   par(mar = c(5, 4, 5, 2))
   plot(ylab = outcome_ylab, xlab = if (by_cohort) "Birth Cohort" else "year",
-       x = d$year[d$run_id == 1], y = d[[outcome]][d$run_id == 1], type="l",
-       col = "#b0b0b0", ylim = c(miny, maxy), main = title, log = log)
-
-  for (i in 2:runs) {
-    lines(x = d$year[d$run_id == i], y = d[[outcome]][d$run_id == i],
-          col = "#b0b0b0")
+       x = NULL, y = NULL,
+       col = "#b0b0b0", xlim = c(minx, maxx), ylim = c(miny, maxy), main = title,
+       log = log)
+
+  if (include_stochastics) {
+    for (i in seq_len(runs)) {
+      lines(x = d$year[d$run_id == i], y = d[[outcome]][d$run_id == i],
+            col = "#b0b0b0")
+    }
   }
 
   avgs <- d %>% group_by(.data$year) %>%
diff --git a/inst/app/app.R b/inst/app/app.R
index bef6258..0850ee9 100644
--- a/inst/app/app.R
+++ b/inst/app/app.R
@@ -54,8 +54,8 @@ app_ui <- function() {
                    choices = c("All", "Under 5")),
       checkboxGroupInput(
         sprintf("%s_options", prefix), "Plot Options:",
-        choices = c("Quantiles", "Median", "Mean", "Log-Y"),
-        selected = c("Quantiles", "Median", "Mean", "Log-Y"),
+        choices = c("Stochastics", "Quantiles", "Median", "Mean", "Log-Y"),
+        selected = c("Stochastics", "Quantiles", "Median", "Mean", "Log-Y"),
         inline = TRUE),
       actionButton(sprintf("%s_plot_btn", prefix), "Plot")
     ))
@@ -325,7 +325,8 @@ app_server <- function(input, output, session) {
             log = "Log-Y" %in% pr$opts,
             include_median = "Median" %in% pr$opts,
             include_quantiles = "Quantiles" %in% pr$opts,
-            include_mean = "Mean" %in% pr$opts
+            include_mean = "Mean" %in% pr$opts,
+            include_stochastics = "Stochastics" %in% pr$opts
           )
         })
       })

From a33090186b5661d13751c2a9c012d8fcd950b548 Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Mon, 27 Apr 2026 16:31:02 +0100
Subject: [PATCH 03/14] Impact -> Burden Diff

---
 inst/app/app.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/inst/app/app.R b/inst/app/app.R
index 0850ee9..a1d7509 100644
--- a/inst/app/app.R
+++ b/inst/app/app.R
@@ -112,9 +112,9 @@ app_ui <- function() {
       make_panel("Burden", "b", 1, 1, 1),
       make_panel("Burden/TS", "bts", 2, 1, 1),
       make_panel("Burden/MG", "bmg", 1, 1, 2),
-      make_panel("Impact", "i", 1, 2, 1),
-      make_panel("Impact/TS", "its", 2, 2, 1),
-      make_panel("Impact/MG", "img", 1, 2, 2)
+      make_panel("Burden Diff", "i", 1, 2, 1),
+      make_panel("Burden Diff/TS", "its", 2, 2, 1),
+      make_panel("Burden Diff/MG", "img", 1, 2, 2)
     )
   )
 }

From 87ee15cca68fc9d9b919666e1a187f25913edd6b Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Thu, 30 Apr 2026 16:01:01 +0100
Subject: [PATCH 04/14] Refactor graphs for y-axis and add age-based

---
 DESCRIPTION                             |   2 +-
 R/stochastic_graphs.R                   | 359 +++++++++++++++++-------
 inst/app/app.R                          |  79 +++---
 inst/app/utils.R                        |  16 ++
 tests/testthat/test_stochastic_graphs.R |  37 ++-
 5 files changed, 335 insertions(+), 158 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 72ada1b..f82a26c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: stoner
 Title: Support for Building VIMC Montagu Touchstones, using Dettl
-Version: 0.1.21
+Version: 0.1.22
 Authors@R:
     c(person("Wes", "Hinsley",role = c("aut", "cre", "cst", "dnc", "elg", "itr", "sng", "ard"),
            email = "w.hinsley@imperial.ac.uk"),
diff --git a/R/stochastic_graphs.R b/R/stochastic_graphs.R
index 7158804..a2c59e7 100644
--- a/R/stochastic_graphs.R
+++ b/R/stochastic_graphs.R
@@ -1,13 +1,25 @@
-age_string <- function(ages) {
-  if (is.null(ages)) {
-    "all ages"
-  } else if (identical(unique(sort(ages)), as.numeric(min(ages):max(ages)))) {
-    sprintf("age %d..%d", min(ages), max(ages))
-  } else "selected ages"
+filter_string <- function(s, units) {
+  if (is.null(s)) return(sprintf("all %s", units))
+  s <- as.numeric(s)
+  if (identical(unique(sort(s)), as.numeric(min(s):max(s)))) {
+    sprintf("%s %d..%d", units, min(s), max(s))
+  } else sprintf("selected %s", units)
 }
 
-##' Draw a stochastic plot showing all the different runs, with the mean,
-##' median, 5% and 95% quantiles shown.
+##' A feature-rich graph plotting function, which can show the stochastic
+##' line for an outcome for different runs, the mean, median and quantiles.
+##' If two scenarios are specified, then the burden difference between
+##' scenarios is plotted. Additionally, if multiple groups or multiple
+##' touchstones are specified, then separate graphs with the same scaling are
+##' plotted to allow comparison.
+##'
+##' Graphs can have calendar year or birth cohort on the y-axis for
+##' time-series plots, in which case age is aggregated and the ages to be
+##' included can be specified. Alternatively, the x-axis can be age aggregated
+##' over time, where the calendar years to be aggregated can be specified.
+##'
+##' Finally, it can also include a central estimate provided as a file within
+##' a packit, from the montagu reporting portal.
 ##'
 ##' @export
 ##' @title Stochastic plot
@@ -17,18 +29,33 @@ age_string <- function(ages) {
 ##' @importFrom graphics lines par
 ##' @importFrom stats quantile median
 ##' @importFrom grDevices recordPlot
-##' @param base The folder in which the standardised stochastic files are found.
-##' @param touchstone The touchstone name (for the graph title)
-##' @param disease The disease, used for building the filename and graph title.
-##' @param group The modelling group, used in the filename and graph title.
+##' @param base The root older in which the standardised stochastic files are
+##' found. Within it should be folders by touchstone.
+##' @param touchstones A touchstone to plot, or a pair  of touchstones to
+##' compare. If a pair, then only one modelling group can be specified.
+##' @param disease The disease for the stochastics to display
+##' @param groups The modelling group to plot data for, or a pair of modelling
+##' groups to be compared, in which case, only touchstone can be specified.
 ##' @param country The country to plot.
-##' @param scenario The scenario to plot.
-##' @param outcome The outcome to plot, for example `deaths`, `cases`, `dalys` or
-##' since 2023, `yll`.
-##' @param ages A vector of one or more ages to be selected and aggregated, or
-##' if left as NULL, then all ages are used and aggregated.
+##' @param scenarios A scenario to plot data for, or a pair of scenarios, in
+##' which case the outcome for the first scenario, subtract the second, will be
+##' plotted.
+##' @param outcome The outcome to plot, for example `deaths`, `cases`, `dalys`
+##' or  since 2023, `yll`.
+##' @param xaxis The default is "time", meaning the x-axis is either calendar
+##' year, or birth cohort, depending on the `by_cohort` parameter, and age is
+##' then aggregated according using the `aggregate_ages` parameter.
+##' Alternatively, set to "age", and aggregate by time using the
+##' `aggregate_years` parameter.
+##' @param filter Filter either ages, years, or birth cohort years to a range,
+##' depending on what `xaxis` is chosen. When `xaxis` is `time`, we are summing
+##' ages, so can filter to `0:4` for example, to only include under 5 burdens.
+##' When `xaxis` is `age` then we want to see burden by age for an aggregated
+##' time period; the filter here is for which years to include, or which
+##' birth cohorts to include, depending on whether `by_cohort` is set. `filter`
+##' can be left as `NULL` to include all ages or years.
 ##' @param by_cohort If TRUE, then age is subtracted from year to convert it to
-##' year of birth before aggregating.
+##' year of birth before aggregating. This is only used when xaxis is "time".
 ##' @param log If TRUE, then use a logged y-axis.
 ##' @param packit_id If set, then read central burden estimates from a file
 ##' within a packit on the Montagu packit server.
@@ -41,129 +68,247 @@ age_string <- function(ages) {
 ##' 5% and 95% quantile lines.
 ##' @param include_mean Default TRUE, select whether to plot the mean.
 ##' @param include_median Default TRUE, select whether to plot the median.
-##' @param scenario2 Default NULL; if set, then the burdens from this
-##' scenario will be subtracted from those in `scenario` - ie, this plots
-##' an impact graph of applying the second scenario. For many graphs that
-##' use this, the result will be positive numbers, representing cases
-##' or deaths averted.
-
-stone_stochastic_graph <- function(base, touchstone, disease, group, country,
-                                   scenario, outcome, ages = NULL,
+
+stone_stochastic_graph <- function(base,
+                                   touchstones, disease, groups, country,
+                                   scenarios, outcome,
+                                   xaxis = "time",
+                                   filter = NULL,
                                    by_cohort = FALSE, log = FALSE,
                                    packit_id = NULL, packit_file = NULL,
                                    include_stochastics = TRUE,
                                    include_quantiles = TRUE,
                                    include_mean = TRUE,
-                                   include_median = TRUE,
-                                   scenario2 = NULL) {
+                                   include_median = TRUE) {
 
-  d <- prepare_graph_data(base, touchstone, disease, group, country,
-                         scenario, outcome, ages, by_cohort)
-  age <- age_string(ages)
+  # Forbid multi-touchstone AND multi-group for now - which means we will
+  # only produce 2 graphs. We could do more - for example, 3 groups that
+  # model a disease, but for now, we'll stick with two.
 
-  title <- sprintf("%s, %s, %s, %s\n%s, %s\n", touchstone, disease, group, age,
-                   scenario, country)
-  outcome_ylab <- outcome
+  if (!length(touchstones) %in% 1:2) {
+    cli::cli_abort("Only specify one or two touchstones.")
+  }
+
+  if (!length(groups) %in% 1:2) {
+    cli::cli_abort("Only specify one or two modelling groups.")
+  }
+
+  if (!length(scenarios) %in% 1:2) {
+    cli::cli_abort("Only specify one or two scenarios.")
+  }
+
+  if ((length(touchstones) == 2) && (length(groups) ==2)) {
+    cli::cli_abort("Only one of `touchstones` or `groups` can be plural.")
+  }
+
+  if (!isTRUE(tolower(xaxis) %in% c("time", "age"))) {
+    cli::cli_abort("`xaxis` must be either `time` or `age`.")
+  }
+
+  xaxis <- tolower(xaxis)
 
-  if (!is.null(scenario2)) {
-    d2 <- prepare_graph_data(base, touchstone, disease, group, country,
-                            scenario2, outcome, ages, by_cohort)
-    title <- sprintf("%s, %s, %s, %s\n%s, %s\n", touchstone, disease, group, age,
-                     sprintf("Impact of %s ->\n%s", scenario, scenario2), country)
-    d <- d[order(d$year, d$run_id), ]
-    d2 <- d2[order(d2$year, d2$run_id), ]
-    d[[outcome]] <- d[[outcome]] - d2[[outcome]]
-    outcome_ylab <- paste(outcome_ylab, "averted")
+  if (!isTRUE(tolower(outcome) %in% c("cases", "dalys", "deaths", "yll"))) {
+    cli::cli_abort("`outcome` must be `cases`, `dalys`, `deaths` or `yll`")
   }
 
-  runs <- max(d$run_id)
-  miny <- max(1, min(d[[outcome]]))
-  maxy <- max(d[[outcome]])
-  minx <- min(d$year)
-  maxx <- max(d$year)
-  log <- if (log) "y" else ""
+  outcome <- tolower(outcome)
+
+  # Fetch the packit data if wanted
 
   if (!is.null(packit_id)) {
-    central <- prepare_central_data(packit_id, packit_file,
-                                    country, scenario, outcome, ages, by_cohort)
-    # To be continued...
-  }
-  par(mar = c(5, 4, 5, 2))
-  plot(ylab = outcome_ylab, xlab = if (by_cohort) "Birth Cohort" else "year",
-       x = NULL, y = NULL,
-       col = "#b0b0b0", xlim = c(minx, maxx), ylim = c(miny, maxy), main = title,
-       log = log)
-
-  if (include_stochastics) {
-    for (i in seq_len(runs)) {
-      lines(x = d$year[d$run_id == i], y = d[[outcome]][d$run_id == i],
-            col = "#b0b0b0")
+    central <- get_packit_data(packit_id, packit_file,
+                               country, scenarios, outcome, by_cohort)
+    if (xaxis == "age") {
+      central <- aggregate_by_age(central, outcome, filter)
+    } else {
+      central <- aggregate_by_year(central, outcome, filter)
     }
   }
 
-  avgs <- d %>% group_by(.data$year) %>%
-    summarise(
-      mean   = mean(.data[[outcome]]),
-      median = median(.data[[outcome]]),
-      q05 = quantile(.data[[outcome]], 0.05),
-      q95 = quantile(.data[[outcome]], 0.95),
-      .groups = "drop"
-    )
-  if (include_mean) {
-    lines(x = avgs$year, y = avgs$mean, col = "#ff4040", lwd = 2)
-  }
-  if (include_median) {
-    lines(x = avgs$year, y = avgs$median, col = "#00ff00", lwd = 2)
-  }
-  if (include_quantiles) {
-    lines(x = avgs$year, y = avgs$q05, col = "#202020", lwd = 2)
-    lines(x = avgs$year, y = avgs$q95, col = "#202020", lwd = 2)
-  }
-  recordPlot()
+  # Fetch the data from the files. We may end up with 1 or 2 graphs here.
+
+  data <- list()
+  n_graphs <- 0
+  for (touchstone in touchstones) {
+    for (group in groups) {
+      n_graphs <- n_graphs + 1
+      data[[n_graphs]] <- get_graph_data(base, touchstone, disease, group,
+                                         country, scenarios, outcome, by_cohort)
+    }
+  }
+
+  # Filter and aggregate the data, finding bounds
+
+  miny <- Inf
+  maxy <- -Inf
+  minx <- Inf
+  maxx <- -Inf
+
+  for (i in 1:n_graphs) {
+    if (xaxis == "age") {
+      data[[i]] <- aggregate_by_age(data[[i]], outcome, filter)
+      minx <- 0
+      maxx <- 100
+    } else {
+      data[[i]] <- aggregate_by_year(data[[i]], outcome, filter)
+      minx <- min(minx, data[[i]]$year)
+      maxx <- max(maxx, data[[i]]$year)
+    }
+    miny <- min(miny, data[[i]][[outcome]])
+    maxy <- max(maxy, data[[i]][[outcome]])
+  }
+
+  # Sort out titles and labels common to both graphs
+  units <- if (xaxis == "time") "ages" else if (by_cohort) "cohorts" else "years"
+  filter_title <- filter_string(filter, units)
+  outcome_ylab <- outcome
+  scenario_title <- scenarios[1]
+
+  if (log) {
+    log <- "y"
+    miny <- max(1, miny)
+  } else {
+    log <- ""
+  }
+
+  if (length(scenarios) == 2) {
+    outcome_ylab <- paste(outcome_ylab, "avered")
+    scenario_title <- sprintf("Difference of %s ->\n%s", scenarios[1],
+                              scenarios[2])
+  }
+
+
+  # Plot the one or two graphs
+
+  res <- list()
+  for (i in 1:n_graphs) {
+    touchstone_title <- touchstones[min(i, length(touchstones))]
+    group_title <- groups[min(i, length(groups))]
+    title <- sprintf("%s, %s, %s, %s\n%s, %s\n",
+                     touchstone_title, disease, group_title,
+                     filter_title,
+                     scenario_title, country)
+
+
+    if (xaxis == "age") {
+      xlabel <- "Age"
+      xfield <- "age"
+    } else {
+      xlabel <- if (by_cohort) "Birth Cohort" else "Year"
+      xfield <- "year"
+    }
+
+
+    runs <- max(data[[i]]$run_id)
+    par(mar = c(5, 4, 5, 2))
+    plot(ylab = outcome_ylab, xlab = xlabel,
+         x = NULL, y = NULL,
+         col = "#b0b0b0", xlim = c(minx, maxx), ylim = c(miny, maxy),
+         main = title, log = log)
+
+    if (include_stochastics) {
+      for (j in seq_len(runs)) {
+        lines(x = data[[i]][[xfield]][data[[i]]$run_id == j],
+              y = data[[i]][[outcome]][data[[i]]$run_id == j],
+              col = "#b0b0b0")
+      }
+    }
+
+    if (include_mean | include_median | include_quantiles) {
+      avgs <- data[[i]] %>% group_by(.data[[xfield]]) %>%
+        summarise(
+          mean   = mean(.data[[outcome]]),
+          median = median(.data[[outcome]]),
+          q05 = quantile(.data[[outcome]], 0.05),
+          q95 = quantile(.data[[outcome]], 0.95),
+          .groups = "drop"
+        )
+      if (include_mean) {
+        lines(x = avgs[[xfield]], y = avgs$mean, col = "#ff4040", lwd = 2)
+      }
+      if (include_median) {
+        lines(x = avgs[[xfield]], y = avgs$median, col = "#00ff00", lwd = 2)
+      }
+      if (include_quantiles) {
+        lines(x = avgs[[xfield]], y = avgs$q05, col = "#202020", lwd = 2)
+        lines(x = avgs[[xfield]], y = avgs$q95, col = "#202020", lwd = 2)
+      }
+    }
+    res[[i]] <- recordPlot()
+  }
+  res
+}
+
+get_burden_difference <- function(data, outcome) {
+  if (length(data) == 2) {
+    data[[1]][[outcome]] <- data[[1]][[outcome]] - data[[2]][[outcome]]
+  }
+  data[[1]]
 }
 
+# Here we'll fetch the data for one graph. `scenarios` might be length 1
+# (which is easy) or 2 - in which case we'll subtract the outcome values of
+# the second from the first. We can also do the by_cohort calculation in here
 
-prepare_graph_data <- function(base, touchstone, disease, group, country,
-                               scenario, outcome, ages, by_cohort) {
+get_graph_data <- function(base, touchstone, disease, group, country,
+                           scenarios, outcome, by_cohort) {
 
-  pq <- sprintf("%s/%s/%s_%s/%s_%s_%s.pq", base, touchstone, disease,
-                group, group, scenario, country)
+  data <- list()
+  for (s in seq_along(scenarios)) {
+    pq <- sprintf("%s/%s/%s_%s/%s_%s_%s.pq", base, touchstone, disease,
+                  group, group, scenarios[s], country)
 
-  d <- arrow::read_parquet(pq)
+    if (!file.exists(pq)) {
+      cli::cli_abort("Couldn't find file {pq} - check files or parameters")
+    }
+    d <- arrow::read_parquet(pq)
+    if (by_cohort) {
+      d$year <- d$year - d$age
+    }
+    d <- d[order(d$year ,d$age, d$run_id), ]
+    data[[s]] <- d[, c("run_id", "year", "age", outcome)]
+  }
+  get_burden_difference(data, outcome)
+}
+
+aggregate_by_year <- function(d, outcome, ages = NULL) {
   if (!is.null(ages)) {
     d <- d[d$age %in% ages, ]
   }
-  if (by_cohort) {
-    d$year <- d$year - d$age
+  d %>% group_by(.data$run_id, .data$year) %>%
+    summarise(
+      !!outcome := sum(.data[[outcome]], na.rm = TRUE),
+      .groups = "drop")
+}
+
+aggregate_by_age <- function(d, outcome, years = NULL) {
+  if (!is.null(years)) {
+    d <- d[d$year %in% years, ]
   }
-  d <- d[, c("run_id", "year", "age", outcome)]
-  d <- d %>% group_by(.data$run_id, .data$year) %>%
+  d %>% group_by(.data$run_id, .data$age) %>%
     summarise(
       !!outcome := sum(.data[[outcome]], na.rm = TRUE),
       .groups = "drop")
-  d
 }
 
