diff --git a/hewr/DESCRIPTION b/hewr/DESCRIPTION
index 8d15ab91..4a451dd7 100644
--- a/hewr/DESCRIPTION
+++ b/hewr/DESCRIPTION
@@ -7,7 +7,7 @@ Description: What the package does (one paragraph).
 License: Apache License (>= 2)
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.3
+RoxygenNote: 7.3.2
 Imports:
     argparser,
     cowplot,
diff --git a/hewr/NAMESPACE b/hewr/NAMESPACE
index 83d59c73..316f2573 100644
--- a/hewr/NAMESPACE
+++ b/hewr/NAMESPACE
@@ -1,7 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(process_model_samples,pyrenew)
-S3method(process_model_samples,timeseries)
 export(epiweekly_samples_from_daily)
 export(format_timeseries_output)
 export(generate_exp_growth_pois)
@@ -16,9 +14,7 @@ export(parse_model_run_dir_path)
 export(parse_pyrenew_model_name)
 export(parse_variable_name)
 export(path_up_to)
-export(process_forecast)
 export(process_loc_forecast)
-export(process_model_samples)
 export(prop_from_timeseries)
 export(read_and_combine_data)
 export(to_tidy_draws_timeseries)
diff --git a/hewr/R/process_loc_forecast.R b/hewr/R/process_loc_forecast.R
index 73e4a108..4c2368a9 100644
--- a/hewr/R/process_loc_forecast.R
+++ b/hewr/R/process_loc_forecast.R
@@ -278,101 +278,6 @@ to_tidy_draws_timeseries <- function(
 }
 
 
-#' Process model samples based on model type
-#'
-#' S3 generic function that dispatches to appropriate method based on
-#' model type. This allows extensibility for new model types without
-#' changing existing code.
-#'
-#' @param model_type Character string indicating model type
-#'   ("pyrenew", "timeseries", "epiautogp")
-#' @param ... Additional arguments passed to methods. See specific methods
-#'   for details: [process_model_samples.pyrenew()],
-#'   [process_model_samples.timeseries()]
-#' @return Tibble of model samples
-#' @export
-process_model_samples <- function(model_type, ...) {
-  UseMethod("process_model_samples", structure(list(), class = model_type))
-}
-
-#' Process PyRenew model samples
-#'
-#' @param model_type Character string indicating model type
-#' @param model_run_dir Model run directory
-#' @param model_name Name of directory containing model outputs
-#' @param ts_samples Timeseries samples (if available)
-#' @param required_columns_e Required columns for output
-#' @param n_forecast_days Number of forecast days
-#' @param ... Additional arguments (unused)
-#' @return Tibble of PyRenew model samples
-#' @exportS3Method
-process_model_samples.pyrenew <- function(
-  model_type,
-  model_run_dir,
-  model_name,
-  ts_samples = NULL,
-  required_columns_e,
-  n_forecast_days,
-  ...
-) {
-  process_pyrenew_model(
-    model_run_dir = model_run_dir,
-    pyrenew_model_name = model_name,
-    ts_samples = ts_samples,
-    required_columns_e = required_columns_e,
-    n_forecast_days = n_forecast_days
-  )
-}
-
-#' Process timeseries model samples
-#'
-#' @param model_type Character string indicating model type
-#' @param model_run_dir Model run directory
-#' @param model_name Name of directory containing model outputs
-#' @param ts_samples Timeseries samples (required for this method)
-#' @param required_columns_e Required columns for output
-#' @param n_forecast_days Number of forecast days
-#' @param ... Additional arguments (unused)
-#' @return Tibble of timeseries model samples
-#' @exportS3Method
-process_model_samples.timeseries <- function(
-  model_type,
-  model_run_dir,
-  model_name,
-  ts_samples = NULL,
-  required_columns_e,
-  n_forecast_days,
-  ...
-) {
-  # For timeseries, ts_samples should already be loaded
-  # This is essentially a pass-through
-  if (is.null(ts_samples)) {
-    stop("ts_samples must be provided for timeseries model type")
-  }
-  ts_samples
-}
-
-#' Detect model type from model name
-#'
-#' Internal helper function to infer model type from naming conventions
-#'
-#' @param model_name Character string with model name
-#' @return Character string with model type ("pyrenew", "timeseries", etc.)
-#' @keywords internal
-detect_model_type <- function(model_name) {
-  if (
-    grepl("^ts_", model_name) ||
-      grepl("timeseries", model_name, ignore.case = TRUE)
-  ) {
-    return("timeseries")
-  } else if (grepl("epiautogp", model_name, ignore.case = TRUE)) {
-    return("epiautogp")
-  } else {
-    # Default to pyrenew for backward compatibility
-    return("pyrenew")
-  }
-}
-
 process_pyrenew_model <- function(
   model_run_dir,
   pyrenew_model_name,
@@ -477,52 +382,38 @@ process_pyrenew_model <- function(
 #' Process loc forecast
 #'
 #' @param model_run_dir Model run directory
-#' @param n_forecast_days An integer specifying the number of days to forecast.