-prepare_central_data <- function(packit_id, packit_file,
-  country, scenario, outcome, ages, by_cohort) {
+get_packit_data <- function(packit_id, packit_file,
+  country, scenarios, outcome, by_cohort) {
 
   central <- readRDS(fetch_packit(packit_id, packit_file))
   central <- central[central$country == country, ]
-  central <- central[central$scenario == scenario, ]
   central <- central[central$burden_outcome == outcome, ]
-  if (!is.null(ages)) {
-    central <- central[central$age %in% ages, ]
-  }
-  if (by_cohort) {
-    central$year <- central$year - central$age
+
+  data <- list()
+  for (s in seq_along(scenarios)) {
+    d <- central[central$scenario == scenarios[s], ]
+    if (by_cohort) {
+      d$year <- d$year - d$age
+    }
+    names(d)[names(d) == "value"] <- outcome
+    d <- d[order(d$year, d$age), ]
+    data[[s]] <- d
   }
-  names(central)[names(central) == "value"] <- outcome
-  central <- central %>% group_by(.data$year) %>%
-    summarise(
-      !!outcome := sum(.data[[outcome]], na.rm = TRUE),
-      .groups = "drop")
-  central$run_id <- 0
-  central
+  get_burden_difference(data, outcome)
 }
 
 ##' Launch a Shiny app to allow interactive plotting of
diff --git a/inst/app/app.R b/inst/app/app.R
index a1d7509..03a5242 100644
--- a/inst/app/app.R
+++ b/inst/app/app.R
@@ -47,11 +47,11 @@ app_ui <- function() {
     sidebar <- append(sidebar, scenarios)
     sidebar <- append(sidebar, list(
       selectInput(sprintf("%s_country", prefix), "Country:", choices = c0),
+      selectInput(sprintf("%s_xaxis", prefix), "X-Axis:", choices =
+        c("Time (Calendar)", "Time (Birth Cohort)",
+          "Age (Calendar)", "Age (Birth Cohort)")),
       selectInput(sprintf("%s_outcome", prefix), "Y-Axis:", choices = c0),
-      radioButtons(sprintf("%s_year", prefix), "X-Axis:", inline = TRUE,
-                   choices = c("Calendar", "Cohort")),
-      radioButtons(sprintf("%s_ages", prefix), "Age:", inline = TRUE,
-                   choices = c("All", "Under 5")),
+      textInput(sprintf("%s_filter", prefix), "Filter Ages", value = "All"),
       checkboxGroupInput(
         sprintf("%s_options", prefix), "Plot Options:",
         choices = c("Stochastics", "Quantiles", "Median", "Mean", "Log-Y"),
@@ -253,6 +253,17 @@ app_server <- function(input, output, session) {
                                 input,
                                 prefix)})
 
+    filter <- sprintf("%s_filter", prefix)
+    xaxis <- sprintf("%s_xaxis", prefix)
+
+    observeEvent(input[[xaxis]], {
+      xaxis_val <- input[[xaxis]]
+      if (grepl("Time", xaxis_val)) lab <- "Filter age: (eg. All, or 0-4)"
+      else if (grepl("Calendar", xaxis_val)) lab <- "Filter years: (eg. All, or 2000-2010)"
+      else lab <- "Filter cohorts: (eg. All, or 2000-2010)"
+      updateTextInput(session, filter, label = lab)
+    })
+
     n_graphs <- 1 + ((n_touchstone * n_group) > 1)
     if (n_graphs == 1) {
       graphs <- sprintf("%s_main_plot", prefix)
@@ -262,11 +273,6 @@ app_server <- function(input, output, session) {
     button <- sprintf("%s_plot_btn", prefix)
 
     plot_reactive <- eventReactive(input[[button]], {
-      ages <- NULL
-      if (input[[sprintf("%s_ages", prefix)]] == "Under 5") {
-        ages <- 0:4
-      }
-
       check <- function(pre, x1, x2, type) {
         if (is.null(x2)) return(TRUE)
         if ((grepl(pre, prefix)) && (input[[x1]] == input[[x2]])) {
@@ -286,7 +292,7 @@ app_server <- function(input, output, session) {
       if (!check("i", is1, is2, "scenario")) return(NULL)
 
       list(
-        ages = ages,
+        filter = input[[sprintf("%s_filter", prefix)]],
         opts = input[[sprintf("%s_options", prefix)]],
 
         touchstone = c(input[[it1]], if (!is.null(it2)) input[[it2]] else NULL),
@@ -296,38 +302,41 @@ app_server <- function(input, output, session) {
         disease = input[[id]],
         country = input[[ic]],
         outcome = input[[sprintf("%s_outcome", prefix)]],
-        year    = input[[sprintf("%s_year", prefix)]]
+        xaxis   = input[[sprintf("%s_xaxis", prefix)]]
+      )
+    })
+
+    res_reactive <- reactive({
+      pr <- plot_reactive()
+      req(pr)
+      filter_vec <- parse_filter(pr$filter)
+
+      stoner::stone_stochastic_graph(
+        base = data_dir,
+        touchstones = pr$touchstone,
+        disease = pr$disease,
+        groups = pr$group,
+        country = pr$country,
+        scenarios = pr$scenario,
+        outcome = pr$outcome,
+        xaxis = if (grepl("Time", pr$xaxis)) "time" else "age",
+        filter = filter_vec,
+        by_cohort = grepl("Birth Cohort", pr$xaxis),
+        log = "Log-Y" %in% pr$opts,
+        include_median = "Median" %in% pr$opts,
+        include_quantiles = "Quantiles" %in% pr$opts,
+        include_mean = "Mean" %in% pr$opts,
+        include_stochastics = "Stochastics" %in% pr$opts
       )
     })
 
     for (g in seq_along(graphs)) {
       local({
         gg <- g
-
         output[[graphs[gg]]] <- renderPlot({
-          pr <- plot_reactive()
-          req(pr)
-
-          it <- pr$touchstone[min(gg, length(pr$touchstone))]
-          ig <- pr$group[min(gg, length(pr$group))]
-
-          stoner::stone_stochastic_graph(
-            base = data_dir,
-            touchstone = it,
-            disease = pr$disease,
-            group = ig,
-            country = pr$country,
-            scenario = pr$scenario[1],
-            scenario2 = if (length(pr$scenario) > 1) pr$scenario[2] else NULL,
-            outcome = pr$outcome,
-            by_cohort = pr$year == "Cohort",
-            ages = pr$ages,
-            log = "Log-Y" %in% pr$opts,
-            include_median = "Median" %in% pr$opts,
-            include_quantiles = "Quantiles" %in% pr$opts,
-            include_mean = "Mean" %in% pr$opts,
-            include_stochastics = "Stochastics" %in% pr$opts
-          )
+          res <- res_reactive()
+          req(res)
+          replayPlot(res[[gg]])
         })
       })
     }
diff --git a/inst/app/utils.R b/inst/app/utils.R
index 26ce773..f3a6ad6 100644
--- a/inst/app/utils.R
+++ b/inst/app/utils.R
@@ -170,3 +170,19 @@ update_country <- function(session, ts1, ts2, disease, g1, g2, s1, s2, country,
   outcomes <- get_outcomes(ts1, ts2, disease, g1, g2, s1, s2, country)
   update_dropdown_keep(session, o, outcomes, input[[o]])
 }
+
+parse_filter <- function(s) {
+  if (tolower(s) == "all") return(NULL)
+  s <- gsub(" ", "", s)
+  s <- strsplit(s, ",")[[1]]
+  sel <- integer(0)
+  for (bit in s) {
+    if (!grepl("-", bit)) {
+      sel <- c(sel, as.integer(bit))
+    } else {
+      from_to <- strsplit(bit, "-")[[1]]
+      sel <- c(sel, from_to[1]:from_to[2])
+    }
+  }
+  sort(unique(sel))
+}
diff --git a/tests/testthat/test_stochastic_graphs.R b/tests/testthat/test_stochastic_graphs.R
index 58a2735..01b03e1 100644
--- a/tests/testthat/test_stochastic_graphs.R
+++ b/tests/testthat/test_stochastic_graphs.R
@@ -29,32 +29,39 @@ test_that("stochastic_graph data transforms", {
   if (file.exists(f)) file.remove(f)
   arrow::write_parquet(data, file.path(folder, filename))
 
-  # Aggregate all ages, not by cohort. Should have 1 point per year.
+  # Expecting unaggregated data
 
-  res <- prepare_graph_data(base, touchstone, disease, group, country,
-                            scenario, "deaths", NULL, FALSE)
+  res <- get_graph_data(base, touchstone, disease, group, country,
+                        scenario, "deaths", FALSE)
 
-  expect_equal(nrow(res), 25)    # 5 runs, 5 years
-  expect_equal(res$deaths[res$run_id == 1 & res$year == 2002],
+  expect_equal(nrow(res), 125)    # 5 runs, 5 years, 5 ages
+
+  # Aggregate by year
+
+  res2 <- aggregate_by_year(res, "deaths")
+  expect_equal(nrow(res2), 25)    # 5 runs, 5 years, all age
+
+  expect_equal(res2$deaths[res2$run_id == 1 & res2$year == 2002],
                sum(data$deaths[data$year == 2002 & data$run_id == 1]))
 
   # Select ages
 
-  res <- prepare_graph_data(base, touchstone, disease, group, country,
-                            scenario, "deaths", c(10, 12, 14), FALSE)
+  res2 <- aggregate_by_year(res, "deaths", c(10, 12, 14))
 
-  expect_equal(nrow(res), 25)    # 5 runs, 5 years
-  expect_equal(res$deaths[res$run_id == 1 & res$year == 2003],
+  expect_equal(nrow(res2), 25)    # 5 runs, 5 years
+  expect_equal(res2$deaths[res2$run_id == 1 & res2$year == 2003],
                sum(data$deaths[data$year == 2003 & data$run_id == 1 &
                                  data$age %in% c(10, 12, 14)]))
 
   # By cohort
 
-  res <- prepare_graph_data(base, touchstone, disease, group, country,
-                            scenario, "deaths", c(10, 12, 14), TRUE)
+  res <- get_graph_data(base, touchstone, disease, group, country,
+                        scenario, "deaths", TRUE)
+
+  res2 <- aggregate_by_year(res, "deaths", c(10, 12, 14))
 
-  expect_equal(min(res$year), min(data$year) - max(data$age))
-  expect_equal(max(res$year), max(data$year) - min(data$age))
+  expect_equal(min(res2$year), min(data$year) - max(data$age))
+  expect_equal(max(res2$year), max(data$year) - min(data$age))
 
   # Test graph - we can't really, but just check it doesn't crash.
 
@@ -68,12 +75,12 @@ test_that("stochastic_graph data transforms", {
 
   expect_no_error(stone_stochastic_graph(
     base, touchstone, disease, group, country,
-    scenario, "deaths", scenario2 = scenario))
+    c(scenario, scenario), "deaths"))
 
   # Packit gets called if needed
 
   fake_result <- mockery::mock("fake_result")
-  mockery::stub(stone_stochastic_graph, "prepare_central_data", fake_result)
+  mockery::stub(stone_stochastic_graph, "get_packit_data", fake_result)
 
   expect_no_error(stone_stochastic_graph(
     base, touchstone, disease, group, country,

From 1e6279be6edca51387d0caddd63dbe25cc66dd59 Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Thu, 30 Apr 2026 17:24:06 +0100
Subject: [PATCH 05/14] Cleanup docs

---
 R/stochastic_graphs.R                   | 42 +++++++--------
 man/stone_stochastic_graph.Rd           | 71 ++++++++++++++++---------
 tests/testthat/test_stochastic_graphs.R | 10 ++--
 3 files changed, 71 insertions(+), 52 deletions(-)

diff --git a/R/stochastic_graphs.R b/R/stochastic_graphs.R
index a2c59e7..1b9e38b 100644
--- a/R/stochastic_graphs.R
+++ b/R/stochastic_graphs.R
@@ -6,7 +6,7 @@ filter_string <- function(s, units) {
   } else sprintf("selected %s", units)
 }
 
-##' A feature-rich graph plotting function, which can show the stochastic
+##' A feature-rich-ish graph plotting function, which can show the stochastic
 ##' line for an outcome for different runs, the mean, median and quantiles.
 ##' If two scenarios are specified, then the burden difference between
 ##' scenarios is plotted. Additionally, if multiple groups or multiple
@@ -29,33 +29,29 @@ filter_string <- function(s, units) {
 ##' @importFrom graphics lines par
 ##' @importFrom stats quantile median
 ##' @importFrom grDevices recordPlot
-##' @param base The root older in which the standardised stochastic files are
-##' found. Within it should be folders by touchstone.
+##' @param base The root folder in which the standardised stochastic files are
+##' found. Within it should be folders with touchstone names.
 ##' @param touchstones A touchstone to plot, or a pair  of touchstones to
-##' compare. If a pair, then only one modelling group can be specified.
-##' @param disease The disease for the stochastics to display
-##' @param groups The modelling group to plot data for, or a pair of modelling
-##' groups to be compared, in which case, only touchstone can be specified.
+##' compare. If a pair, then only one modelling group can be specified below.
+##' @param disease The disease to display.
+##' @param groups The modelling group to plot, or a pair of modelling
+##' groups to be compared, in which case, only one touchstone can be specified.
 ##' @param country The country to plot.
-##' @param scenarios A scenario to plot data for, or a pair of scenarios, in
-##' which case the outcome for the first scenario, subtract the second, will be
-##' plotted.
+##' @param scenarios A scenario to plot data for, or a pair of scenarios in
+##' which case we plot the outcome for the first scenario, subtract the second.
 ##' @param outcome The outcome to plot, for example `deaths`, `cases`, `dalys`
-##' or  since 2023, `yll`.
+##' or since 2023, `yll`.
 ##' @param xaxis The default is "time", meaning the x-axis is either calendar
-##' year, or birth cohort, depending on the `by_cohort` parameter, and age is
-##' then aggregated according using the `aggregate_ages` parameter.
-##' Alternatively, set to "age", and aggregate by time using the
-##' `aggregate_years` parameter.
-##' @param filter Filter either ages, years, or birth cohort years to a range,
-##' depending on what `xaxis` is chosen. When `xaxis` is `time`, we are summing
-##' ages, so can filter to `0:4` for example, to only include under 5 burdens.
-##' When `xaxis` is `age` then we want to see burden by age for an aggregated
-##' time period; the filter here is for which years to include, or which
-##' birth cohorts to include, depending on whether `by_cohort` is set. `filter`
-##' can be left as `NULL` to include all ages or years.
+##' year, or birth cohort, depending on the `by_cohort` parameter. This means
+##' and age gets filtered by the `filter` parameter, and then aggregated.
+##' Alternatively, if set to "age", then age will be on the x-axis, and the
+##' `filter` parameter specifies either the years, or the birth cohorts to
+##' select (depending on the `by_cohort` parameter), before aggregation.
+##' @param filter Filter either ages, years, or birth cohort years to a range -
+##' see the comments above on the `xaxis` parametern. The filter is a vector
+##' of ages, or years, or can be NULL to do no filtering.
 ##' @param by_cohort If TRUE, then age is subtracted from year to convert it to
-##' year of birth before aggregating. This is only used when xaxis is "time".
+##' year of birth before aggregating. See the `xaxis` and `filter` parameters.
 ##' @param log If TRUE, then use a logged y-axis.
 ##' @param packit_id If set, then read central burden estimates from a file
 ##' within a packit on the Montagu packit server.
diff --git a/man/stone_stochastic_graph.Rd b/man/stone_stochastic_graph.Rd
index 5e40693..59451ec 100644
--- a/man/stone_stochastic_graph.Rd
+++ b/man/stone_stochastic_graph.Rd
@@ -6,44 +6,57 @@
 \usage{
 stone_stochastic_graph(
   base,
-  touchstone,
+  touchstones,
   disease,
-  group,
+  groups,
   country,
-  scenario,
+  scenarios,
   outcome,
-  ages = NULL,
+  xaxis = "time",
+  filter = NULL,
   by_cohort = FALSE,
   log = FALSE,
   packit_id = NULL,
   packit_file = NULL,
+  include_stochastics = TRUE,
   include_quantiles = TRUE,
   include_mean = TRUE,
-  include_median = TRUE,
-  scenario2 = NULL
+  include_median = TRUE
 )
 }
 \arguments{
-\item{base}{The folder in which the standardised stochastic files are found.}
+\item{base}{The root folder in which the standardised stochastic files are
+found. Within it should be folders with touchstone names.}
 
-\item{touchstone}{The touchstone name (for the graph title)}
+\item{touchstones}{A touchstone to plot, or a pair  of touchstones to
+compare. If a pair, then only one modelling group can be specified below.}
 
-\item{disease}{The disease, used for building the filename and graph title.}
+\item{disease}{The disease to display.}
 
-\item{group}{The modelling group, used in the filename and graph title.}
+\item{groups}{The modelling group to plot, or a pair of modelling
+groups to be compared, in which case, only one touchstone can be specified.}
 
 \item{country}{The country to plot.}
 
-\item{scenario}{The scenario to plot.}
+\item{scenarios}{A scenario to plot data for, or a pair of scenarios in
+which case we plot the outcome for the first scenario, subtract the second.}
 
-\item{outcome}{The outcome to plot, for example \code{deaths}, \code{cases}, \code{dalys} or
-since 2023, \code{yll}.}
+\item{outcome}{The outcome to plot, for example \code{deaths}, \code{cases}, \code{dalys}
+or since 2023, \code{yll}.}
 
-\item{ages}{A vector of one or more ages to be selected and aggregated, or
-if left as NULL, then all ages are used and aggregated.}
+\item{xaxis}{The default is "time", meaning the x-axis is either calendar
+year, or birth cohort, depending on the \code{by_cohort} parameter. This means
+and age gets filtered by the \code{filter} parameter, and then aggregated.
+Alternatively, if set to "age", then age will be on the x-axis, and the
+\code{filter} parameter specifies either the years, or the birth cohorts to
+select (depending on the \code{by_cohort} parameter), before aggregation.}
+
+\item{filter}{Filter either ages, years, or birth cohort years to a range -
+see the comments above on the \code{xaxis} parametern. The filter is a vector
+of ages, or years, or can be NULL to do no filtering.}
 
 \item{by_cohort}{If TRUE, then age is subtracted from year to convert it to
-year of birth before aggregating.}
+year of birth before aggregating. See the \code{xaxis} and \code{filter} parameters.}
 
 \item{log}{If TRUE, then use a logged y-axis.}
 
@@ -54,20 +67,30 @@ within a packit on the Montagu packit server.}
 file providing burden estimates. We expect to find scenario, year, age,
 country, burden_outcome and value fields in the table.}
 
+\item{include_stochastics}{Default TRUE, select whether to draw the
+individual stochastic lines.}
+
 \item{include_quantiles}{Default TRUE, select whether to plot the
 5\% and 95\% quantile lines.}
 
 \item{include_mean}{Default TRUE, select whether to plot the mean.}
 
 \item{include_median}{Default TRUE, select whether to plot the median.}
-
-\item{scenario2}{Default NULL; if set, then the burdens from this
-scenario will be subtracted from those in \code{scenario} - ie, this plots
-an impact graph of applying the second scenario. For many graphs that
-use this, the result will be positive numbers, representing cases
-or deaths averted.}
 }
 \description{
-Draw a stochastic plot showing all the different runs, with the mean,
-median, 5\% and 95\% quantiles shown.
+A feature-rich-ish graph plotting function, which can show the stochastic
+line for an outcome for different runs, the mean, median and quantiles.
+If two scenarios are specified, then the burden difference between
+scenarios is plotted. Additionally, if multiple groups or multiple
+touchstones are specified, then separate graphs with the same scaling are
+plotted to allow comparison.
+}
+\details{
+Graphs can have calendar year or birth cohort on the y-axis for
+time-series plots, in which case age is aggregated and the ages to be
+included can be specified. Alternatively, the x-axis can be age aggregated
+over time, where the calendar years to be aggregated can be specified.
+
+Finally, it can also include a central estimate provided as a file within
+a packit, from the montagu reporting portal.
 }
diff --git a/tests/testthat/test_stochastic_graphs.R b/tests/testthat/test_stochastic_graphs.R
index 01b03e1..ca167cd 100644
--- a/tests/testthat/test_stochastic_graphs.R
+++ b/tests/testthat/test_stochastic_graphs.R
@@ -123,10 +123,10 @@ test_that("Can launch shiny app", {
   })
 })
 
-test_that("Age formats are reasonable", {
-  expect_equal(age_string(NULL), "all ages")
-  expect_equal(age_string(c(5,4,3,2,1,5,4,3,2,1)), "age 1..5")
-  expect_equal(age_string(c(2,4,6,8)), "selected ages")
+test_that("Filter formats are reasonable", {
+  expect_equal(filter_string(NULL, "ages"), "all ages")
+  expect_equal(filter_string(c(5,4,3,2,1,5,4,3,2,1), "ages"), "ages 1..5")
+  expect_equal(filter_string(c(2,4,6,8), "potatoes"), "selected potatoes")
 })
 
 test_that("Parsing central from packit works", {
@@ -144,7 +144,7 @@ test_that("Parsing central from packit works", {
   saveRDS(fake, rds)
 
   fetch_fake <- function(id, file) rds
-  mockery::stub(prepare_central_data, "fetch_packit", fetch_fake)
+  mockery::stub(get_packit_data, "fetch_packit", fetch_fake)
 
   res <- prepare_central_data("123", "file.csv",
     "RFP", "RSV-rout", "deaths", 0:5, TRUE)

From 4bbc072a02ff27181db032fd682c7c974e941915 Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Fri, 1 May 2026 15:01:03 +0100
Subject: [PATCH 06/14] Omit X and X.1 cols in standardise

---
 R/stochastic_files.R | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/R/stochastic_files.R b/R/stochastic_files.R
index 31c13c6..cbcd9e1 100644
--- a/R/stochastic_files.R
+++ b/R/stochastic_files.R
@@ -150,10 +150,19 @@ stone_stochastic_standardise <- function(
         }
       }
 
+      # Note that for MenA, we want to keep the _cwyx outcomes.
+
       if (missing_run_id_fix) {
         if ((!"run_id" %in% names(d)) && (length(index) == 200)) d$run_id <- j
       }
 
+      # Remove columns "X" and "X.1" that have crept in with some of the
+      # inputs saved with row.names
+
+      d[["X"]] <- NULL
+      d[["X.1"]] <- NULL
+
+
 
       # Round to integer, as per guidance. (Not using as.integer, as that
       # has limits on how large numbers can be, so we are just truncating

From e71cf0d4d92b9f7138170d0fbb4b4e04286bbb3c Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Fri, 1 May 2026 18:44:20 +0100
Subject: [PATCH 07/14] Update meta creation

---
 R/stochastic_files.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/R/stochastic_files.R b/R/stochastic_files.R
index cbcd9e1..64fe58e 100644
--- a/R/stochastic_files.R
+++ b/R/stochastic_files.R
@@ -276,8 +276,9 @@ stone_stochastic_make_meta <- function(path) {
     first <- file.path(path, touchstone, folder, files[1])
     ds <- arrow::open_dataset(first)
     outcomes <- ds$schema$names
-    outcomes <- outcomes[tolower(outcomes) %in%
-                           c("cases", "deaths", "dalys", "yll")]
+    outcomes <- outcomes[!outcomes %in% c("run_id", "disease", "year", "age",
+                                          "country", "cohort_size")]
+    outcomes <- sort(unique(tolower(outcomes)))
 
     files <- strsplit(list.files(file.path(path, touchstone, folder)), "_")
 

From 51fe6283183e38c9024bf3ccfe59d92214233b82 Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Fri, 1 May 2026 20:11:15 +0100
Subject: [PATCH 08/14] Plot more outcomes

---
 R/stochastic_graphs.R            |  4 ----
 scripts/process_stoch_202602yf.R | 27 +++++++++++++++++++++++++++
 2 files changed, 27 insertions(+), 4 deletions(-)
 create mode 100644 scripts/process_stoch_202602yf.R

diff --git a/R/stochastic_graphs.R b/R/stochastic_graphs.R
index 1b9e38b..417d19c 100644
--- a/R/stochastic_graphs.R
+++ b/R/stochastic_graphs.R
@@ -103,10 +103,6 @@ stone_stochastic_graph <- function(base,
 
   xaxis <- tolower(xaxis)
 
-  if (!isTRUE(tolower(outcome) %in% c("cases", "dalys", "deaths", "yll"))) {
-    cli::cli_abort("`outcome` must be `cases`, `dalys`, `deaths` or `yll`")
-  }
-
   outcome <- tolower(outcome)
 
   # Fetch the packit data if wanted
diff --git a/scripts/process_stoch_202602yf.R b/scripts/process_stoch_202602yf.R
new file mode 100644
index 0000000..f203742
--- /dev/null
+++ b/scripts/process_stoch_202602yf.R
@@ -0,0 +1,27 @@
+base_in_path <- "//wpia-hn2.hpc.dide.ic.ac.uk/vimc_stochastics_dropbox/latest/202602yf"
+base_out_path <- "//wpia-hn2.hpc.dide.ic.ac.uk/vimc_stochastics/2026202yf"
+
+###############
+# YF
+
+scenarios = c("yf-no-vaccination", "yf-routine-default",
+              "yf-routine-campaign-default")
+
+stoner::stone_stochastic_standardise(
+  group = "IC-Gaythorpe",
+  in_path = file.path(base_in_path, "YF-IC-Gaythorpe"),
+  out_path = file.path(base_out_path, "YF_IC-Gaythorpe"),
+  scenarios = scenarios,
+  files = c("burden_results_stochastic_2026_updates_01_novacc_:index.csv.xz",
+            "burden_results_stochastic_2026_updates_02_default_routine_:index.csv.xz",
+            "burden_results_stochastic_2026_updates_03_default_routine_campaign_:index.csv.xz"),
+  index = 1:200)
+
+stoner::stone_stochastic_standardise(
+  group = "UND-Perkins",
+  in_path = file.path(base_in_path, "YF-UND-Perkins"),
+  out_path = file.path(base_out_path, "YF_UND-Perkins"),
+  scenarios = "yf-routine-campaign-default",
+  files = c("stochastic_burden_est_YF_UND-Perkins_yf-scenario-1-default-Routine_and_Campaign_:index.csv.xz"),
+  index = 1:200)
+

From 1ebd4461e79814140281a89971ae640d84ece95d Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Fri, 1 May 2026 20:13:26 +0100
Subject: [PATCH 09/14] Fix trailing comma in 2023 script

---
 scripts/process_stoch_202310gavi.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/process_stoch_202310gavi.R b/scripts/process_stoch_202310gavi.R
index b2eaaef..49349bd 100644
--- a/scripts/process_stoch_202310gavi.R
+++ b/scripts/process_stoch_202310gavi.R
@@ -323,7 +323,7 @@ stoner::stone_stochastic_standardise(
 
 scenarios = c("malaria-no-vaccination",
               "malaria-rts3-default", "malaria-rts3-rts4-default",
-              "malaria-rts3-bluesky", "malaria-rts3-rts4-bluesky"),
+              "malaria-rts3-bluesky", "malaria-rts3-rts4-bluesky")
 
 stoner::stone_stochastic_standardise(
   group = "UAC-Kakai",

From 5f1facedfa0182b0fa4369f70599c631c7cc4975 Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Fri, 1 May 2026 20:58:12 +0100
Subject: [PATCH 10/14] Fix packet tests

---
 R/stochastic_graphs.R                   | 30 ++++++++++++++-----
 tests/testthat/test_stochastic_graphs.R | 39 ++-----------------------
 2 files changed, 25 insertions(+), 44 deletions(-)

diff --git a/R/stochastic_graphs.R b/R/stochastic_graphs.R
index 417d19c..7a2d7b8 100644
--- a/R/stochastic_graphs.R
+++ b/R/stochastic_graphs.R
@@ -102,9 +102,13 @@ stone_stochastic_graph <- function(base,
   }
 
   xaxis <- tolower(xaxis)
-
   outcome <- tolower(outcome)
 
+  miny <- Inf
+  maxy <- -Inf
+  minx <- Inf
+  maxx <- -Inf
+
   # Fetch the packit data if wanted
 
   if (!is.null(packit_id)) {
@@ -112,8 +116,14 @@ stone_stochastic_graph <- function(base,
                                country, scenarios, outcome, by_cohort)
     if (xaxis == "age") {
       central <- aggregate_by_age(central, outcome, filter)
+      minx <- 0
+      maxx <- 100
     } else {
       central <- aggregate_by_year(central, outcome, filter)
+      miny <- min(central[[outcome]])
+      maxy <- max(central[[outcome]])
+      minx <- min(central$year)
+      maxx <- max(central$year)
     }
   }
 
@@ -131,11 +141,6 @@ stone_stochastic_graph <- function(base,
 
   # Filter and aggregate the data, finding bounds
 
-  miny <- Inf
-  maxy <- -Inf
-  minx <- Inf
-  maxx <- -Inf
-
   for (i in 1:n_graphs) {
     if (xaxis == "age") {
       data[[i]] <- aggregate_by_age(data[[i]], outcome, filter)
@@ -164,7 +169,7 @@ stone_stochastic_graph <- function(base,
   }
 
   if (length(scenarios) == 2) {
-    outcome_ylab <- paste(outcome_ylab, "avered")
+    outcome_ylab <- paste(outcome_ylab, "averted")
     scenario_title <- sprintf("Difference of %s ->\n%s", scenarios[1],
                               scenarios[2])
   }
@@ -226,6 +231,12 @@ stone_stochastic_graph <- function(base,
         lines(x = avgs[[xfield]], y = avgs$q95, col = "#202020", lwd = 2)
       }
     }
+
+    if (!is.null(packit_id)) {
+      lines(x = central[[xfield]], y = central[[outcome]],
+            col = "#2020ff", lwd = 2)
+    }
+
     res[[i]] <- recordPlot()
   }
   res
@@ -300,7 +311,10 @@ get_packit_data <- function(packit_id, packit_file,
     d <- d[order(d$year, d$age), ]
     data[[s]] <- d
   }
-  get_burden_difference(data, outcome)
+  d <- get_burden_difference(data, outcome)
+  d <- as.data.frame(d[, c("year", "age", outcome)])
+  d$run_id <- 1
+  d
 }
 
 ##' Launch a Shiny app to allow interactive plotting of
diff --git a/tests/testthat/test_stochastic_graphs.R b/tests/testthat/test_stochastic_graphs.R
index ca167cd..0496eb5 100644
--- a/tests/testthat/test_stochastic_graphs.R
+++ b/tests/testthat/test_stochastic_graphs.R
@@ -79,7 +79,8 @@ test_that("stochastic_graph data transforms", {
 
   # Packit gets called if needed
 
-  fake_result <- mockery::mock("fake_result")
+  fake_data <- data.frame(year = 2000, age = 10, deaths = 25, run_id = 1)
+  fake_result <- mockery::mock(fake_data)
   mockery::stub(stone_stochastic_graph, "get_packit_data", fake_result)
 
   expect_no_error(stone_stochastic_graph(
@@ -89,7 +90,7 @@ test_that("stochastic_graph data transforms", {
 
   mockery::expect_called(fake_result, 1)
   mockery::expect_args(fake_result, 1, "123", "file.csv", country,
-                       scenario, "deaths", NULL, FALSE)
+                       scenario, "deaths", FALSE)
 
 })
 
@@ -128,37 +129,3 @@ test_that("Filter formats are reasonable", {
   expect_equal(filter_string(c(5,4,3,2,1,5,4,3,2,1), "ages"), "ages 1..5")
   expect_equal(filter_string(c(2,4,6,8), "potatoes"), "selected potatoes")
 })
-
-test_that("Parsing central from packit works", {
-  # Packit gets called if needed
-
-  fake <- data.frame(
-    scenario_type = "RSV-rout", scenario = "RSV-rout",
-    year = c(rep(2000, 4), rep(2001, 4), rep(2000, 4), rep(2001, 4)),
-    age = c(rep(0, 8), rep(1, 8)),
-    country = "RFP",
-    burden_outcome = rep(c("cases", "dalys", "deaths", "yll"), 2),
-    value = 1:16)
-
-  rds <- tempfile(fileext = ".rds")
-  saveRDS(fake, rds)
-
-  fetch_fake <- function(id, file) rds
-  mockery::stub(get_packit_data, "fetch_packit", fetch_fake)
-
-  res <- prepare_central_data("123", "file.csv",
-    "RFP", "RSV-rout", "deaths", 0:5, TRUE)
-
-  # Data in for death is: (year, age, deaths)
-  # 2000, 0, 3
-  # 2000, 1, 11
-  # 2001, 0, 7
-  # 2001, 1, 15
-  # For cohort - this should become...
-  # 1999, 11   (2000 year 1, were born in 1999)
-  # 2000, 18   (2000 year 0, and 2001 year 1 born in 2000)
-  # 2001, 7    (2001 year 0)
-
-  expect_true(all.equal(res$year, c(1999, 2000, 2001)))
-  expect_true(all.equal(res$deaths, c(11, 18, 7)))
-})

From ac7cbb79221a16800ed7364e347b131b57d302ca Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Sat, 2 May 2026 17:07:50 +0100
Subject: [PATCH 11/14] Fix tests

---
 tests/testthat/test_stochastic_files.R  |  32 +++++++
 tests/testthat/test_stochastic_graphs.R | 109 +++++++++++++++++++++++-
 2 files changed, 139 insertions(+), 2 deletions(-)

diff --git a/tests/testthat/test_stochastic_files.R b/tests/testthat/test_stochastic_files.R
index ac19da8..c7b92a2 100644
--- a/tests/testthat/test_stochastic_files.R
+++ b/tests/testthat/test_stochastic_files.R
@@ -330,3 +330,35 @@ test_that("Different file count per scenario is handled", {
   expect_true("north_pole_lurgy_fatalistic_NOR.pq" %in% files)
   expect_true("north_pole_lurgy_fatalistic_LAP.pq" %in% files)
 })
+
+test_that("Can produce metadata", {
+  tmpout <- file.path(tempdir(), "base")
+  d <- fake_data()
+  dir.create(file.path(tmpout, "T1", "elf-piles_G1"), recursive = TRUE)
+  dir.create(file.path(tmpout, "T2", "elf-piles_G2"), recursive = TRUE)
+  arrow::write_parquet(d, file.path(tmpout, "T1", "elf-piles_G1", "G1_S1_LAP.pq"))
+  d$country <- "NPL"
+  arrow::write_parquet(d, file.path(tmpout, "T1", "elf-piles_G1", "G1_S1_NPL.pq"))
+  d$yll <- NULL
+  d$potatoes <- sample(nrow(d))
+  arrow::write_parquet(d, file.path(tmpout, "T2", "elf-piles_G2", "G1_S1_ZAP.pq"))
+  arrow::write_parquet(d, file.path(tmpout, "T2", "elf-piles_G2", "G1_S1_ZIP.pq"))
+  stone_stochastic_make_meta(tmpout)
+
+  expect_true(file.exists(file.path(tmpout, "meta.csv")))
+  meta <- read.csv(file.path(tmpout, "meta.csv"))
+  expect_equal(nrow(meta), 2)
+  expect_true("T1" %in% meta$touchstone)
+  t1 <- meta[meta$touchstone %in% "T1", ]
+  expect_equal(t1$group, "G1")
+  expect_equal(t1$scenario, "S1")
+  expect_equal(t1$countries, "LAP;NPL")
+  expect_equal(t1$outcomes, "cases;dalys;deaths;yll")
+  expect_true("T2" %in% meta$touchstone)
+  t2 <- meta[meta$touchstone %in% "T2", ]
+  expect_equal(t2$group, "G2")
+  expect_equal(t2$scenario, "S1")
+  expect_equal(t2$countries, "ZAP;ZIP")
+  expect_equal(t2$outcomes, "cases;dalys;deaths;potatoes")
+})
+
diff --git a/tests/testthat/test_stochastic_graphs.R b/tests/testthat/test_stochastic_graphs.R
index 0496eb5..4ab224e 100644
--- a/tests/testthat/test_stochastic_graphs.R
+++ b/tests/testthat/test_stochastic_graphs.R
@@ -63,7 +63,23 @@ test_that("stochastic_graph data transforms", {
   expect_equal(min(res2$year), min(data$year) - max(data$age))
   expect_equal(max(res2$year), max(data$year) - min(data$age))
 
-  # Test graph - we can't really, but just check it doesn't crash.
+  # Aggregate by age, for all years
+
+  res2 <- aggregate_by_age(data, "deaths", NULL)
+  expect_equal(nrow(res2), 25)
+  expect_equal(res2$deaths[res2$run_id == 2 & res2$age == 10],
+               sum(data$deaths[data$age == 10 & data$run_id == 2]))
+
+  # Aggregate by age, for some years
+
+  res2 <- aggregate_by_age(data, "deaths", c(2002, 2003))
+  expect_equal(nrow(res2), 25)
+  expect_equal(res2$deaths[res2$run_id == 2 & res2$age == 10],
+               sum(data$deaths[data$age == 10 & data$run_id == 2 &
+                                 data$year %in% c(2002, 2003)]))
+
+  # Test graph - we can't really, but just check it doesn't crash; we've
+  # already tested the functions being called.
 
   expect_no_error(stone_stochastic_graph(
     base, touchstone, disease, group, country,
@@ -80,7 +96,7 @@ test_that("stochastic_graph data transforms", {
   # Packit gets called if needed
 
   fake_data <- data.frame(year = 2000, age = 10, deaths = 25, run_id = 1)
-  fake_result <- mockery::mock(fake_data)
+  fake_result <- mockery::mock(fake_data, cycle = TRUE)
   mockery::stub(stone_stochastic_graph, "get_packit_data", fake_result)
 
   expect_no_error(stone_stochastic_graph(
@@ -92,8 +108,20 @@ test_that("stochastic_graph data transforms", {
   mockery::expect_args(fake_result, 1, "123", "file.csv", country,
                        scenario, "deaths", FALSE)
 
+  expect_no_error(stone_stochastic_graph(
+    base, touchstone, disease, group, country,
+    scenario, "deaths", xaxis = "age"))
+
+  expect_no_error(stone_stochastic_graph(
+    base, touchstone, disease, group, country,
+    scenario, "deaths", xaxis = "age",
+    packit_id = "123",
+    packit_file = "file.csv"))
+
+
 })
 
+
 test_that("stochastic_explorer data_dir handling", {
   expect_error(stochastic_explorer(file.path(tempdir(), "potato", "salad")),
                       "Cannot access the path/mount")
@@ -129,3 +157,80 @@ test_that("Filter formats are reasonable", {
   expect_equal(filter_string(c(5,4,3,2,1,5,4,3,2,1), "ages"), "ages 1..5")
   expect_equal(filter_string(c(2,4,6,8), "potatoes"), "selected potatoes")
 })
+
+test_that("Arguments are tested", {
+
+  expect_error(stone_stochastic_graph(
+    "b", c("T1", "T2", "T3"), "d", "g", "c", "s", "o"),
+    "Only specify one or two touchstones")
+
+  expect_error(stone_stochastic_graph(
+    "b", NULL, "d", "g", "c", "s", "o"),
+    "Only specify one or two touchstones")
+
+  expect_error(stone_stochastic_graph(
+    "b", "t", "d", NULL, "c", "s", "o"),
+    "Only specify one or two modelling groups")
+
+  expect_error(stone_stochastic_graph(
+    "b", "t", "d", c("g1", "g2", "g3"), "c", "s", "o"),
+    "Only specify one or two modelling groups")
+
+  expect_error(stone_stochastic_graph(
+    "b", "t", "d", "g", "c", NULL, "o"),
+    "Only specify one or two scenarios")
+
+  expect_error(stone_stochastic_graph(
+    "b", "t", "d", "g", "c", c("s1", "s2", "s3"), "o"),
+    "Only specify one or two scenarios")
+
+  expect_error(stone_stochastic_graph(
+    "b", c("T1", "T2"), "d", c("g1", "g2"), "c", "s", "o"),
+    "Only one of `touchstones` or `groups` can be plural")
+
+  expect_error(stone_stochastic_graph(
+    "b", c("T1", "T2"), "d", c("g1", "g2"), "c", c("s1", "s2"), "o"),
+    "Only one of `touchstones` or `groups` can be plural")
+
+  expect_error(stone_stochastic_graph(
+    "b", c("T1", "T2"), "d", "g", "c", c("s1", "s2"), "o", xaxis = "potato"),
+    "`xaxis` must be either `time` or `age`")
+
+  expect_error(stone_stochastic_graph(
+    "b", c("T1", "T2"), "d", "g", "c", c("s1", "s2"), "o", xaxis = "age"),
+    "Couldn't find file")
+})
+
+
+test_that("Packit data is arranged correctly", {
+  fake <- data.frame(
+    scenario_type = "RSV-rout", scenario = "RSV-rout",
+    year = c(rep(2000, 4), rep(2001, 4), rep(2000, 4), rep(2001, 4)),
+    age = c(rep(6, 8), rep(7, 8)),
+    country = "RFP",
+    burden_outcome = rep(c("cases", "dalys", "deaths", "yll"), 2),
+    value = 1:16)
+  fake2 <- fake
+  fake2$country <- "POT"
+  fake2$scenario <- "XYZ-rout"
+  fake <- rbind(fake, fake2)
+
+  rds <- tempfile(fileext = ".rds")
+  saveRDS(fake, rds)
+
+  fetch_fake <- function(id, file) rds
+  mockery::stub(get_packit_data, "fetch_packit", fetch_fake)
+
+  res <- get_packit_data("", "", "RFP", "RSV-rout", "cases", TRUE)
+  expect_true(unique(res$run_id) == 1)
+  fake$year <- fake$year - fake$age
+  fake <- fake[fake$country == "RFP", ]
+  fake <- fake[fake$scenario == "RSV-rout", ]
+  fake <- fake[fake$burden_outcome == "cases", ]
+  fake <- fake[order(fake$year, fake$age), ]
+  res <- res[order(res$year, res$age), ]
+  expect_equal(nrow(fake), nrow(res))
+  expect_all_true(fake$year ==res$year)
+  expect_all_true(fake$age == res$age)
+  expect_all_true(fake$value == res$cases)
+})