-#' @param model_name Name of directory containing model outputs.
-#'   If provided, uses new S3 dispatch interface (overrides
-#'   pyrenew_model_name/timeseries_model_name).
 #' @param pyrenew_model_name Name of directory containing pyrenew
-#'   model outputs (legacy interface)
+#' model outputs
 #' @param timeseries_model_name Name of directory containing timeseries
-#'   model outputs (legacy interface)
-#' @param model_type Optional character string specifying model type
-#'   explicitly. Only used with model_name parameter.
-#'   If NULL (default), will auto-detect from model_name.
-#'   Options: "pyrenew", "timeseries", "epiautogp"
+#' model outputs
+#' @param n_forecast_days An integer specifying the number of days to forecast.
 #' @param ci_widths Vector of probabilities indicating one or more
-#'   central credible intervals to compute. Passed as the `.width`
-#'   argument to [ggdist::median_qi()]. Default `c(0.5, 0.8, 0.95)`.
+#' central credible intervals to compute. Passed as the `.width`
+#' argument to [ggdist::median_qi()]. Default `c(0.5, 0.8, 0.95)`.
 #' @param save Boolean indicating whether or not to save the output
-#'   to parquet files. Default `TRUE`.
-#' @return a list of 2 tibbles: `samples` and `ci`
+#' to parquet files. Default `TRUE`.
+#' @return a list of 8 tibbles:
+#' `daily_combined_training_eval_data`,
+#' `epiweekly_combined_training_eval_data`,
+#' `daily_samples`,
+#' `epiweekly_samples`,
+#' `epiweekly_with_epiweekly_other_samples`,
+#' `daily_ci`,
+#' `epiweekly_ci`,
+#' `epiweekly_with_epiweekly_other_ci`
 #' @export
 process_loc_forecast <- function(
   model_run_dir,
   n_forecast_days,
-  model_name = NA,
   pyrenew_model_name = NA,
   timeseries_model_name = NA,
-  model_type = NULL,
   ci_widths = c(0.5, 0.8, 0.95),
   save = TRUE
 ) {
-  # New interface: delegate to process_forecast
-  if (!is.na(model_name)) {
-    return(process_forecast(
-      model_run_dir = model_run_dir,
-      model_name = model_name,
-      n_forecast_days = n_forecast_days,
-      model_type = model_type,
-      ci_widths = ci_widths,
-      save = save
-    ))
-  }
-
-  # Legacy interface validation
   if (is.na(pyrenew_model_name) && is.na(timeseries_model_name)) {
     stop(
-      "Either `model_name` or `pyrenew_model_name`/",
-      "`timeseries_model_name` must be provided."
+      "Either `pyrenew_model_name` or `timeseries_model_name`",
+      "must be provided."
     )
   }
   model_name <- dplyr::if_else(
@@ -627,131 +518,3 @@ process_loc_forecast <- function(
 
   return(result)
 }
-
-#' Process location forecast with generic model name
-#'
-#' Simplified version of process_loc_forecast that accepts a single model_name
-#' parameter and auto-detects the model type. Uses S3 dispatch internally for
-#' extensibility.
-#'
-#' @param model_run_dir Model run directory
-#' @param model_name Name of directory containing model outputs
-#' @param n_forecast_days An integer specifying the number of days to forecast.
-#' @param model_type Optional character string specifying model type explicitly.
-#'   If NULL (default), will auto-detect from model_name.
-#'   Options: "pyrenew", "timeseries", "epiautogp"
-#' @param ci_widths Vector of probabilities indicating one or more
-#'   central credible intervals to compute. Passed as the `.width`
-#'   argument to [ggdist::median_qi()]. Default `c(0.5, 0.8, 0.95)`.
-#' @param save Boolean indicating whether or not to save the output
-#'   to parquet files. Default `TRUE`.