From 61dde98edc23589803f31dffd2bde049cec7e484 Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Tue, 5 May 2026 12:04:07 +0100
Subject: [PATCH 12/14] Export/document make_meta

---
 NAMESPACE            |  1 +
 R/stochastic_files.R | 20 ++++++++++++++++++++
 2 files changed, 21 insertions(+)

diff --git a/NAMESPACE b/NAMESPACE
index 0fa8961..50b8f44 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -8,6 +8,7 @@ export(stone_load)
 export(stone_stochastic_central)
 export(stone_stochastic_cert_verify)
 export(stone_stochastic_graph)
+export(stone_stochastic_make_meta)
 export(stone_stochastic_process)
 export(stone_stochastic_standardise)
 export(stone_stochastic_upload)
diff --git a/R/stochastic_files.R b/R/stochastic_files.R
index 64fe58e..b27e578 100644
--- a/R/stochastic_files.R
+++ b/R/stochastic_files.R
@@ -269,6 +269,26 @@ stone_stochastic_central <- function(base, touchstone, disease, group,
 
 
 
+##' Create a `meta.csv` file in the root of the standardised
+##' stochastics. The columns contain scalars of `touchstone`,
+##' `disease`, `group`, `scenario` - and for each row, a
+##' semi-colon-separated lists for `countries` and `outcomes`.
+##' This is useful for making the stochastic explorer faster
+##' on startup (otherwise it has to sample all of the files
+##' each time you run it) - and also it is a good general
+##' record of all the stochastic data we have.
+##'
+##' This does mean that we should re-create the meta data
+##' each time we make changes to the standardised stochastic
+##' data though.
+##'
+##' @export
+##' @title Produce `meta.csv` summary of the structure and
+##' content of a standardised stochastic data folder.
+##' @importFrom data.table rbindlist
+##' @importFrom utils write.csv
+##' @param path The root folder of the stochastic data.
+
 stone_stochastic_make_meta <- function(path) {
 
   explore_files <- function(touchstone, folder, disease, group) {

From 76ab53c41ccfae7548a6200ab8ee27c1ac87871a Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Tue, 5 May 2026 12:18:35 +0100
Subject: [PATCH 13/14] Add Rd

---
 man/stone_stochastic_make_meta.Rd | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 man/stone_stochastic_make_meta.Rd

diff --git a/man/stone_stochastic_make_meta.Rd b/man/stone_stochastic_make_meta.Rd
new file mode 100644
index 0000000..6a4b797
--- /dev/null
+++ b/man/stone_stochastic_make_meta.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/stochastic_files.R
+\name{stone_stochastic_make_meta}
+\alias{stone_stochastic_make_meta}
+\title{Produce \code{meta.csv} summary of the structure and
+content of a standardised stochastic data folder.}
+\usage{
+stone_stochastic_make_meta(path)
+}
+\arguments{
+\item{path}{The root folder of the stochastic data.}
+}
+\description{
+Create a \code{meta.csv} file in the root of the standardised
+stochastics. The columns contain scalars of \code{touchstone},
+\code{disease}, \code{group}, \code{scenario} - and for each row, a
+semi-colon-separated lists for \code{countries} and \code{outcomes}.
+This is useful for making the stochastic explorer faster
+on startup (otherwise it has to sample all of the files
+each time you run it) - and also it is a good general
+record of all the stochastic data we have.
+}
+\details{
+This does mean that we should re-create the meta data
+each time we make changes to the standardised stochastic
+data though.
+}

From 09896a5d058760807d1102beceda5cea24d72475 Mon Sep 17 00:00:00 2001
From: Wes Hinsley <w.hinsley@imperial.ac.uk>
Date: Wed, 6 May 2026 13:43:06 +0100
Subject: [PATCH 14/14] Update vignette

---
 scripts/imports.R                             | 1498 +++++++++++++++++
 scripts/process_stoch_201710gavi.R            |    4 +-
 scripts/process_stoch_201910gavi.R            |    4 +-
 scripts/process_stoch_202110gavi.R            |   12 +-
 scripts/process_stoch_202310gavi.R            |    2 +
 scripts/process_stoch_202409malaria.R         |    2 +
 scripts/process_stoch_202602yf.R              |    1 +
 vignettes/Stochastics.Rmd                     |  489 ++++--
 vignettes/figures/stoch_burden_diff.png       |  Bin 0 -> 16700 bytes
 vignettes/figures/stoch_byage.png             |  Bin 0 -> 14790 bytes
 vignettes/figures/stoch_byage_filter.png      |  Bin 0 -> 15355 bytes
 .../figures/stoch_byage_filter_cohort.png     |  Bin 0 -> 17344 bytes
 vignettes/figures/stoch_cohort.png            |  Bin 0 -> 15181 bytes
 vignettes/figures/stoch_compare_mg_1.png      |  Bin 0 -> 17474 bytes
 vignettes/figures/stoch_compare_mg_2.png      |  Bin 0 -> 8607 bytes
 vignettes/figures/stoch_compare_ts_1.png      |  Bin 0 -> 12503 bytes
 vignettes/figures/stoch_compare_ts_2.png      |  Bin 0 -> 19676 bytes
 vignettes/figures/stoch_example_1.png         |  Bin 27811 -> 20663 bytes
 vignettes/figures/stoch_explorer.png          |  Bin 0 -> 178468 bytes
 vignettes/figures/stoch_packit.png            |  Bin 0 -> 18479 bytes
 vignettes/figures/stoch_packit_diff.png       |  Bin 0 -> 17821 bytes
 vignettes/figures/stoch_u5.png                |  Bin 0 -> 23747 bytes
 22 files changed, 1884 insertions(+), 128 deletions(-)
 create mode 100644 scripts/imports.R
 create mode 100644 vignettes/figures/stoch_burden_diff.png
 create mode 100644 vignettes/figures/stoch_byage.png
 create mode 100644 vignettes/figures/stoch_byage_filter.png
 create mode 100644 vignettes/figures/stoch_byage_filter_cohort.png
 create mode 100644 vignettes/figures/stoch_cohort.png
 create mode 100644 vignettes/figures/stoch_compare_mg_1.png
 create mode 100644 vignettes/figures/stoch_compare_mg_2.png
 create mode 100644 vignettes/figures/stoch_compare_ts_1.png
 create mode 100644 vignettes/figures/stoch_compare_ts_2.png
 create mode 100644 vignettes/figures/stoch_explorer.png
 create mode 100644 vignettes/figures/stoch_packit.png
 create mode 100644 vignettes/figures/stoch_packit_diff.png
 create mode 100644 vignettes/figures/stoch_u5.png