-#' @return a list of 2 tibbles: `samples` and `ci`
-#' @export
-process_forecast <- function(
-  model_run_dir,
-  model_name,
-  n_forecast_days,
-  model_type = NULL,
-  ci_widths = c(0.5, 0.8, 0.95),
-  save = TRUE
-) {
-  # Auto-detect model type if not specified
-  if (is.null(model_type)) {
-    model_type <- detect_model_type(model_name)
-  }
-
-  model_dir <- fs::path(model_run_dir, model_name)
-  data_col_types <- readr::cols(
-    date = readr::col_date(),
-    geo_value = readr::col_character(),
-    disease = readr::col_character(),
-    data_type = readr::col_character(),
-    .variable = readr::col_character(),
-    .value = readr::col_double()
-  )
-
-  # Load training data
-  daily_training_dat <- readr::read_tsv(
-    fs::path(model_dir, "data", "combined_training_data", ext = "tsv"),
-    col_types = data_col_types
-  )
-
-  epiweekly_training_dat <- readr::read_tsv(
-    fs::path(
-      model_dir,
-      "data",
-      "epiweekly_combined_training_data",
-      ext = "tsv"
-    ),
-    col_types = data_col_types
-  )
-
-  required_columns_e <- c(
-    ".chain",
-    ".iteration",
-    ".draw",
-    "date",
-    "geo_value",
-    "disease",
-    ".variable",
-    ".value",
-    "resolution",
-    "aggregated_numerator",
-    "aggregated_denominator"
-  )
-
-  # Load timeseries samples if needed (for any model that might use them)
-  ts_samples <- NULL
-  if (model_type %in% c("timeseries", "pyrenew")) {
-    # Check if timeseries model exists
-    ts_model_dirs <- fs::dir_ls(
-      model_run_dir,
-      regexp = "(^ts_|timeseries)",
-      type = "directory"
-    )
-
-    if (length(ts_model_dirs) > 0) {
-      ts_model_name <- fs::path_file(ts_model_dirs[1])
-      ts_samples <- load_and_aggregate_ts(
-        model_run_dir,
-        ts_model_name,
-        daily_training_dat,
-        epiweekly_training_dat,
-        required_columns = required_columns_e
-      )
-    }
-  }
-
-  # Dispatch to appropriate S3 method
-  model_samples_tidy <- process_model_samples(
-    model_type = model_type,
-    model_run_dir = model_run_dir,
-    model_name = model_name,
-    ts_samples = ts_samples,
-    required_columns_e = required_columns_e,
-    n_forecast_days = n_forecast_days
-  )
-
-  # Calculate credible intervals
-  ci <- model_samples_tidy |>
-    dplyr::select(-tidyselect::any_of(c(".chain", ".iteration", ".draw"))) |>
-    dplyr::group_by(dplyr::across(-".value")) |>
-    ggdist::median_qi(.width = ci_widths)
-
-  result <- list(
-    "samples" = model_samples_tidy,
-    "ci" = ci
-  )
-
-  if (save) {
-    save_dir <- fs::path(model_run_dir, model_name)
-    purrr::iwalk(result, \(tab, name) {
-      forecasttools::write_tabular(
-        tab,
-        fs::path(save_dir, name, ext = "parquet")
-      )
-    })
-  }
-
-  return(result)
-}
diff --git a/hewr/man/detect_model_type.Rd b/hewr/man/detect_model_type.Rd
deleted file mode 100644
index bb3cb446..00000000
--- a/hewr/man/detect_model_type.Rd
+++ /dev/null
@@ -1,18 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/process_loc_forecast.R
-\name{detect_model_type}
-\alias{detect_model_type}
-\title{Detect model type from model name}
-\usage{
-detect_model_type(model_name)
-}
-\arguments{
-\item{model_name}{Character string with model name}
-}
-\value{
-Character string with model type ("pyrenew", "timeseries", etc.)
-}
-\description{
-Internal helper function to infer model type from naming conventions
-}
-\keyword{internal}
diff --git a/hewr/man/process_forecast.Rd b/hewr/man/process_forecast.Rd
deleted file mode 100644
index 58209728..00000000
--- a/hewr/man/process_forecast.Rd
+++ /dev/null
@@ -1,41 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/process_loc_forecast.R
-\name{process_forecast}
-\alias{process_forecast}
-\title{Process location forecast with generic model name}
-\usage{
-process_forecast(
-  model_run_dir,
-  model_name,
-  n_forecast_days,
-  model_type = NULL,
-  ci_widths = c(0.5, 0.8, 0.95),
-  save = TRUE
-)
-}
-\arguments{
-\item{model_run_dir}{Model run directory}
-
-\item{model_name}{Name of directory containing model outputs}
-
-\item{n_forecast_days}{An integer specifying the number of days to forecast.}
-
-\item{model_type}{Optional character string specifying model type explicitly.
-If NULL (default), will auto-detect from model_name.
-Options: "pyrenew", "timeseries", "epiautogp"}
-
-\item{ci_widths}{Vector of probabilities indicating one or more
-central credible intervals to compute. Passed as the \code{.width}
-argument to \code{\link[ggdist:point_interval]{ggdist::median_qi()}}. Default \code{c(0.5, 0.8, 0.95)}.}
-
-\item{save}{Boolean indicating whether or not to save the output
-to parquet files. Default \code{TRUE}.}
-}
-\value{
-a list of 2 tibbles: \code{samples} and \code{ci}
-}
-\description{
-Simplified version of process_loc_forecast that accepts a single model_name
-parameter and auto-detects the model type. Uses S3 dispatch internally for
-extensibility.