diff --git a/scripts/imports.R b/scripts/imports.R
new file mode 100644
index 0000000..89472c8
--- /dev/null
+++ b/scripts/imports.R
@@ -0,0 +1,1498 @@
+get_con <- function() {
+  vault <- vaultr::vault_client(login = "github")
+  password <- vault$read("/secret/vimc/database/production/users/readonly")$password
+  DBI::dbConnect(RPostgres::Postgres(),
+                 dbname = "montagu",
+                 host = "montagu.vaccineimpact.org",
+                 port = 5432, password = password,
+                 user = "readonly")
+}
+
+get_annex <- function() {
+  vault <- vaultr::vault_client(login = "github")
+  password <- vault$read("/secret/vimc/annex/users/vimc")$password
+  DBI::dbConnect(RPostgres::Postgres(),
+                 dbname = "montagu",
+                 host = "annex.montagu.dide.ic.ac.uk",
+                 port = 15432,
+                 password = password,
+                 user = "vimc")
+
+}
+
+
+upload4 <- function(out_path, modelling_group, disease, touchstone,
+                    con, annex) {
+  stub <- file.path(out_path, sprintf("%s_%s_", modelling_group, disease))
+
+  stoner::stone_stochastic_upload(
+    paste0(stub, "calendar.qs"),
+    con, annex, modelling_group, disease, touchstone,
+    is_cohort = FALSE, is_under5 = FALSE)
+
+  stoner::stone_stochastic_upload(
+    paste0(stub, "calendar_u5.qs"),
+    con, annex, modelling_group, disease, touchstone,
+    is_cohort = FALSE, is_under5 = TRUE)
+
+  stoner::stone_stochastic_upload(
+    paste0(stub, "cohort.qs"),
+    con, annex, modelling_group, disease, touchstone,
+    is_cohort = TRUE, is_under5 = FALSE)
+
+  stoner::stone_stochastic_upload(
+    paste0(stub, "cohort_u5.qs"),
+    con, annex, modelling_group, disease, touchstone,
+    is_cohort = TRUE, is_under5 = TRUE)
+}
+
+do_stochastics_2024 <- function() {
+  con <- get_con()
+  annex <- get_annex()
+  in_path <- "D:/Dropbox (SPH Imperial College)/File requests/latest/202409malaria-1/"
+  out_path <- "D:/stochastic_2024/"
+
+  # UAC-Kakai
+
+  stoner::stone_stochastic_process(con, "UAC-Glele_Kakai", "Malaria", "202409malaria-1",
+    c("malaria-no-vaccination",
+      "malaria-r3-r4-default", "malaria-rts3-rts4-default"),
+    file.path(in_path, "Malaria-UAC-Kakai"),
+     c("Stochastic_Burden_Estimates_Glele_Kakai_No_Vaccine_:index.csv.xz",
+       "Stochastic_Burden_Estimates_Glele_Kakai_Default_r34_:index.csv.xz",
+       "Stochastic_Burden_Estimates_Glele_Kakai_Default_rts34_:index.csv.xz"),
+       "", 1, 200, out_path, bypass_cert_check = TRUE)
+
+  upload4(out_path, "UAC-Glele_Kakai", "Malaria", "202409malaria-1", con, annex)
+
+  # IC-Okell
+  in_path <- "D:/Dropbox (SPH Imperial College)/File requests/latest/202409malaria-1/Malaria-IC-Okell/"
+  out_path <- "D:/stochastic_2024/"
+  stoner::stone_stochastic_process(con,
+    "IC-Okell", "Malaria", "202409malaria-1",
+  c("malaria-no-vaccination",
+    "malaria-r3-r4-default", "malaria-rts3-rts4-default"),
+   in_path,
+   c("stochastic-burden-est-no-vaccination_:index Lydia Haile.csv.xz",
+     "stochastic-burden-est-malaria-r3-r4-default_:index Lydia Haile.csv.xz",
+     "stochastic-burden-est-malaria-rts3-rts4-default_:index Lydia Haile.csv.xz"),
+          "", 1, 200, out_path, bypass_cert_check = TRUE)
+
+  upload4(out_path, "IC-Okell", "Malaria", "202409malaria-1", con, annex)
+
+  # TKI-Penny
+
+  in_path <- "D:/Dropbox (SPH Imperial College)/File requests/latest/202409malaria-1/Malaria-TKI-Penny/"
+
+  stoner::stone_stochastic_process(con,
+    "TKI-Penny", "Malaria", "202409malaria-1",
+    c("malaria-no-vaccination",
+      "malaria-r3-r4-default",
+      "malaria-rts3-rts4-default"),
+      in_path,
+    c("stochastic_burden_est_malaria_:index_novaccine Josephine Malinga.csv.xz",
+      "stochastic_burden_est_malaria_:index_r21_d4_default Josephine Maasd   linga.csv.xz",
+      "stochastic_burden_est_malaria_:index_rtss_d4_default Josephine asd fgMalinga.csv.xz"),
+      "", 1, 31, out_path, bypass_cert_check = TRUE)
+
+  upload4(out_path, "TKI-Penny", "Malaria", "202409malaria-1", con, annex)
+
+}
+
+
+do_stochastics_2023 <- function() {
+  con <- get_con()
+  annex <- get_annex()
+  in_path <- "D:/Dropbox (SPH Imperial College)/File requests/latest/202310gavi/"
+  out_path <- "D:/stochastic_2023/"
+
+  ###################### HepB #########################################
+
+  stoner::stone_stochastic_process(con, "Li", "HepB", "202310gavi-4",
+                                   c(  "hepb-hepb3-bd-bluesky",
+                                       "hepb-hepb3-bd-default",
+                                       "hepb-hepb3-bd-ia2030",
+                                       "hepb-hepb3-bluesky",
+                                       "hepb-hepb3-default",
+                                       "hepb-hepb3-ia2030",
+                                       "hepb-no-vaccination"
+                                   ),
+                                   file.path(in_path, "HepB-Li"),
+                                   paste0(":scenario:index.csv.xz"),
+                                   "cert156", 1, 200, out_path, NULL,
+                                   "deaths", "cases", "dalys")
+
+  upload4(out_path, "Li", "HepB", "202310gavi-4", con, annex)
+
+  stoner::stone_stochastic_process(
+    con, "IC-Hallett", "HepB", "202310gavi-4",
+    c(  "hepb-hepb3-bd-bluesky",
+        "hepb-hepb3-bd-default",
+        "hepb-hepb3-bd-ia2030",
+        "hepb-hepb3-bluesky",
+        "hepb-hepb3-default",
+        "hepb-hepb3-ia2030",
+        "hepb-no-vaccination"),
+    file.path(in_path, "HepB-IC-Hallett"),
+    c("stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_bd_bluesky_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_bd_default_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_bd_ia2030_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_bluesky_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_default_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_ia2030_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_no_vaccination_:index.csv.xz"),
+    "", 1, 200, out_path, NULL,
+    "deaths", "cases", "dalys", bypass_cert_check = TRUE )
+
+  upload4(out_path, "IC-Hallett", "HepB", "202310gavi-4", con, annex)
+
+
+  stoner::stone_stochastic_process(
+    con, "IC-Hallett", "HepB", "202310gavi-4",
+    c(  "hepb-hepb3-bd-bluesky",
+        "hepb-hepb3-bd-default",
+        "hepb-hepb3-bd-ia2030",
+        "hepb-hepb3-bluesky",
+        "hepb-hepb3-default",
+        "hepb-hepb3-ia2030",
+        "hepb-no-vaccination"),
+    file.path(in_path, "HepB-IC-Hallett"),
+    c("stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_bd_bluesky_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_bd_default_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_bd_ia2030_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_bluesky_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_default_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_hepb3_ia2030_:index.csv.xz",
+      "stochastic_burden_est_HepB-IC-Hallett_hepb_no_vaccination_:index.csv.xz"),
+    "", 1, 200, out_path, NULL,
+    "deaths", "cases", "dalys", bypass_cert_check = TRUE )
+
+  upload4(out_path, "IC-Hallett", "HepB", "202310gavi-4", con, annex)
+
+
+
+  ###################### YF #########################################
+
+  in_path <- "//fi--didenas1-app/Test/YF"
+  out_path <- in_path
+
+  stoner::stone_stochastic_process(
+    con, "IC-Garske", "YF", "202310gavi-4",
+    c("yf-no-vaccination",
+      "yf-routine-bluesky",
+      "yf-routine-campaign-bluesky",
+      "yf-routine-campaign-default",
+      "yf-routine-campaign-ia2030",
+      "yf-routine-default",
+      "yf-routine-ia2030"),
+    in_path,
+    "burden_results_stochastic_202310gavi-3_:scenario Keith Fraser.csv.xz",
+    "", NA, NA, out_path, outcomes = list(cases = "cases", deaths = "deaths",
+                                          dalys = "dalys", yll = "yll"), bypass_cert_check = TRUE )
+
+  upload4(out_path, "IC-Garske", "YF", "202310gavi-4", con, annex)
+
+
+  stoner::stone_stochastic_process(
+    con, "UND-Perkins", "YF", "202310gavi-7",
+    c("yf-no-vaccination",
+      "yf-routine-bluesky",
+      "yf-routine-campaign-bluesky",
+      "yf-routine-campaign-default",
+      "yf-routine-campaign-ia2030",
+      "yf-routine-default",
+      "yf-routine-ia2030"),
+    file.path(in_path, "YF-UND-Perkins"),
+    c("stochastic_burden_est_YF_UND-Perkins_yf-no_vaccination_:index.csv.xz",
+      "stochastic_burden_est_YF_UND-Perkins_yf-routine_bluesky_:index.csv.xz",
+      "stochastic_burden_est_YF_UND-Perkins_yf-routine_campaign_bluesky_:index.csv.xz",
+      "stochastic_burden_est_YF_UND-Perkins_yf-routine_campaign_default_:index.csv.xz",
+      "stochastic_burden_est_YF_UND-Perkins_yf-routine_campaign_ia2030_:index.csv.xz",
+      "stochastic_burden_est_YF_UND-Perkins_yf-routine_default_:index.csv.xz",
+      "stochastic_burden_est_YF_UND-Perkins_yf-routine_ia2030_:index.csv.xz"),
+
+    "", 1, 200, out_path, outcomes = list(cases = "cases", deaths = "deaths",
+                                          dalys = "dalys", yll = "yll"), bypass_cert_check = TRUE )
+
+  upload4(out_path, "UND-Perkins", "YF", "202310gavi-7", con, annex)
+
+
+
+  ###################### HPV #########################################
+
+  stoner::stone_stochastic_process(
+    con, "Harvard-Sweet", "HPV", "202310gavi-4",
+    c("hpv-no-vaccination",
+      "hpv-campaign-default",
+      "hpv-campaign-bluesky",
+      "hpv-campaign-routine-bluesky",
+      "hpv-campaign-routine-default",
+      "hpv-campaign-default_transition_hpv_1d",
+      "hpv-campaign-routine-default_transition_hpv_1d",
+      "hpv-campaign-ia2030",
+      "hpv-campaign-routine-ia2030"),
+    file.path(in_path, ""),
+    c("stochastic-burden-est.coverage_202310gavi-4_:scenario_run_:index Allison Portnoy.csv.xz"),
+    "", 1, 200, out_path, NULL,
+    "deaths", "cases", "dalys", "yll", bypass_cert_check = TRUE )
+
+  upload4(out_path, "Harvard-Sweet", "HPV", "202310gavi-4", con, annex)
+
+  in_path <- "//fi--didenas1-app/Test/hpv-jit"
+  stoner::stone_stochastic_process(
+    con, "LSHTM-Jit", "HPV", "202310gavi-7",
+    c("hpv-no-vaccination",
+      "hpv-campaign-default",
+      "hpv-campaign-bluesky",
+      "hpv-campaign-routine-bluesky",
+      "hpv-campaign-routine-default",
+      "hpv-campaign-default_transition_hpv_1d",
+      "hpv-campaign-routine-default_transition_hpv_1d",
+      "hpv-campaign-ia2030",
+      "hpv-campaign-routine-ia2030"),
+    file.path(in_path, ""),
+    c("stochastic-burden-novaccination_all_202310gavi-7_hpv-no-vaccination Kaja Abbas.csv.xz",
+      "stochastic-burden-vaccination_all_202310gavi-7_hpv-campaign-default Kaja Abbas.csv.xz",
+      "stochastic-burden-vaccination_all_202310gavi-7_hpv-campaign-bluesky Kaja Abbas.csv.xz",
+      "stochastic-burden-vaccination_all_202310gavi-7_hpv-campaign-routine-bluesky Kaja Abbas.csv.xz",
+      "stochastic-burden-vaccination_all_202310gavi-7_hpv-campaign-routine-default Kaja Abbas.csv.xz",
+      "stochastic-burden-vaccination_all_202310gavi-7_hpv-campaign-default_transition_hpv_1d Kaja Abbas.csv.xz",
+      "stochastic-burden-vaccination_all_202310gavi-7_hpv-campaign-routine-default_transition_hpv_1d Kaja Abbas.csv.xz",
+      "stochastic-burden-vaccination_all_202310gavi-7_hpv-campaign-ia2030 Kaja Abbas.csv.xz",
+      "stochastic-burden-vaccination_all_202310gavi-7_hpv-campaign-routine-ia2030 Kaja Abbas.csv.xz"),
+    "", NA, NA, in_path, NULL,
+    "deaths", "cases", "dalys", "yll", bypass_cert_check = TRUE )
+
+  upload4(in_path, "LSHTM-Jit", "HPV", "202310gavi-7", con, annex)
+
+
+  ###################### Cholera #########################################
+
+  in_path <- "D:/Dropbox (SPH Imperial College)/File requests/latest/202310gavi/Cholera-IVI-Kim"
+  out_path <- "D:/stochastic_2023/"
+  stoner::stone_stochastic_process(con,
+                                   "IVI-Kim", "Cholera", "202310gavi-7",
+                                   c("cholera-no-vaccination", "cholera-ocv1-default", "cholera-ocv1-ocv2-default"),
+                                   file.path(in_path),
+                                   c("stoch_Cholera_novacc_20250704T130601 Jong-Hoon Kim.csv.xz",
+                                     "stoch_Cholera_campaign_default_ocv1_20250704T130601 Jong-Hoon Kim.csv.xz",
+                                     "stoch_Cholera_campaign_default_ocv12_20250704T130601 Jong-Hoon Kim.csv.xz"),
+
+                                   "", NA, NA, out_path, NULL, bypass_cert_check = TRUE )
+  upload4(out_path, "IVI-Kim", "Cholera", "202310gavi-7", con, annex)
+
+
+  in_path <- "K:/jhu-chol"
+  stoner::stone_stochastic_process(con,
+                                   "JHU-Lee", "Cholera", "202310gavi-7",
+                                   c("cholera-no-vaccination", "cholera-ocv1-default", "cholera-ocv1-ocv2-default"),
+                                   file.path(in_path),
+                                   c("stochastic-burden-template.202310gavi-4.Cholera_standard_template.529.no-vaccination Christina Alam.csv.xz",
+                                     "stochastic-burden-template.202310gavi-4.Cholera_standard_template.529.ocv1-default_one Christina Alam.csv.xz",
+                                     "stochastic-burden-template.202310gavi-4.Cholera_standard_template.529.ocv1-ocv2-default_two Christina Alam.csv.xz"),
+                                   "cert172 Christina Alam", NA, NA, out_path, NULL,
+                                   "deaths", "cases", "dalys", "yll", bypass_cert_check = TRUE )
+  upload4(out_path, "JHU-Lee", "Cholera", "202310gavi-4", con, annex)
+
+
+  ###################### COVID #########################################
+
+  in_path <- "//fi--didenas1-app/Test/covid-liu"
+  stoner::stone_stochastic_process(con,
+                                   "LSHTM-Liu", "COVID", "202310gavi-7",
+                                   c("covid-no-vaccination",
+                                     "covid-no-vaccination_severe",
+                                     "covid-primary-bluesky",
+                                     "covid-primary-bluesky_severe",
+                                     "covid-primary-booster-bluesky",
+                                     "covid-primary-booster-bluesky_severe",
+                                     "covid-primary-booster-default",
+                                     "covid-primary-booster-default_severe",
+                                     "covid-primary-default",
+                                     "covid-primary-default_severe"),
+                                   in_path,
+                                   c("stochastic_burden_est_covid-LSHTM-Liu_covid-no-vaccination Yang Liu.csv.xz",
+                                     "stochastic_burden_est_covid-LSHTM-Liu_covid-no-vaccination_severe Yang Liu.csv.xz",
+                                     "stochastic_burden_est_covid-LSHTM-Liu_covid-primary-bluesky Yang Liu.csv.xz",
+                                     "stochastic_burden_est_covid-LSHTM-Liu_covid-primary-bluesky_severe Yang Liu.csv.xz",
+                                     "stochastic_burden_est_covid-LSHTM-Liu_covid-primary-booster-bluesky Yang Liu.csv.xz",
+                                     "stochastic_burden_est_covid-LSHTM-Liu_covid-primary-booster-bluesky_severe Yang Liu.csv.xz",
+                                     "stochastic_burden_est_covid-LSHTM-Liu_covid-primary-booster-default Yang Liu.csv.xz",
+                                     "stochastic_burden_est_covid-LSHTM-Liu_covid-primary-booster-default_severe Yang Liu.csv.xz",
+                                     "stochastic_burden_est_covid-LSHTM-Liu_covid-primary-default Yang Liu.csv.xz",
+                                     "stochastic_burden_est_covid-LSHTM-Liu_covid-primary-default_severe Yang Liu.csv.xz"),
+                                   "", NA, NA, in_path, NULL, "deaths", "cases", "dalys", "yll", bypass_cert_check = TRUE)
+  upload4(in_path, "LSHTM-Liu", "COVID", "202310gavi-7", con, annex)
+
+
+  in_path <- "//fi--didenas1-app/Test/cov"
+  stoner::stone_stochastic_process(con,
+                                   "IC-Ghani", "COVID", "202310gavi-7",
+                                   c("covid-no-vaccination",
+                                     "covid-no-vaccination_severe",
+                                     "covid-primary-bluesky",
+                                     "covid-primary-bluesky_severe",
+                                     "covid-primary-booster-bluesky",
+                                     "covid-primary-booster-bluesky_severe",
+                                     "covid-primary-booster-default",
+                                     "covid-primary-booster-default_severe",
+                                     "covid-primary-default",
+                                     "covid-primary-default_severe"),
+                                   in_path,
+                                   c("covid-no-vaccination Gemma Gilani_:index.csv.xz",
+                                     "covid-no-vaccination_severe Gemma Gilani_:index.csv.xz",
+                                     "covid-primary-bluesky Daniela Olivera_:index.csv.xz",
+                                     "covid-primary-bluesky_severe Daniela Olivera_:index.csv.xz",
+                                     "covid-primary-booster-bluesky Daniela Olivera_:index.csv.xz",
+                                     "covid-primary-booster-bluesky_severe Daniela Olivera_:index.csv.xz",
+                                     "covid-primary-booster-default Gemma Gilani_:index.csv.xz",
+                                     "covid-primary-booster-default_severe Gemma Gilani_:index.csv.xz",
+                                     "covid-primary-default Gemma Gilani_:index.csv.xz",
+                                     "covid-primary-default_severe Gemma Gilani_:index.csv.xz"),
+                                   "", 1, 200, in_path, NULL, "deaths", "cases", "dalys", "yll", bypass_cert_check = TRUE)
+  upload4(in_path, "IC-Ghani", "COVID", "202310gavi-7", con, annex)
+
+
+
+
+  ###################### Measles #########################################
+
+  # Updated June 2025
+
+  stoner::stone_stochastic_process(
+    con,
+    "PSU-Ferrari", "Measles", "202310gavi-7",
+    c("measles-no-vaccination",
+      "measles-mcv1-bluesky",
+      "measles-mcv1-default",
+      "measles-mcv1-ia2030",
+      "measles-mcv1-mcv2-bluesky",
+      "measles-mcv1-mcv2-campaign-bluesky",
+      "measles-mcv1-mcv2-campaign-default",
+      "measles-mcv1-mcv2-campaign-default_under5sia",
+      "measles-mcv1-mcv2-campaign-default_update",
+      "measles-mcv1-mcv2-campaign-ia2030",
+      "measles-mcv1-mcv2-default",
+      "measles-mcv1-mcv2-ia2030"),
+    file.path(in_path),
+    c("no-vaccination.202310gavi-6.Measles_PSU-Ferrari_standard.csv.xz",
+      "mcv1-bluesky.202310gavi-6.Measles_PSU-Ferrari_standard.csv.xz",
+      "mcv1-default.202310gavi-6.Measles_PSU-Ferrari_standard.csv.xz",
+      "mcv1-ia2030.202310gavi-6.Measles_PSU-Ferrari_standard.csv.xz",
+      "mcv1-mcv2-bluesky.202310gavi-6.Measles_PSU-Ferrari_standard.csv.xz",
+      "mcv1-mcv2-campaign-bluesky.202310gavi-6.Measles_PSU-Ferrari_standard.csv.xz",
+      "mcv1-mcv2-campaign-default.202310gavi-6.Measles_PSU-Ferrari_standard.csv.xz",
+      "measles-mcv1-mcv2-campaign-default_under5sia_PSU_Ferrari_Stochastic_Runs_Revised_05102025.csv.xz",
+      "measles-mcv1-mcv2-campaign-default_update_PSU_Ferrari_Stochastic_Runs_Revised_05102025.csv.xz",
+      "mcv1-mcv2-campaign-ia2030.202310gavi-6.Measles_PSU-Ferrari_standard.csv.xz",
+      "mcv1-mcv2-default.202310gavi-6.Measles_PSU-Ferrari_standard.csv.xz",
+      "mcv1-mcv2-ia2030.202310gavi-6.Measles_PSU-Ferrari_standard.csv.xz"),
+      "", NA, NA, out_path, bypass_cert_check = TRUE)
+
+  upload4(out_path, "PSU-Ferrari", "Measles", "202310gavi-7", con, annex)
+
+
+  stoner::stone_stochastic_process(
+    con,
+    "LSHTM-Jit", "Measles", "202310gavi-7",
+    c("measles-no-vaccination",
+      "measles-mcv1-bluesky",
+      "measles-mcv1-default",
+      "measles-mcv1-ia2030",
+      "measles-mcv1-mcv2-bluesky",
+      "measles-mcv1-mcv2-campaign-bluesky",
+      "measles-mcv1-mcv2-campaign-default",
+      "measles-mcv1-mcv2-campaign-default_under5sia",
+      "measles-mcv1-mcv2-campaign-default_update",
+      "measles-mcv1-mcv2-campaign-ia2030",
+      "measles-mcv1-mcv2-default",
+      "measles-mcv1-mcv2-ia2030"),
+    in_path,
+    c("stochastic_burden_estimate_measles-LSHTM-Jit-no-vaccination Han Fu.csv.xz",
+      "stochastic_burden_estimate_measles-LSHTM-Jit-mcv1-bluesky Han Fu.csv.xz",
+      "stochastic_burden_estimate_measles-LSHTM-Jit-mcv1-default Han Fu.csv.xz",
+      "stochastic_burden_estimate_measles-LSHTM-Jit-mcv1-ia2030 Han Fu.csv.xz",
+      "stochastic_burden_estimate_measles-LSHTM-Jit-mcv1-mcv2-bluesky Han Fu.csv.xz",
+      "stochastic_burden_estimate_measles-LSHTM-Jit-mcv1-mcv2-campaign-bluesky Han Fu.csv.xz",
+      "stochastic_burden_estimate_measles-LSHTM-Jit-mcv1-mcv2-campaign-default.csv.xz",
+      "stochastic_burden_estimate_measles-LSHTM-Jit-mcv1-mcv2-campaign-default_under5sia.csv.xz",
+      "stochastic_burden_estimate_measles-LSHTM-Jit-mcv1-mcv2-campaign-default_update.csv.xz",
+      "stochastic_burden_estimate_measles-LSHTM-Jit-mcv1-mcv2-campaign-ia2030 Han Fu.csv.xz",
+      "stochastic_burden_estimate_measles-LSHTM-Jit-mcv1-mcv2-default Han Fu.csv.xz",
+      "stochastic_burden_estimate_measles-LSHTM-Jit-mcv1-mcv2-ia2030 Han Fu.csv.xz"),
+    "", NA, NA, out_path, bypass_cert_check = TRUE)
+  upload4(out_path, "LSHTM-Jit", "Measles", "202310gavi-7", con, annex)
+
+
+  ###################### Rubella #########################################
+
+  in_path <- "//fi--didenas1-app/Test/rub-uga"
+  stoner::stone_stochastic_process(con,
+                                   "JHU-Lessler", "Rubella", "202310gavi-7",
+                                   c("rubella-no-vaccination",
+                                     "rubella-campaign-default",
+                                     "rubella-campaign-bluesky",
+                                     "rubella-campaign-rcv1-rcv2-bluesky",
+                                     "rubella-campaign-rcv1-bluesky",
+                                     "rubella-campaign-rcv1-rcv2-default",
+                                     "rubella-campaign-rcv1-default",
+                                     "rubella-campaign-ia2030",
+                                     "rubella-campaign-rcv1-rcv2-ia2030",
+                                     "rubella-campaign-rcv1-ia2030"),
+                                   in_path,
+                                   c("stochastic_burden_est-rubella-no-vaccination_:index Amy Winter.csv.xz",
+                                     "stochastic_burden_est-rubella-campaign-default_:index Amy Winter.csv.xz",
+                                     "stochastic_burden_est-rubella-campaign-bluesky_:index Amy Winter.csv.xz",
+                                     "stochastic_burden_est-rubella-campaign-rcv1-rcv2-bluesky_:index Amy Winter.csv.xz",
+                                     "stochastic_burden_est-rubella-campaign-rcv1-bluesky_:index Amy Winter.csv.xz",
+                                     "stochastic_burden_est-rubella-campaign-rcv1-rcv2-default_:index Amy Winter.csv.xz",
+                                     "stochastic_burden_est-rubella-campaign-rcv1-default_:index Amy Winter.csv.xz",
+                                     "stochastic_burden_est-rubella-campaign-ia2030_:index Amy Winter.csv.xz",
+                                     "stochastic_burden_est-rubella-campaign-rcv1-rcv2-ia2030_:index Amy Winter.csv.xz",
+                                     "stochastic_burden_est-rubella-campaign-rcv1-ia2030_:index Amy Winter.csv.xz"),
+                                   "", 1, 12, in_path, NULL,
+                                   "rubella_deaths_congenital",
+                                   "rubella_cases_congenital", "dalys", "yll", bypass_cert_check = TRUE)
+  upload4(in_path, "JHU-Lessler", "Rubella", "202310gavi-7", con, annex)
+
+  in_path <- "//fi--didenas1-app/Test/rub-ukhsa"
+  stoner::stone_stochastic_process(con,
+                                   "PHE-Vynnycky", "Rubella", "202310gavi-7",
+                                   c("rubella-no-vaccination",
+                                     "rubella-campaign-default",
+                                     "rubella-campaign-bluesky",
+                                     "rubella-campaign-rcv1-rcv2-bluesky",
+                                     "rubella-campaign-rcv1-bluesky",
+                                     "rubella-campaign-rcv1-rcv2-default",
+                                     "rubella-campaign-rcv1-default",
+                                     "rubella-campaign-ia2030",
+                                     "rubella-campaign-rcv1-rcv2-ia2030",
+                                     "rubella-campaign-rcv1-ia2030"),
+                                   in_path,
+                                   c("imp.c:indexs401.csv.xz",
+                                     "stochastic_burden_est_rubella_Vynnycky_rubella-campaign-default_:index.csv.xz",
+                                     "stochastic_burden_est_rubella_Vynnycky_rubella-campaign-bluesky_:index.csv.xz",
+                                     "Vynnycky-camp-rcv1-rcv2-bluesky_country:index.csv.xz",
+                                     "Vynnycky-camp-rcv1-bluesky_country:index.csv.xz",
+                                     "stochastic_burden_est_rubella_Vynnycky_rubella-campaign-rcv1-rcv2-default_:index.csv.xz",
+                                     "stochastic_burden_est_rubella_Vynnycky_rubella-campaign-rcv1-default_:index.csv.xz",
+                                     "stochastic_burden_est_rubella_Vynnycky_rubella-campaign-ia2030_:index.csv.xz",
+                                     "Vynnycky-camp-rcv1-rcv2-ia2030_country:index.csv.xz",
+                                     "Vynnycky-camp-rcv1-ia2030_country:index.csv.xz"),
+                                   "", 1, 117, in_path, NULL,
+                                   "rubella_deaths_congenital",
+                                   "rubella_cases_congenital", "dalys", "yll", bypass_cert_check = TRUE)
+  upload4(in_path, "PHE-Vynnycky", "Rubella", "202310gavi-7", con, annex)
+
+
+
+  ###################### Typhoid #########################################
+
+  in_path <- "//fi--didenas1-app/Test/ivi-typhoid"
+  stoner::stone_stochastic_process(con, "IVI-Kim", "Typhoid", "202310gavi-7",
+                                   c("typhoid-no-vaccination",
+                                     "typhoid-campaign-default", "typhoid-campaign-routine-default",
+                                     "typhoid-campaign-bluesky",  "typhoid-campaign-routine-bluesky"),
+                                   in_path,
+                                   c("stoch_Typhoid_novacc_20240314T233526 Jong-Hoon Kim.csv.xz",
+                                     "stoch_Typhoid_campaign_default_20240314T233526 Jong-Hoon Kim.csv.xz",
+                                     "stoch_Typhoid_campaign_routine_default_20240314T233526 Jong-Hoon Kim.csv.xz",
+                                     "stoch_Typhoid_campaign_bluesky_20240314T233526 Jong-Hoon Kim.csv.xz",
+                                     "stoch_Typhoid_campaign_routine_bluesky_20240314T233526 Jong-Hoon Kim.csv.xz"),
+                                   "", NA, NA, in_path, bypass_cert_check = TRUE)
+  upload4(in_path, "IVI-Kim", "Typhoid", "202310gavi-7", con, annex)
+
+
+  ###################### Malaria R2 #########################################
+
+  in_path <- "//fi--didenas1-app/Test/uac-malaria"
+  stoner::stone_stochastic_process(con, "UAC-Glele_Kakai", "Malaria", "202310gavi-7",
+                                   c("malaria-no-vaccination",
+                                     "malaria-rts3-default", "malaria-rts3-rts4-default",
+                                     "malaria-rts3-bluesky", "malaria-rts3-rts4-bluesky"
+                                   ),
+                                   in_path,
+                                   c("Stochastic_Burden_Estimates_Glele_Kakai_No_Vaccine_:index.csv.xz",
+                                     "Stochastic_Burden_Estimates_Glele_Kakai_Default_rts3_:index.csv.xz",
+                                     "Stochastic_Burden_Estimates_Glele_Kakai_Default_rts3_4_:index.csv.xz",
+                                     "stochastic_Burden_Estimates_Glele_Kakai_Blue_Sky_rts3_:index.csv.xz",
+                                     "Stochastic_Burden_Estimates_Glele_Kakai_Blue_Sky_rts34_:index.csv.xz"),
+                                   "", 1, 200, in_path, bypass_cert_check = TRUE)
+
+  upload4(in_path, "UAC-Glele_Kakai", "Malaria", "202310gavi-7", con, annex)
+
+  in_path <- "//fi--didenas1-app/Test/mal"
+  stoner::stone_stochastic_process(con, "IC-Okell", "Malaria", "202310gavi-7",
+                                   c("malaria-no-vaccination",
+                                     "malaria-rts3-default", "malaria-rts3-rts4-default",
+                                     "malaria-rts3-bluesky", "malaria-rts3-rts4-bluesky"
+                                   ),
+                                   in_path,
+                                   c("stochastic-burden-est.202310gavi-7.Malaria_IC-Okell_no-vaccination_draw_:index Lydia Haile.csv.xz",
+                                     "stochastic-burden-est.202310gavi-7.Malaria_IC-Okell_malaria-rts3-default_draw_:index Lydia Haile.csv.xz",
+                                     "stochastic-burden-est.202310gavi-7.Malaria_IC-Okell_malaria-rts3-rts4-default_draw_:index Lydia Haile.csv.xz",
+                                     "stochastic-burden-est.202310gavi-7.Malaria_IC-Okell_malaria-rts3-bluesky_draw_:index Lydia Haile.csv.xz",
+                                     "stochastic-burden-est.202310gavi-7.Malaria_IC-Okell_malaria-rts3-rts4-bluesky_draw_:index Lydia Haile.csv.xz"
+                                   ),
+                                   "", 1, 200, in_path, bypass_cert_check = TRUE)
+  upload4(in_path, "IC-Okell", "Malaria", "202310gavi-7", con, annex)
+
+
+
+  in_path <- "//fi--didenas1-app/Test/tki"
+  stoner::stone_stochastic_process(con, "TKI-Penny", "Malaria", "202310gavi-7",
+                                   c("malaria-no-vaccination",
+                                     "malaria-rts3-default", "malaria-rts3-rts4-default",
+                                     "malaria-rts3-bluesky", "malaria-rts3-rts4-bluesky"
+                                   ),
+                                   in_path,
+                                   c("stochastic_burden_est_malaria_:index_novaccine Josephine Malinga.csv.xz",
+                                     "stochastic_burden_est_malaria_:index_rtss_d3_default Josephine Malinga.csv.xz",
+                                     "stochastic_burden_est_malaria_:index_rtss_d4_default Josephine Malinga.csv.xz",
+                                     "stochastic_burden_est_malaria_:index_rtss_d3_bluesky Josephine Malinga.csv.xz",
+                                     "stochastic_burden_est_malaria_:index_rtss_d4_bluesky Josephine Malinga.csv.xz"),
+                                   "", 1, 31, in_path, bypass_cert_check = TRUE)
+  upload4(in_path, "TKI-Penny", "Malaria", "202310gavi-7", con, annex)
+
+
+  ################################################################
+  # MENA
+  # NB - mena-campaign-default and mena-campaign-ia2030 are identical,
+  # so same file used below.
+
+  in_path <- "//fi--didenas1-app/Test/MenA"
+  out_path <- in_path
+
+
+  stub <- "stochastic-burden-template202310gavi-7MenA_Cambridge-Trotter_mena-"
+  stoner::stone_stochastic_process(con,
+                                   "Cambridge-Trotter", "MenA", "202310gavi-7",
+                                   c("mena-no-vaccination", "mena-campaign-default", "mena-campaign-routine-default",
+                                     "mena-campaign-bluesky", "mena-campaign-routine-bluesky",
+                                     "mena-campaign-ia2030", "mena-campaign-routine-ia2030"),
+                                   in_path,
+                                   c(paste0(stub, "novaccination (:index) Andromachi Karachaliou.csv"),
+                                     paste0(stub, "campaign-default (:index) Andromachi Karachaliou.csv"),
+                                     paste0(stub, "routine-default (:index) Andromachi Karachaliou.csv"),
+                                     paste0(stub, "campaign-bluesky (:index) Andromachi Karachaliou.csv"),
+                                     paste0(stub, "routine-bluesky (:index) Andromachi Karachaliou.csv"),
+                                     paste0(stub, "campaign-default (:index) Andromachi Karachaliou.csv"),
+                                     paste0(stub, "routine-ia2030 (:index) Andromachi Karachaliou.csv")),
+                                   "", 1, 26, out_path, outcomes = list(cases = "cases", deaths = "deaths",
+                                                                        dalys = "dalys", yll = "yll", cases_cwyx = "cases_cwyx",
+                                                                        deaths_cwyx = "deaths_cwyx", dalys_cwyx = "dalys_cwyx", yll_cwyx = "yll_cwyx"),
+                                   bypass_cert_check = TRUE)
+  upload4(in_path, "Cambridge-Trotter", "MenA", "202310gavi-7", con, annex)
+
+}
+
+do_stochastics_2021 <- function(con) {
+  out_path <- "E:/stochastic_2023/"
+  in_path <- "E:/Dropbox (SPH Imperial College)/File requests/latest/202110gavi/"
+
+
+  stub <- "Andromachi Karachaliou - stochastic-burden.202110gavi-2.MenA_Cambridge-Trotter_"
+  stone_stochastic_process(con,
+                           "Cambridge-Trotter", "MenA", "202110gavi-3",
+                           c("mena-no-vaccination", "mena-campaign-default", "mena-routine-default",
+                             "mena-booster-default", "mena-campaign-ia2030_target",
+                             "mena-routine-ia2030_target"),
+                           file.path(in_path, "Cambridge-Trotter"),
+                           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")),
+                           "", 1, 26, out_path, bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  stub <- "Aniruddha Deshpande - stochastic_burden_est_lopman_"
+  stone_stochastic_process(con,
+                           "Emory-Lopman", "Rota", "202110gavi-3",
+                           c("rota-no-vaccination",
+                             "rota-routine-default",
+                             "rota-routine-ia2030_target"),
+                           file.path(in_path, "Emory-Lopman"),
+                           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")),
+                           "", NA, NA, out_path, allow_missing_disease = TRUE, bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  stub <- "Allison Portnoy - stochastic-burden-est."
+  stone_stochastic_process(con,
+                           "Harvard-Sweet", "HPV", "202110gavi-3",
+                           c("hpv-no-vaccination",
+                             "hpv-campaign-default",
+                             "hpv-campaign-ia2030_target",
+                             "hpv-routine-default",
+                             "hpv-routine-ia2030_target"),
+                           in_path,
+                           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")),
+                           "", 1, 200, out_path,
+                           runid_from_file = TRUE, bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  stub <- "Keith Fraser - stochastic-burden-estimates.202110gavi-3_YF_IC-Garske_"
+  stoner::stone_stochastic_process(con,
+                                   "IC-Garske", "YF", "202110gavi-3",
+                                   c("yf-no-vaccination",
+                                     "yf-preventive-default",
+                                     "yf-preventive-ia2030_target",
+                                     "yf-routine-default",
+                                     "yf-routine-ia2030_target"),
+                                   file.path(in_path, "IC-Garske-YF"),
+                                   paste0(stub, ":scenario_:index.csv.xz"),
+                                   "", 1, 200, out_path, bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  stone_stochastic_process(con, "IVI-Kim", "Typhoid", "202110gavi-3",
+                           c("typhoid-no-vaccination",
+                             "typhoid-campaign-default", "typhoid-campaign-ia2030_target",
+                             "typhoid-routine-default",  "typhoid-routine-ia2030_target"),
+                           in_path,
+                           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"),
+                           "", NA, NA, out_path, bypass_cert_check = TRUE)
+
+  #####################################################
+
+  stub <- "stochastic_burden_est_HepB-IC-Hallett_"
+  stone_stochastic_process(con,
+                           "IC-Hallett", "HepB", "202110gavi-3",
+                           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,
+                           paste0(stub, ":scenario_:index.csv.xz"),
+                           "Margaret de Villiers - cert115", 1, 200, out_path,
+                           "deaths", c("hepb_cases_acute_severe","hepb_cases_comp_cirrh",
+                                       "hepb_cases_hcc_no_cirrh"), "dalys")
+
+  #############################################################################
+
+  stoner::stone_stochastic_process(con,
+                                   "IVI-Kim", "Cholera", "202110gavi-3",
+                                   c("cholera-no-vaccination", "cholera-campaign-default"),
+                                   file.path(in_path, "IVI-Kim-Cholera"),
+                                   c("Jong-Hoon Kim - stoch_Cholera_novacc_20211221T00.csv.xz",
+                                     "Jong-Hoon Kim - stoch_Cholera_campaign_20211222T212131.csv.xz"),
+                                   "", NA, NA, out_path, bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  stone_stochastic_process(con,
+                           "JHU-Lee", "Cholera", "202110gavi-3",
+                           c("cholera-no-vaccination", "cholera-campaign-default"),
+                           file.path(in_path, "JHU-Lee-Cholera"),
+                           c("Kaiyue Zou - no-vaccination.csv.xz",
+                             "Kaiyue Zou - campaign-default.csv.xz"),
+                           "", NA, NA, out_path, bypass_cert_check = TRUE)
+
+  #############################################################################
+
+
+  stub <- "Amy Winter - stochastic_burden_est-rubella-"
+  stone_stochastic_process(con,
+                           "JHU-Lessler", "Rubella", "202110gavi-3",
+                           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,
+                           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")),
+                           "", 1, 11, out_path,
+                           "rubella_deaths_congenital",
+                           "rubella_cases_congenital", "dalys", bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  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")
+
+  stone_stochastic_process(con,
+                           "JHU-Tam", "Hib", "202110gavi-3",
+                           hib_scenarios,
+                           file.path(in_path, "JHU-Tam-Carter-Hib"), ":scenario.csv.xz",
+                           "", NA, NA, out_path,
+                           outcomes <- list(deaths = c("deaths_men", "deaths_pneumo"),
+                                            cases = c("cases_men", "cases_pneumo"),
+                                            dalys = list_params_hib_pcv),
+                           bypass_cert_check = TRUE)
+
+  # 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(out_path, sprintf("%s_central_dalys.csv",
+                                                                          hib_scenario)))
+  }
+
+  pcv_scenarios <- c("pcv-no-vaccination-LiST",
+                     "pcv-routine-default-LiST",
+                     "pcv-routine-ia2030_target-LiST")
+
+  stone_stochastic_process(con,
+                           "JHU-Tam", "PCV", "202110gavi-3",
+                           pcv_scenarios,
+                           file.path(in_path, "JHU-Tam-Carter-PCV"), ":scenario.csv.xz",
+                           "", NA, NA, out_path,
+                           deaths = c("deaths_men", "deaths_pneumo"),
+                           cases = c("cases_men", "cases_pneumo"),
+                           dalys = list_params_hib_pcv,
+                           bypass_cert_check = TRUE)
+
+  # 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(out_path, sprintf("%s_central_dalys.csv",
+                                                                          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")
+
+  stone_stochastic_process(con,
+                           "JHU-Tam", "Rota", "202110gavi-3",
+                           rota_scenarios,
+                           file.path(in_path, "JHU-Tam-Carter-Rota"), ":scenario.csv.xz",
+                           "", NA, NA, out_path,
+                           dalys = list_params_rota,
+                           bypass_cert_check = TRUE)
+
+
+  # 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(out_path, sprintf("%s_central_dalys.csv",
+                                                                          rota_scenario)))
+  }
+
+  ####################################################################################
+
+  stub <- "Eric Johnson - "
+  stone_stochastic_process(con,
+                           "KPW-Jackson", "MenA", "202110gavi-3",
+                           c("mena-booster-default",
+                             "mena-campaign-default",
+                             "mena-campaign-ia2030_target",
+                             "mena-no-vaccination",
+                             "mena-routine-default",
+                             "mena-routine-ia2030_target"),
+                           file.path(in_path, "KPW-Jackson-MenA"),
+                           paste0(stub, ":scenario-202111gavi-2.MenA_KPW-Johnson.csv.xz"),
+                           "", NA, NA, out_path, bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  stoner::stone_stochastic_process(con,
+                                   "Li", "HepB", "202110gavi-2",
+                                   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"
+
+                                   ),
+                                   file.path(in_path, "Li"),
+                                   paste0(":scenario:index.csv"),
+                                   "cert105", 1, 200, out_path,
+                                   c("hepb_deaths_acute", "hepb_deaths_total_cirrh", "hepb_deaths_hcc"),
+                                   c("hepb_cases_acute_symp", "hepb_cases_fulminant",
+                                     "hepb_cases_chronic", "hepb_chronic_symptomatic_in_acute_phase"),
+                                   "dalys"
+  )
+
+  #############################################################################
+
+  stub <- "Kaja Abbas - PSA_202110gavi-3_"
+  stone_stochastic_process(con,
+                           "LSHTM-Clark", "Hib", "202110gavi-3",
+                           c("hib-no-vaccination","hib-routine-default","hib-routine-ia2030_target"),
+                           file.path(in_path, "LSHTM-Clark_Hib"),
+                           c(paste0(stub, ":scenario.csv.xz")),
+                           "Kaja Abbas - hib_cert116", NA, NA, out_path)
+
+  #############################################################################
+
+
+  stub <- "Kaja Abbas - PSA_202110gavi-3_"
+  stone_stochastic_process(con,
+                           "LSHTM-Clark", "Rota", "202110gavi-3",
+                           c("rota-no-vaccination","rota-routine-default","rota-routine-ia2030_target"),
+                           file.path(in_path, "LSHTM-Clark_Rota"),
+                           c(paste0(stub, ":scenario.csv.xz")),
+                           "Kaja Abbas - rota_cert117", NA, NA, out_path)
+
+  #############################################################################
+
+
+  stub <- "stochastic-burden-"
+  stoner::stone_stochastic_process(con,
+                                   "LSHTM-Jit", "HPV", "202110gavi-3",
+                                   c("hpv-no-vaccination",
+                                     "hpv-campaign-default",
+                                     "hpv-routine-default",
+                                     "hpv-campaign-ia2030_target",
+                                     "hpv-routine-ia2030_target"),
+                                   "E:/Dropbox (SPH Imperial College)/File requests/latest/202110gavi/LSHTM-Jit_HPV",
+                                   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")),
+                                   "cert104", NA, NA, out_path, bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  stub <- "Han Fu - stochastic_burden_estimate_measles-LSHTM-Jit-"
+  stone_stochastic_process(con, "LSHTM-Jit", "Measles", "202110gavi-2",
+                           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"),
+                           file.path(in_path, "LSHTM-Jit_Measles"),
+                           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")),
+                           "Han Fu - cert107", NA, NA, out_path)
+
+  #############################################################################
+
+  stub <- "stochastic_burden_est_"
+  stone_stochastic_process(con,
+                           "NUS-Chen", "PCV", "202110gavi-3",
+                           c("pcv-no-vaccination","pcv-routine-default","pcv-routine-ia2030_target"),
+                           file.path(in_path, "LSHTM-NUS-Chen_PCV"),
+                           paste0(stub, ":scenario.csv.xz"),
+                           "Jemima Koh - cert121", NA, NA, "E:/Stochastic_2021")
+
+
+
+  stoner::stone_stochastic_process(con,
+                                   "PHE-Vynnycky", "Rubella", "202110gavi-3",
+                                   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"),
+                                   file.path(in_path, "PHE-Vynnycky_Rubella"),
+                                   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"),
+                                   "", 1, 112, out_path,
+                                   "rubella_deaths_congenital",
+                                   "rubella_cases_congenital", "dalys", bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  stub <- "Sean Moore - stochastic_burden_est_JE_UND-Moore_"
+  stone_stochastic_process(con,
+                           "UND-Moore", "JE", "202110gavi-2",
+                           c("je-routine-no-vaccination",
+                             "je-campaign-default",
+                             "je-routine-default",
+                             "je-campaign-ia2030_target",
+                             "je-routine-ia2030_target"
+                           ),
+                           file.path(in_path, "UND-Moore-JE"),
+                           paste0(stub, ":scenario.csv.xz"),
+                           "Sean Moore - cert108", NA, NA, out_path)
+
+  stub <- "stochastic_burden_est_YF_UND-Perkins_"
+  stoner::stone_stochastic_process(con,
+                                   "UND-Perkins", "YF", "202110gavi-3",
+                                   c("yf-no-vaccination",
+                                     "yf-preventive-default",
+                                     "yf-preventive-ia2030_target",
+                                     "yf-routine-default",
+                                     "yf-routine-ia2030_target"),
+                                   file.path(in_path, "UND-Perkins-YF"),
+                                   paste0(stub, ":scenario_:index.csv.xz"),
+                                   "", 1, 200, out_path, bypass_cert_check = TRUE)
+
+  stub <- "Holly Burrows - stochastic_burden_est_TF-Yale-Burrows"
+  stone_stochastic_process(con,
+                           "Yale-Pitzer", "Typhoid", "202110gavi-3",
+                           c("typhoid-no-vaccination",
+                             "typhoid-campaign-default", "typhoid-campaign-ia2030_target",
+                             "typhoid-routine-default",  "typhoid-routine-ia2030_target"),
+                           file.path(in_path, "Yale-Pitzer-Typhoid"),
+                           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")),
+                           "", NA, NA, out_path, bypass_cert_check = TRUE)
+
+
+  upload4(out_path, "Cambridge-Trotter", "MenA", "202110gavi-3", con, annex)
+  upload4(out_path, "Emory-Lopman", "Rota", "202110gavi-3", con, annex)
+  upload4(out_path, "IC-Garske", "YF", "202110gavi-3", con, annex)
+  upload4(out_path, "IC-Hallett", "HepB", "202110gavi-3", con, annex)
+  upload4(out_path, "IVI-Kim", "Cholera", "202110gavi-3", con, annex)
+  upload4(out_path, "IVI-Kim", "Typhoid", "202110gavi-3", con, annex)
+  upload4(out_path, "JHU-Lee", "Cholera", "202110gavi-3", con, annex)
+  upload4(out_path, "JHU-Lessler", "Rubella", "202110gavi-3", con, annex)
+  upload4(out_path, "JHU-Tam", "Hib", "202110gavi-3", con, annex)
+  upload4(out_path, "JHU-Tam", "PCV", "202110gavi-3", con, annex)
+  upload4(out_path, "JHU-Tam", "Rota", "202110gavi-3", con, annex)
+  upload4(out_path, "Li", "HepB", "202110gavi-2", con, annex)
+  upload4(out_path, "LSHTM-Clark", "Hib", "202110gavi-3", con, annex)
+  upload4(out_path, "LSHTM-Clark", "Rota", "202110gavi-3", con, annex)
+  upload4(out_path, "LSHTM-Jit", "HPV", "202110gavi-3", con, annex)
+  upload4(out_path, "LSHTM-Jit", "Measles", "202110gavi-2", con, annex)
+  upload4(out_path, "NUS-Chen", "PCV", "202110gavi-3", con, annex)
+  upload4(out_path, "PHE-Vynnycky", "Rubella", "202110gavi-2", con, annex)
+  upload4(out_path, "UND-Moore", "JE", "202110gavi-3", con, annex)
+  upload4(out_path, "UND-Perkins", "YF", "202110gavi-3", con, annex)
+  upload4(out_path, "Yale-Pitzer", "Typhoid", "202110gavi-3", con, annex)
+
+
+
+}
+
+do_stochastics_2019 <- function() {
+  in_path <- "E:/Dropbox (SPH Imperial College)/File requests/latest/201910gavi"
+  out_path <- "E:/Stocastic_2019"
+
+  #############################################################################
+
+  stub <- "Andromachi Karachaliou - stochastic-burden.201910gavi-4.MenA_Cambridge-Trotter_"
+  stone_stochastic_process(con,
+                           "Cambridge-Trotter", "MenA", "201910gavi-5",
+                           c("mena-campaign-bestcase", "mena-campaign-default", "mena-no-vaccination",
+                             "mena-routine-bestcase", "mena-routine-default"),
+                           file.path(in_path, "Cambridge-Trotter"),
+                           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")),
+                           "cert60", 1, 52, out_path)
+
+  #############################################################################
+
+  stub <- "Ivane Gamkrelidze - stochastic-burden-template.201910gavi-4.HepB_CDA-Razavi_"
+  stone_stochastic_process(con,
+                           "CDA-Razavi", "HepB", "201910gavi-5",
+                           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"
+                           ),
+                           file.path(in_path, "CDA-Razavi"),
+                           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")),
+                           "Ivane Gamkrelidze - cert68", NA, NA, out_path,
+                           c("hepb_deaths_acute","hepb_deaths_dec_cirrh","hepb_deaths_hcc"),
+                           c("hepb_cases_acute_severe","hepb_cases_dec_cirrh","hepb_cases_hcc"),
+                           "dalys"
+  )
+
+  #############################################################################
+
+  stub <- "Molly Steele - stochastic-burden.201910gavi-4.Rota_Emory-Lopman_"
+  stone_stochastic_process(con,
+                           "Emory-Lopman", "Rota", "201910gavi-5",
+                           c("rota-no-vaccination",
+                             "rota-routine-bestcase",
+                             "rota-routine-default"),
+                           file.path(in_path, "Emory-Lopman"),
+                           paste0(stub, ":scenario.csv.xz"),
+                           "Molly Steele - cert66", NA, NA, out_path,
+                           allow_missing_disease = TRUE)
+
+  #############################################################################
+
+  stub <- "stochastic-burden-est.201910gavi-5.HPV_Harvard-Sweet_"
+  stone_stochastic_process(con,
+                           "Harvard-Sweet", "HPV", "201910gavi-5",
+                           c("hpv-campaign-bestcase",
+                             "hpv-campaign-default",
+                             "hpv-no-vaccination",
+                             "hpv-routine-bestcase",
+                             "hpv-routine-default"),
+                           file.path(in_path, "Harvard-Sweet"),
+                           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")),
+                           "", 1, 200, out_path,
+                           runid_from_file = TRUE, bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  stub <- "stochastic-burden-estimates.201910gavi-4_YF_IC-Garske_"
+  stone_stochastic_process(con,
+                           "IC-Garske", "YF", "201910gavi-5",
+                           c("yf-no-vaccination",
+                             "yf-preventive-bestcase",
+                             "yf-preventive-default",
+                             "yf-routine-bestcase",
+                             "yf-routine-default",
+                             "yf-stop"),
+                           file.path(in_path, "IC-Garske2"),
+                           paste0(stub, ":scenario_:index.csv.xz"),
+                           "Katy Gaythorpe - cert62", 1, 200, out_path)
+
+  #############################################################################
+
+  stub <- "stochastic_burden_est_HepB-IC-Hallett_"
+  stone_stochastic_process(con,
+                           "IC-Hallett", "HepB", "201910gavi-5",
+                           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"),
+                           file.path(in_path, "IC-Hallett"),
+                           paste0(stub, ":scenario_:index.csv.xz"),
+                           "Margaret de Villiers  - cert73", 1, 200, out_path,
+                           "deaths", c("hepb_cases_acute_severe","hepb_cases_comp_cirrh",
+                                       "hepb_cases_hcc_no_cirrh"), "dalys")
+
+  #############################################################################
+
+  stone_stochastic_process(con,
+                           "IVI-Kim", "Cholera", "201910gavi-5",
+                           c("cholera-no-vaccination", "cholera-campaign-default"),
+                           file.path(in_path, "IVI-Kim-Cholera"),
+                           c("Jong-Hoon Kim - stoch_output_Cholera_novacc_20210902.csv.xz",
+                             "Jong-Hoon Kim - stoch_output_Cholera_campaign_20210902.csv.xz"),
+                           "Jong-Hoon Kim - cert89", NA, NA, out_path)
+
+  #############################################################################
+
+  stone_stochastic_process(con,
+                           "IVI-Kim", "Typhoid", "201910gavi-5",
+                           c("typhoid-no-vaccination", "typhoid-campaign-default", "typhoid-routine-default"),
+                           file.path(in_path, "IVI-Kim-Typhoid"),
+                           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"),
+                           "Jong-Hoon Kim - cert90", NA, NA, out_path)
+
+  #############################################################################
+
+  stub <- "Amy Winter - stochastic_burden_est-"
+  stone_stochastic_process(con,
+                           "JHU-Lessler", "Rubella", "201910gavi-5",
+                           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"),
+                           file.path(in_path, "JHU-Lessler"),
+                           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)),
+                           "Amy Winter - cert70", 1, 12, out_path,
+                           "rubella_deaths_congenital",
+                           "rubella_cases_congenital", "dalys")
+
+  #############################################################################
+
+  stub <- "Michael Jackson - stochastic_burden_est_MenA_KPWA_"
+  stone_stochastic_process(con,
+                           "KPW-Jackson", "MenA", "201910gavi-5",
+                           c("mena-routine-bestcase",
+                             "mena-routine-default",
+                             "mena-campaign-bestcase",
+                             "mena-campaign-default",
+                             "mena-no-vaccination"),
+                           file.path(in_path, "KPW-Jackson"),
+                           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")),
+                           "cert61", 1, 26, out_path)
+
+  #############################################################################
+
+  stone_stochastic_process(con,
+                           "JHU-Lee", "Cholera", "201910gavi-5",
+                           c("cholera-no-vaccination", "cholera-campaign-default"),
+                           file.path(in_path, "JHU-Lee"),
+                           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"),
+                           "", NA, NA, out_path, bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  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)
+  )
+
+  stone_stochastic_process(con,
+                           "JHU-Tam", "Hib", "201910gavi-5",
+                           c("hib-no-vaccination-LiST", "hib-routine-default-LiST", "hib-routine-bestcase-LiST"),
+                           file.path(in_path, "JHU-Tam-Hib"),
+                           c("novac:index.csv.xz", "default:index.csv.xz", "best:index.csv.xz"),
+                           "", 1, 14, out_path,
+                           deaths = c("deaths_men", "deaths_pneumo"),
+                           cases = c("cases_men", "cases_pneumo"),
+                           dalys = list_params_hib_pcv,
+                           bypass_cert_check = TRUE)
+
+  # And to add DALYs to the existing
+
+  stone_stochastic_process(con,
+                           "JHU-Tam", "PCV", "201910gavi-5",
+                           c("pcv-no-vaccination-LiST", "pcv-routine-default-LiST", "pcv-routine-bestcase-LiST"),
+                           file.path(in_path, "JHU-Tam-PCV"),
+                           c("novac:index.csv.xz", "default:index.csv.xz", "best:index.csv.xz"),
+                           "", 1, 14, out_path,
+                           deaths = c("deaths_men", "deaths_pneumo"),
+                           cases = c("cases_men", "cases_pneumo"),
+                           dalys = list_params_hib_pcv,
+                           bypass_cert_check = TRUE)
+
+  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)
+  )
+
+  stone_stochastic_process(con,
+                           "JHU-Tam", "Rota", "201910gavi-5",
+                           c("rota-no-vaccination-LiST", "rota-routine-default-LiST", "rota-routine-bestcase-LiST"),
+                           file.path(in_path, "JHU-Tam-Rota"),
+                           c("novac:index.csv.xz", "default:index.csv.xz", "best:index.csv.xz"),
+                           "", 1, 14, out_path,
+                           dalys = list_params_rota,
+                           bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  stone_stochastic_process(con,
+                           "Li", "HepB", "201910gavi-5",
+                           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"
+                           ),
+                           file.path(in_path, "Li"),
+                           paste0(":scenario:index.csv.xz"),
+                           "cert74", 1, 200, out_path,
+                           c("hepb_deaths_acute", "hepb_deaths_total_cirrh", "hepb_deaths_hcc"),
+                           c("hepb_cases_acute_symp", "hepb_cases_fulminant",
+                             "hepb_cases_chronic", "hepb_chronic_symptomatic_in_acute_phase"),
+                           "dalys"
+  )
+
+  #############################################################################
+
+  stub <- "VIMC_Hib_PSA_"
+  stone_stochastic_process(con,
+                           "LSHTM-Clark", "Hib", "201910gavi-5",
+                           c("hib-no-vaccination","hib-routine-bestcase","hib-routine-default"),
+                           file.path(in_path, "LSHTM-Clark_Hib"),
+                           c(paste0(stub, "NoVax.csv.xz"),
+                             paste0(stub, "Best.csv.xz"),
+                             paste0(stub, "Default.csv.xz")),
+                           "cert81", NA, NA, out_path)
+
+  stub <- "VIMC_Sp_PSA_"
+  stone_stochastic_process(con,
+                           "LSHTM-Clark", "PCV", "201910gavi-5",
+                           c("pcv-no-vaccination","pcv-routine-bestcase","pcv-routine-default"),
+                           file.path(in_path, "LSHTM-Clark_PCV"),
+                           c(paste0(stub, "NoVax.csv.xz"),
+                             paste0(stub, "Best.csv.xz"),
+                             paste0(stub, "Default.csv.xz")),
+                           "cert82", NA, NA, "E:/Stochastic_2019")
+
+  stub <- "Hira Tanvir - VIMC_Rota_PSA_"
+  stone_stochastic_process(con,
+                           "LSHTM-Clark", "Rota", "201910gavi-5",
+                           c("rota-no-vaccination","rota-routine-bestcase","rota-routine-default"),
+                           file.path(in_path, "LSHTM-Clark_Rota"),
+                           c(paste0(stub, "NoVax.csv.xz"),
+                             paste0(stub, "Best.csv.xz"),
+                             paste0(stub, "Default.csv.xz")),
+                           "", NA, NA, "E:/Stochastic_2019")
+
+  #############################################################################
+
+  stub <- "stochastic-burden-"
+  stone_stochastic_process(con,
+                           "LSHTM-Jit", "HPV", "201910gavi-5",
+                           c("hpv-campaign-bestcase",
+                             "hpv-campaign-default",
+                             "hpv-no-vaccination",
+                             "hpv-routine-bestcase",
+                             "hpv-routine-default"),
+                           file.path(in_path, "LSHTM-Jit_HPV"),
+                           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")),
+                           "Kaja Abbas - stochastic_parameters_certificate_HPV_LSHTM-Jit_201910gavi-4",
+                           NA, NA, out_path, bypass_cert_check = TRUE)
+
+  #############################################################################
+
+  stub <- "stochastic_burden_estimate_measles-LSHTM-Jit-"
+  stone_stochastic_process(con, "LSHTM-Jit", "Measles", "201910gavi-5",
+                           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"),
+                           file.path(in_path, "LSHTM-Jit_Measles"),
+                           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")),
+                           "cert83", NA, NA, out_path)
+
+  #############################################################################
+
+  stub <- "Template_Stochastic_"
+  stone_stochastic_process(con,
+                           "OUCRU-Clapham", "JE", "201910gavi-5",
+                           c("je-campaign-bestcase",
+                             "je-campaign-default",
+                             "je-routine-no-vaccination",
+                             "je-routine-bestcase",
+                             "je-routine-default"),
+                           file.path(in_path, "OUCRU-Clapham"),
+                           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")),
+                           "cert76", 1, 200, out_path)
+
+  #############################################################################
+
+  stub <- "stochastic_burden_est_"
+  stone_stochastic_process(con,
+                           "PHE-Vynnycky", "Rubella", "201910gavi-5",
+                           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"),
+                           file.path(in_path, "PHE-Vynnycky"),
+                           paste0(stub, ":scenario_country:index.csv.xz"),
+                           "", 1, 112, out_path,
+                           "rubella_deaths_congenital",
+                           "rubella_cases_congenital", "dalys")
+
+  #############################################################################
+
+  stub <- "Heather Santos - "
+  stone_stochastic_process(con,
+                           "PSU-Ferrari", "Measles", "201910gavi-5",
+                           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"),
+                           file.path(in_path, "PSU-Ferrari"),
+                           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")),
+                           "Heather Santos - cert80", 1, 8, out_path)
+
+  #############################################################################
+
+  stub <- "Sean Moore - stochastic_burden_est_JE_UND-Moore_"
+  stone_stochastic_process(con,
+                           "UND-Moore", "JE", "201910gavi-5",
+                           c("je-campaign-bestcase",
+                             "je-campaign-default",
+                             "je-routine-no-vaccination",
+                             "je-routine-bestcase",
+                             "je-routine-default"),
+                           file.path(in_path, "UND-Moore"),
+                           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")),
+                           "Sean Moore - cert58", NA, NA, out_path)
+
+  #############################################################################
+
+  stub <- "stochastic_burden_est_YF_UND-Perkins_"
+  stone_stochastic_process(con,
+                           "UND-Perkins", "YF", "201910gavi-5",
+                           c("yf-no-vaccination",
+                             "yf-preventive-bestcase",
+                             "yf-preventive-default",
+                             "yf-routine-bestcase",
+                             "yf-routine-default",
+                             "yf-stop"),
+                           file.path(in_path, "UND-Perkins"),
+                           paste0(stub, ":scenario_:index.csv.xz"),
+                           "John Huber - cert85", 1, 200, out_path)
+
+  #############################################################################
+
+  stone_stochastic_process(con,
+                           "Yale-Pitzer", "Typhoid", "201910gavi-5",
+                           c("typhoid-no-vaccination", "typhoid-campaign-default", "typhoid-routine-default"),
+                           file.path(in_path, "Yale-Pitzer"),
+                           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"),
+                           "Virginia Pitzer - cert88", NA, NA, out_path)
+
+  #############################################################################
+
+  upload4(out_path, "Cambridge-Trotter", "MenA", "201910gavi-5", con, annex)
+  upload4(out_path, "CDA-Razavi", "HepB", "201910gavi-5", con, annex)
+  upload4(out_path, "Emory-Lopman", "Rota", "201910gavi-5", con, annex)
+  upload4(out_path, "Harvard-Sweet", "HPV", "201910gavi-5", con, annex)
+  upload4(out_path, "IC-Garske", "YF", "201910gavi-5", con, annex)
+  upload4(out_path, "IC-Hallett", "HepB", "201910gavi-5", con, annex)
+  upload4(out_path, "IVI-Kim", "Cholera", "201910gavi-5", con, annex)
+  upload4(out_path, "IVI-Kim", "Typhoid", "201910gavi-5", con, annex)
+  upload4(out_path, "KPW-Jackson", "MenA", "201910gavi-5", con, annex)
+  upload4(out_path, "JHU-Lee", "Cholera", "201910gavi-5", con, annex)
+  upload4(out_path, "JHU-Tam", "Hib", "201910gavi-5", con, annex)
+  upload4(out_path, "JHU-Tam", "PCV", "201910gavi-5", con, annex)
+  upload4(out_path, "JHU-Tam", "Rota", "201910gavi-5", con, annex)
+  upload4(out_path, "JHU-Lessler", "Rubella", "201910gavi-5", con, annex)
+  upload4(out_path, "Li", "HepB", "201910gavi-5", con, annex)
+  upload4(out_path, "LSHTM-Clark", "Hib", "201910gavi-5", con, annex)
+  upload4(out_path, "LSHTM-Clark", "PCV", "201910gavi-5", con, annex)
+  upload4(out_path, "LSHTM-Clark", "Rota", "201910gavi-5", con, annex)
+  upload4(out_path, "LSHTM-Jit", "HPV", "201910gavi-5", con, annex)
+  upload4(out_path, "LSHTM-Jit", "Measles", "201910gavi-5", con, annex)
+  upload4(out_path, "OUCRU-Clapham", "JE", "201910gavi-5", con, annex)
+  upload4(out_path, "PHE-Vynnycky", "Rubella", "201910gavi-5", con, annex)
+  upload4(out_path, "PSU-Ferrari", "Measles", "201910gavi-5", con, annex)
+  upload4(out_path, "UND-Moore", "JE", "201910gavi-5", con, annex)
+  upload4(out_path, "UND-Perkins", "YF", "201910gavi-5", con, annex)
+  upload4(out_path, "Yale-Pitzer", "Typhoid", "201910gavi-5", con, annex)
+
+}
diff --git a/scripts/process_stoch_201710gavi.R b/scripts/process_stoch_201710gavi.R
index bcb89f4..3c2534d 100644
--- a/scripts/process_stoch_201710gavi.R
+++ b/scripts/process_stoch_201710gavi.R
@@ -22,7 +22,7 @@ fetch_scenarios <- function(disease, touchstone='201710gavi-5') {
 # Let's unleash the cow
 
 setwd("Q:/testcow")
-writeLines("vimc/stoner@VIMC-9230", "pkgdepends.txt")
+writeLines("vimc/stoner", "pkgdepends.txt")
 hipercow::hipercow_init(driver = "dide-windows")
 hipercow::hipercow_provision()
 # Network/memory might be too much for more than a job per node.
@@ -296,3 +296,5 @@ stoner::stone_stochastic_standardise(
             "John Huber - stochastic_burden_est_YF_UND-Perkins_yf-preventive-gavi_:index.csv.xz",
             "John Huber - stochastic_burden_est_YF_UND-Perkins_yf-routine-gavi_:index.csv.xz"),
   index = 1:200))
+
+stoner::stone_stochastic_make_meta(base_out_path)
diff --git a/scripts/process_stoch_201910gavi.R b/scripts/process_stoch_201910gavi.R
index 1ed1318..48e2019 100644
--- a/scripts/process_stoch_201910gavi.R
+++ b/scripts/process_stoch_201910gavi.R
@@ -22,7 +22,7 @@ fetch_scenarios <- function(disease) {
 # Let's unleash the cow
 
 setwd("Q:/testcow")
-writeLines("vimc/stoner@VIMC-9230", "pkgdepends.txt")
+writeLines("vimc/stoner", "pkgdepends.txt")
 hipercow::hipercow_init(driver = "dide-windows")
 hipercow::hipercow_provision()
 # Network/memory might be too much for more than a job per node.
@@ -417,3 +417,5 @@ stoner::stone_stochastic_standardise(
   scenarios = scenarios,
   files = "stochastic_burden_est_YF_UND-Perkins_:scenario_:index.csv.xz",
   index = 1:200))
+
+stoner::stone_stochastic_make_meta(base_out_path)
diff --git a/scripts/process_stoch_202110gavi.R b/scripts/process_stoch_202110gavi.R
index 210d016..1abb596 100644
--- a/scripts/process_stoch_202110gavi.R
+++ b/scripts/process_stoch_202110gavi.R
@@ -20,7 +20,7 @@ fetch_scenarios <- function(disease) {
 # Let's unleash the cow
 
 setwd("Q:/testcow")
-writeLines("vimc/stoner@VIMC-9230", "pkgdepends.txt")
+writeLines("vimc/stoner", "pkgdepends.txt")
 hipercow::hipercow_init(driver = "dide-windows")
 hipercow::hipercow_provision()
 # Network/memory might be too much for more than a job per node.
@@ -226,7 +226,6 @@ stoner::stone_stochastic_standardise(
             sprintf("%s_booster_:index.csv.xz", stub)),
   index = 1:26))
 
-hipercow::task_create_expr(resources = hres, expr =
 stoner::stone_stochastic_standardise(
   group = "KPW-Jackson",
   in_path = file.path(base_in_path, "KPW-Jackson-MenA"),
@@ -238,7 +237,7 @@ stoner::stone_stochastic_standardise(
             "stochastic_burden_est_MenA_KPWA_routine_default_:index.csv.xz",
             "stochastic_burden_est_MenA_KPWA_routine_ia2030_target_:index.csv.xz",
             "stochastic_burden_est_MenA_KPWA_booster_default_:index.csv.xz"),
-  index = 1:26))
+  index = 1:26)
 
 ###############
 # PCV
@@ -377,10 +376,13 @@ stoner::stone_stochastic_standardise(
   files = sprintf("%s_:scenario_:index.csv.xz", stub),
   index = 1:200)
 
-stoner::stone_stochastic_standardise(
+hipercow::task_create_expr(resources = hres, expr =
+  stoner::stone_stochastic_standardise(
   group = "UND-Perkins",
   in_path = file.path(base_in_path, "UND-Perkins-YF"),
   out_path = file.path(base_out_path, "YF_UND-Perkins"),
   scenarios = scenarios,
   files = "stochastic_burden_est_YF_UND-Perkins_:scenario_:index.csv.xz",
-  index = 1:200)
+  index = 1:200))
+
+stoner::stone_stochastic_make_meta(base_out_path)
diff --git a/scripts/process_stoch_202310gavi.R b/scripts/process_stoch_202310gavi.R
index 49349bd..ad320b7 100644
--- a/scripts/process_stoch_202310gavi.R
+++ b/scripts/process_stoch_202310gavi.R
@@ -360,3 +360,5 @@ stoner::stone_stochastic_standardise(
              "stochastic_burden_est_malaria_:index_rtss_d3_bluesky Josephine Malinga.csv.xz",
              "stochastic_burden_est_malaria_:index_rtss_d4_bluesky Josephine Malinga.csv.xz"),
   index = 1:31)
+
+stoner::stone_stochastic_make_meta(base_out_path)
diff --git a/scripts/process_stoch_202409malaria.R b/scripts/process_stoch_202409malaria.R
index c150f99..b17d724 100644
--- a/scripts/process_stoch_202409malaria.R
+++ b/scripts/process_stoch_202409malaria.R
@@ -35,3 +35,5 @@ stoner::stone_stochastic_standardise(
             "stochastic_burden_est_malaria_:index_rtss_d4_default Josephine Malinga.csv.xz"),
   index = 1:31
 )
+
+stoner::stone_stochastic_make_meta(base_out_path)
diff --git a/scripts/process_stoch_202602yf.R b/scripts/process_stoch_202602yf.R
index f203742..5e18778 100644
--- a/scripts/process_stoch_202602yf.R
+++ b/scripts/process_stoch_202602yf.R
@@ -25,3 +25,4 @@ stoner::stone_stochastic_standardise(
   files = c("stochastic_burden_est_YF_UND-Perkins_yf-scenario-1-default-Routine_and_Campaign_:index.csv.xz"),
   index = 1:200)
 
+stoner::stone_stochastic_make_meta(base_out_path)
diff --git a/vignettes/Stochastics.Rmd b/vignettes/Stochastics.Rmd
index 9be11c9..e9b63ec 100644
--- a/vignettes/Stochastics.Rmd
+++ b/vignettes/Stochastics.Rmd
@@ -5,8 +5,11 @@ date: "`r Sys.Date()`"
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteIndexEntry{Stochastics}
-  %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
+  %\VignetteEngine{knitr::rmarkdown}
+editor_options: 
+  markdown: 
+    wrap: 80
 ---
 
 ```{r setup, include = FALSE}
@@ -19,59 +22,65 @@ data_frame <- function(...) {
   data.frame(stringsAsFactors = FALSE, ...)
 }
 ```
-## Stochastics
 
-VIMC modelling groups produce stochastic data for us, which is currently
-200 runs of their models varying key parameters, to express uncertainty. 
-These files get uploaded to Box, and the groups are provided with
-guidance to produce files that we can process easily.
+## Stochastics
 
-Stoner can process these stochastic files into a standard format to
-make the files more predictable to work with. For a good compromise 
-between ease-of-use, performance and compression, we have used the
-`Parquet` format and the `arrow` package. This vignette covers
-how to process the data, how to create a central from a set of
-stochastics, and how to see a graph of the stochastics for quick
-diagnosis.
+VIMC modelling groups produce stochastic data for us, which is currently 200
+runs of their models varying key parameters, to express uncertainty. These files
+get uploaded to Box, and the groups are provided with guidance to produce files
+that we can process easily.
 
+Stoner can process these stochastic files into a standard format to make the
+files more predictable to work with. For a good compromise between ease-of-use,
+performance and compression, we have used the `Parquet` format and the `arrow`
+package. This vignette covers how to process the data, how to create a central
+from a set of stochastics, how to produce some graphs of the stochastics for
+quick diagnosis, and also how to use the stochastic explorer, a local shiny app
+for exploring and comparing stochastic datasets.
 
 ### Creating standard stochastic files
 
-This has superceded `stoner_stochastic_process`, which will be
-removed and undocumented soon.
-
-Here, we are mostly likely working with two folders or network shares,
-one of which contains the stochastics that groups have uploaded, and the
-other is a destination where we want to write our standardised files. These
-I'll call `in_path` and `out_path`. The `out_path` folders I am assuming
-will be only written to by stoner; paths and filenames will adhere to the
-following format:-
-
-* Within the root of the share we will have the **touchstone**, without
-a version. At time of writing, we have `202310gavi` and `202409malaria`.
-I am not anticipating having separate stochastics for separate versions
-of the stochastics at this point.
-
-* Within those touchstone folders, we have more folders in the form 
-`Disease_Modelling-Group` - such as `COVID_IC-Ghani` or `YF_IC-Garske`.
-The groups and diseases set up here copy the historic contents of the
-Montagu database.
-
-* Within each of these folders, we will be writing a file per
-country per scenario, containing all run_ids, named in the format
-`Modelling-Group_scenario_country.pq` - for example, 
-`IC-Garske_yf-routine-ia2030_KEN.pq`. If we also create a 
-average (central) of the stochastics (see later), that will be called
-`IC-Garske_yf-routine-ia2030_central.pq`, containing all the countries,
-and no run_id.
+This has superseded `stoner_stochastic_process`, which will be removed and
+undocumented soon.
+
+Here, we are mostly likely working with two folders or network shares, one of
+which contains the stochastics that groups have uploaded, and the other is a
+destination where we want to write our standardised files. These I'll call
+`in_path` and `out_path`. The `out_path` folders I am assuming will be only
+written to by stoner; paths and filenames will adhere to the following format:-
+
+-   Within the root of the share we will have the **touchstone**, without a
+    version. At time of writing, we have `202310gavi` and `202409malaria`. I am
+    not anticipating having separate stochastics for separate versions of the
+    stochastics at this point.
+
+-   Within those touchstone folders, we have more folders in the form
+    `Disease_Modelling-Group` - such as `COVID_IC-Ghani` or `YF_IC-Garske`. The
+    groups and diseases set up here copy the historic contents of the Montagu
+    database.
+
+-   Within each of these folders, we will be writing a file per country per
+    scenario, containing all run_ids, named in the format
+    `Modelling-Group_scenario_country.pq` - for example,
+    `IC-Garske_yf-routine-ia2030_KEN.pq`. If we also create a average (central)
+    of the stochastics (see later), that will be called
+    `IC-Garske_yf-routine-ia2030_central.pq`, containing all the countries, and
+    no run_id.
+
+-   Finally, in the root of the standardised folder, we'll keep a file
+    `meta.csv` to describe in one file the range of stochastic data we have by
+    touchstone, disease, group and scenario, and for each the outcomes and
+    countries provided. This is useful for speeding up the stochastic explorer
+    shiny app, but also may be a useful quick reference of our inventory in
+    general.
 
 #### Example Usage - One file per scenario
 
-Examples used so far are in the `scripts` folder in the root of the
-stoner repo - files such as `process_stoch_202310gavi.R`. Here is an 
-example of the Yellow Fever standardisation:-
+Examples used so far are in the `scripts` folder in the root of the stoner
+repo - files such as `process_stoch_202310gavi.R`. Here is an example of the
+Yellow Fever standardisation:-
 
-```
+```         
 scenarios = c("yf-no-vaccination", "yf-routine-bluesky",
               "yf-routine-campaign-bluesky", "yf-routine-campaign-default",
               "yf-routine-campaign-ia2030", "yf-routine-default",
@@ -86,27 +95,30 @@ stoner::stone_stochastic_standardise(
 ```
 
 `base_in_path` and `base_out_path` are defined elsewhere, and are my drive
-mappings to the incoming, and destination network shares. We provide the
-group name, the paths to the uploaded files (note the original dropbox path
-was a little incorrect with its dashes instead of underscores!) - and the
-destination path in the format described above. 
+mappings to the incoming, and destination network shares. We'll briefly talk
+about those later.
+
+We provide the group name, the paths to the uploaded files and the destination
+we want to write files to. Note that the `in_path` is not managed, and might be
+arbitrary - here it has dashes instead of underscores; but the `out_path`, we
+need to be careful to get right, in the format `disease` then underscore then
+the modelling group.
 
 We then provide the vector of scenarios, and because this group follow the
-guidance very well, they have included the exact scenario in the filenames.
-We can therefore use the `:scenario` glob in the `files` argument, and
-stoner will handle each of the seven scenarios in the right order.
+guidance very well, they have included the exact scenario in the filenames. We
+can therefore use the `:scenario` glob in the `files` argument, and stoner will
+handle each of the seven scenarios in the right order.
 
 #### Example Usage - One file per run
 
-Other groups provide a file per run, which is also fine. Here, we can
-specify an additional `index` parameter, usually `1:200`, and use the glob
-`:index` in the files argument. Here is an example of that - but note for
-this group, they didn't _quite_ include the scenarios exactly in the 
-filenames, so we have to explicitly name the files, rather than using 
-the `:scenario` glob. This is still yellow fever, so using the same
-`scenarios` vector as before.
+Other groups provide a file per run, which is also fine. Here, we can specify an
+additional `index` parameter, usually `1:200`, and use the glob `:index` in the
+files argument. Here is an example of that - but note for this group, they
+didn't *quite* include the scenarios exactly in the filenames, so we have to
+explicitly name the files, rather than using the `:scenario` glob. This is still
+yellow fever, so using the same `scenarios` vector as before.
 
-```
+```         
 stoner::stone_stochastic_standardise(
   group = "UND-Perkins",
   in_path = file.path(base_in_path, "YF-UND-Perkins"),
@@ -122,103 +134,338 @@ stoner::stone_stochastic_standardise(
   index = 1:200)
 ```
 
-The subtle difference is that the group used underscores instead of dashes in the scenario
-names. So here, I have to be careful and ensure the order of `files` matches the order of
-`scenarios` manually. After that, the `:index` glob, and the `index` argument deal with
-the fact we have 200 files per scenario.
+The subtle difference is that the group used underscores instead of dashes in
+the scenario names. So here, I have to be careful and ensure the order of
+`files` matches the order of `scenarios` manually. After that, the `:index`
+glob, and the `index` argument deal with the fact we have 200 files per
+scenario.
 
 #### Example Usage - One file per country
 
-If groups want to supply a file per country, then at present, we need them to do so 
-including the country in their uploads as normal, but number their files using the
-`index` method above, rather than having country names or ISO codes in the filename.
-We could do better if this proves to be a popular method.
+If groups want to supply a file per country, then at present, we need them to do
+so including the country in their uploads as normal, but number their files
+using the `index` method above, rather than having country names or ISO codes in
+the filename. We could do better if this proves to be a popular method.
+
+### Updating the meta-data
+
+Having run `stone_stochastic_standardise` and made changes to the structure, to
+enable the stochastic explorer shiny app to use the data (and to keep a useful
+file up-to-date), we need to run a command to update the metadata.
+
+```         
+stoner::stone_stochastic_make_meta(out_path)
+```
+
+which will run for a few seconds, recreating the entire file. The columns are
+touchstone, disease, group, scenario, countries and outcomes; the first four
+will be single elements, whereas countries and outcomes will be semi-colon
+separated lists.
 
 ### Advanced Usage
 
-You shouldn't have to worry about these things, but this is just so that you know
-they are there. We have some historic fixes that have made it possible for us to
-process some groups' stochastics with some formatting issues, rather than asking
-them to resubmit. These fixes are enabled by default; they won't do anything bad
-for groups where the fixes are not relevant, and are strictly required for the
-groups where they are relevant. 
+You shouldn't have to worry about these things, but this is just so that you
+know they are there. We have some historic fixes that have made it possible for
+us to process some groups' stochastics with some formatting issues, rather than
+asking them to resubmit. These fixes are enabled by default; they won't do
+anything bad for groups where the fixes are not relevant, and are strictly
+required for the groups where they are relevant.
 
 #### The Rubella Fix
 
 An argument `rubella_fix`, by default `TRUE`, deals with the fact that rubella
-groups have traditionally uploaded with different outcome names. Instead of `deaths`,
-`rubella_deaths_congenital`, and instead of `cases`, `rubella_cases_congenital`. 
-Some groups additionally supplied the `rubella_infections` outcome.
+groups have traditionally uploaded with different outcome names. Instead of
+`deaths`, `rubella_deaths_congenital`, and instead of `cases`,
+`rubella_cases_congenital`. Some groups additionally supplied the
+`rubella_infections` outcome.
 
-The `rubella_fix` argument causes stoner to omit `rubella_infections`, and rename
-the others to `deaths` and `cases` respectively, standardising them against the 
-other diseases.
+The `rubella_fix` argument causes stoner to omit `rubella_infections`, and
+rename the others to `deaths` and `cases` respectively, standardising them
+against the other diseases.
 
 #### The Missing Run Id fix
 
 One group also is in the habit of sending us one file per run_id, but omitting
-the `run_id` field from their uploads. The argument `missing_run_id_fix`, default
-`TRUE` lets Stoner spot this, and specifically if `index` is `1:200` (ie, we are
-expecting 200 uploads), it will transplant the index into the file to allow
-processing to be done normally.
+the `run_id` field from their uploads. The argument `missing_run_id_fix`,
+default `TRUE` lets Stoner spot this, and specifically if `index` is `1:200`
+(ie, we are expecting 200 uploads), it will transplant the index into the file
+to allow processing to be done normally.
 
 ### Other Considerations:
 
-* Stoner rounds of all the outcomes, (deaths, cases, dalys, yll and
-cohort_size) to be integer-like, which can create some confusion with small
-countries with a very small number of cases, but we expect those confusions. 
-Some groups still provide us with 16 decimal places, which creates some 
-problems, and the integer-like rounding is in the guidance.
+-   Stoner rounds of all the outcomes, (deaths, cases, dalys, yll and
+    cohort_size) to be integer-like, which can create some confusion with small
+    countries with a very small number of cases, but we expect those confusions.
+    Some groups still provide us with 16 decimal places, which creates some
+    problems, and the integer-like rounding is in the guidance.
+
+-   Note that for some groups with very large files (especially those with 16
+    decimal places), the processing takes **a lot of memory** - towards 128Gb.
+    So run these on a large machine or perhaps a cluster node.
+
+### More about the paths
+
+If at all possible, you should perform Stoner tasks on a computer within DIDE,
+connected with a cable. Stochastic processing involves large volumes of data,
+and ZScaler from outside, or even WiFi from inside may not copy that well with
+these tasks.
 
-* Note that for some groups with very large files (especially those with
-16 decimal places), the processing takes **a lot of memory** - towards
-128Gb. So run these on a large machine or perhaps a cluster node.
+On a DIDE Windows machine, `in_path` and `out_path` can be fully-qualified paths
+to DIDE network shares, which only members of the VIMC team have access to. This
+is probably the easiest case.
+
+On Linux or Mac, you have to mount the DIDE network shares, providing your DIDE
+details, and it is up to you what you call your local mountpoints. On linux,
+they may start with `/mnt/` and on Mac `/Volume/`. Talk to IT, if you need help
+setting up those mounts.
+
+And lastly, if you're on a windows machine but for some reason need to work
+externally, then use ZScaler, map drive letters to the network shares, and pass
+those in, but as we said, this is not recommended as it's not particularly
+stable. If at all possible, use an internal machine to do the heavy data work,
+and remote desktop into that if you need to be external.
 
 ## Creating a central estimate
 
-For many groups, their central estimates are the average of their
-stochastic runs. Historically groups have provided their centrals before
-their stochastics, as a means of early review and detection of problems.
-Having standardised a group's stochastic files, we can then generate 
-an averaged central for a single touchstone, disease, group and scenario,
-with the following call:-
+For many groups, their central estimates are the average of their stochastic
+runs. Historically groups have provided their centrals before their stochastics,
+as a means of early review and detection of problems. Having standardised a
+group's stochastic files, we can then generate an averaged central for a single
+touchstone, disease, group and scenario, with the following call:-
 
-```
+```         
 stone_stochastic_central(base, "202310gavi", "YF", "IC-Garske", 
                          "yf-routine-campaign-ia2030")
 ```
 
-`base` here is the root of the share where our standardised stochastic
-outputs have been put, and since those were created by stoner, it knows
-how to build the paths correctly. 
+`base` here is the root of the share where our standardised stochastic outputs
+have been put, and since those were created by stoner, it knows how to build the
+paths correctly.
 
-The result will be a new file in the form `group_scenario_central.pq` so
-in this case `IC-Garske_yf-routine-campaign-ia2030_central.pq` which
-matches the other standardised filenames, except for the country; this
-one central file will contain all countries.
+The result will be a new file in the form `group_scenario_central.pq` so in this
+case `IC-Garske_yf-routine-campaign-ia2030_central.pq` which matches the other
+standardised filenames, except for the country; this one central file will
+contain all countries.
 
-The default averaging function is `mean` - if we really want the `median`
-of the stochastics, then add the argument `avg_method = mean` to the
-function call.
+The default averaging function is `mean` - if we really want the `median` of the
+stochastics, then add the argument `avg_method = mean` to the function call.
+Note the lack of quotes - we are sending a function, not the name of a function.
 
 ## Creating stochastic graphs
 
-It can be useful to quickly dig out a graph showing all the stochastic
-lines together, to see what sort of spread they have. Again, with 
-`base` set to the root of the share where we are putting our
-standardised outputs, here is an example function call, and
-the graph that it produces.
+The `stone_stochastuc_graph` function can dig out a range of different graphs.
+If you want to explore the stochastics interactively, then try the shiny app
+(below) - but for programmatic plotting, read on.
 
-```
+### Basic burdens.
+
+With `base` set to the root of the share where we are putting our standardised
+outputs, here is an example function call, and the graph that it produces.
+
+```         
 stone_stochastic_graph(base, "202310gavi", "YF", "IC-Garske",
-                       "KEN", "yf-routine-campaign-ia2030",
+                       "KEN", "yf-no-vaccination",
                        "deaths")
 ```
 
-![Stochastic graph of IC-Garske, YF, Kenya, 202310gavi deaths.](figures/stoch_example_1.png)
+![](figures/stoch_example_1.png)
+
+The grey lines are each and every stochastic run. Red is the mean, and green is
+the median of the stochastics. The thicker black lines are the 5% and 95%
+quantiles. We can toggle the presence of each of those line types with the
+optional arguments `include_stochastics`, `include_quantiles`, `include_mean`
+and `include_median`, which can be set to `FALSE`, or left as the default
+`TRUE`.
+
+### Burden Differences
+
+If we specify two scenarios instead of one, then the plot will be the burden of
+the first scenario, subtract the burden of the second - i.e., the difference
+made by applying a scenario. Below is an example - I'll also add the
+`log = TRUE` argument to get a logged y-axis.
+
+```         
+stone_stochastic_graph(base, "202310gavi", "YF", "IC-Garske",
+                       "KEN", c("yf-no-vaccination", "yf-routine-campaign-ia2030"),
+                       "deaths", log = TRUE)
+```
+
+![](figures/stoch_burden_diff.png)
+
+The y-axis now reads "deaths averted" instead of just deaths.
+
+### Comparing touchstones
+
+You may have noticed that `stone_stochastic_graph` when you run it causes a plot
+to appear, but is actually returning you a list of plots - one so far. If we
+specify two touchstones, then stoner will instead return us a list of two
+graphs - assuming that the same disease, group and scenarios are present in both
+touchstones. The y-axis will be matched between the two, so we can see the pair
+of graphs for comparison.
+
+Below is an example returning to just a single scenario, although you can
+provide two and compare burden differences across two touchstones too. Here I am
+collecting both resulting graphs, and then plotting them one by one.
+
+```         
+res <- stone_stochastic_graph(base, c("202110gavi", "202310gavi"), "YF", "IC-Garske",
+                       "ETH", "yf-no-vaccination",
+                       "deaths", log = TRUE)
+res[[1]]
+res[[2]]
+```
+
+::: {style="display: flex; gap: 10px;"}
+<img src="figures/stoch_compare_ts_1.png" width="49%"/>
+<img src="figures/stoch_compare_ts_2.png" width="49%"/>
+:::
+
+### Comparing modelling groups
+
+Similarly, within the same touchstone, we can compare two modelling groups, with
+either a single scenario, or pair of scenarios. The disease, scenarios and
+outcomes must be compatible between the two groups. Here's a comparison of
+burden difference:
+
+```         
+res <- stone_stochastic_graph(base, "202310gavi", "YF", 
+                              c("IC-Garske", "UND-Perkins"),
+                              "UGA", 
+                              c("yf-no-vaccination", "yf-routine-default"),
+                              "cases")
+res[[1]]
+res[[2]]
+```
+
+::: {style="display: flex; gap: 10px;"}
+<img src="figures/stoch_compare_mg_1.png" width="49%"/>
+<img src="figures/stoch_compare_mg_2.png" width="49%"/>
+:::
+
+### Filtering by age
+
+The stochastic data contains both years and ages. Our plots so far have been
+using time on the x-axis, and all the ages have been summed together for each
+calendar year. We can also filter by age before plotting. The example below
+filters to ages between 0 and 4 inclusive (ie, under 5 year-olds).
+
+```         
+res <- stone_stochastic_graph(base, "202310gavi", "YF", "IC-Garske", "UGA",
+"yf-no-vaccination", "deaths", filter = 0:4)
+```
+
+![](figures/stoch_u5.png)
+
+### Plotting by birth cohort
+
+The `by_cohort` parameter lets us subtract age from year before plotting, so we
+can see burden specific to people who were born in a certain year, rather than
+the previous plots, which have all treated the x-axis as calendar year.
+
+```         
+res <- stone_stochastic_graph(base, "202310gavi", "YF", "IC-Garske", "UGA",
+                              "yf-no-vaccination", "deaths", by_cohort = TRUE)
+```
+
+![](figures/stoch_cohort.png)
+
+### Age on the x-axis
+
+If instead we want to see how burden affects people of different ages throughout
+the time series, we can set `xaxis` to `age` (its default is `time`).
+
+```         
+res <- stone_stochastic_graph(base, "202602yf", "YF", "IC-Gaythorpe", "UGA",
+                              "yf-no-vaccination", "deaths", xaxis = "age")
+```
+
+![](figures/stoch_byage.png)
+
+This sums over the entire range of years. If `xaxis` is age, then the `filter`
+argument will let you select which years are to be included. This example shows
+the burden by age in the years 2040-2050.
+
+```         
+res <- stone_stochastic_graph(base, "202602yf", "YF", "IC-Gaythorpe", "UGA",
+                              "yf-no-vaccination", "deaths", xaxis = "age",
+                              filter = 2040:2050)
+```
+
+![](figures/stoch_byage_filter.png)
+
+And if we additionally set the `by_cohort` flag, then we are asking for the
+burden breakdown by age, for people who were born within the filtered range,
+rather than selecting a calendar year range.
+
+```         
+res <- stone_stochastic_graph(base, "202602yf", "YF", "IC-Gaythorpe", "UGA",
+                              "yf-no-vaccination", "deaths", xaxis = "age",
+                              filter = 2040:2050, by_cohort = TRUE)
+```
+
+![](figures/stoch_byage_filter_cohort.png) The graph has this shape because the
+stochastics here only continue until 2100, hence the last 60-year olds will be
+born in 2040.
+
+### Including data from packit.
+
+Lastly, if a central burden estimate has been included in a packit in Montagu's
+reporting portal, we can include that on a graph by specifying the id, and the
+name of the file within that packit. For example, this graph shows both the
+stochastics from the file share, and the central from a packit, which is drawn
+in blue.
+
+```         
+res <- stone_stochastic_graph(base, "202602yf", "YF", "IC-Gaythorpe", "UGA",
+                              "yf-no-vaccination", "deaths", 
+                              packit_id = "20260421-090519-e4a90228",
+                              packit_file = "burden-estimates-disaggregated.rds")
+
+Visit <https://montagu.vaccineimpact.org/packit/device> and enter code DNLH-NMRL
+
+✔ Waiting for response from server [10.6s]
+```
+
+![](figures/stoch_packit.png)
+
+And here's an example of a burden difference graph, showing how much difference
+the `yf-routine-default` scenario makes in both the stochastics, and the central
+from packit.
+
+```         
+res <- stone_stochastic_graph(base, "202602yf", "YF", "IC-Gaythorpe", "UGA",
+                               c("yf-no-vaccination", "yf-routine-default"), "deaths", 
+                               packit_id = "20260421-090519-e4a90228",
+                               packit_file = "burden-estimates-disaggregated.rds")
+
+Visit <https://montagu.vaccineimpact.org/packit/device> and enter code MBWV-NBBC
+✔ Waiting for response from server [10.9s]
+```
+
+![](figures/stoch_packit_diff.png) \## The stochastic explorer shiny app
+
+All of the graphs above (except currently the packit graphs) can be visualised
+interactively using a shiny app that is part of stoner. We call it providing the
+path to the root of the standardised stochastic data, where the `meta.csv` file
+should also be found.
+
+```         
+stoner::stochastic_explorer(path)
+```
 
-Red is the mean, and green is the median of the stochastics, and the
-thicker black lines are the 5% and 95% quantiles. Other useful arguments for
-graphs are `by_cohort` which plots a graph with birth cohort on the x-axis instead
-of calendar year, `log` which causes the y-axis to be log-scale, and `ages` can be
-a vector of ages to filter to, for example `0:4` to plot stochastics of under 5s.
+![](figures/stoch_explorer.png)
+
+The features in the GUI closely map onto the parameters for `stochastic_graph`
+in the following ways:
+
+-   The tabs along the top let you choose a single graph, a comparison by
+    touchstone, or a comparison by modelling group, firstly for simply burdens,
+    and secondly for differences between scenarios. Different tabs therefore
+    cause different components in the sidebar to be available.
+-   One or two touchstones, groups, or scenarios will be available depending on
+    the tab.
+-   Whether the x-axis is time or age, and whether by cohort, are all selected
+    in the x-axis dropdown.
+-   The filter text, whether for time or for age, is a comma-separated list of
+    numbers or dash-separated ranges. So in a contrived example, `0-5,50,95-100`
+    would select the very young, very old, and exactly 50-year olds.
diff --git a/vignettes/figures/stoch_burden_diff.png b/vignettes/figures/stoch_burden_diff.png
new file mode 100644
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diff --git a/vignettes/figures/stoch_example_1.png b/vignettes/figures/stoch_example_1.png
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