-}
diff --git a/hewr/man/process_loc_forecast.Rd b/hewr/man/process_loc_forecast.Rd
index 260b690e..6787fc3a 100644
--- a/hewr/man/process_loc_forecast.Rd
+++ b/hewr/man/process_loc_forecast.Rd
@@ -7,10 +7,8 @@
 process_loc_forecast(
   model_run_dir,
   n_forecast_days,
-  model_name = NA,
   pyrenew_model_name = NA,
   timeseries_model_name = NA,
-  model_type = NULL,
   ci_widths = c(0.5, 0.8, 0.95),
   save = TRUE
 )
@@ -20,20 +18,11 @@ process_loc_forecast(
 
 \item{n_forecast_days}{An integer specifying the number of days to forecast.}
 
-\item{model_name}{Name of directory containing model outputs.
-If provided, uses new S3 dispatch interface (overrides
-pyrenew_model_name/timeseries_model_name).}
-
 \item{pyrenew_model_name}{Name of directory containing pyrenew
-model outputs (legacy interface)}
+model outputs}
 
 \item{timeseries_model_name}{Name of directory containing timeseries
-model outputs (legacy interface)}
-
-\item{model_type}{Optional character string specifying model type
-explicitly. Only used with model_name parameter.
-If NULL (default), will auto-detect from model_name.
-Options: "pyrenew", "timeseries", "epiautogp"}
+model outputs}
 
 \item{ci_widths}{Vector of probabilities indicating one or more
 central credible intervals to compute. Passed as the \code{.width}
@@ -43,7 +32,15 @@ argument to \code{\link[ggdist:point_interval]{ggdist::median_qi()}}. Default \c
 to parquet files. Default \code{TRUE}.}
 }
 \value{
-a list of 2 tibbles: \code{samples} and \code{ci}
+a list of 8 tibbles:
+\code{daily_combined_training_eval_data},
+\code{epiweekly_combined_training_eval_data},
+\code{daily_samples},
+\code{epiweekly_samples},
+\code{epiweekly_with_epiweekly_other_samples},
+\code{daily_ci},
+\code{epiweekly_ci},
+\code{epiweekly_with_epiweekly_other_ci}
 }
 \description{
 Process loc forecast
diff --git a/hewr/man/process_model_samples.Rd b/hewr/man/process_model_samples.Rd
deleted file mode 100644
index 9941a8a0..00000000
--- a/hewr/man/process_model_samples.Rd
+++ /dev/null
@@ -1,24 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/process_loc_forecast.R
-\name{process_model_samples}
-\alias{process_model_samples}
-\title{Process model samples based on model type}
-\usage{
-process_model_samples(model_type, ...)
-}
-\arguments{
-\item{model_type}{Character string indicating model type
-("pyrenew", "timeseries", "epiautogp")}
-
-\item{...}{Additional arguments passed to methods. See specific methods
-for details: \code{\link[=process_model_samples.pyrenew]{process_model_samples.pyrenew()}},
-\code{\link[=process_model_samples.timeseries]{process_model_samples.timeseries()}}}
-}
-\value{
-Tibble of model samples
-}
-\description{
-S3 generic function that dispatches to appropriate method based on
-model type. This allows extensibility for new model types without
-changing existing code.
-}
diff --git a/hewr/man/process_model_samples.pyrenew.Rd b/hewr/man/process_model_samples.pyrenew.Rd
deleted file mode 100644
index f92c8577..00000000
--- a/hewr/man/process_model_samples.pyrenew.Rd
+++ /dev/null
@@ -1,37 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/process_loc_forecast.R
-\name{process_model_samples.pyrenew}
-\alias{process_model_samples.pyrenew}
-\title{Process PyRenew model samples}
-\usage{
-\method{process_model_samples}{pyrenew}(
-  model_type,
-  model_run_dir,
-  model_name,
-  ts_samples = NULL,
-  required_columns_e,
-  n_forecast_days,
-  ...
-)
-}
-\arguments{
-\item{model_type}{Character string indicating model type}
-
-\item{model_run_dir}{Model run directory}
-
-\item{model_name}{Name of directory containing model outputs}
-
-\item{ts_samples}{Timeseries samples (if available)}
-
-\item{required_columns_e}{Required columns for output}
-
-\item{n_forecast_days}{Number of forecast days}
-
-\item{...}{Additional arguments (unused)}
-}
-\value{
-Tibble of PyRenew model samples
-}
-\description{
-Process PyRenew model samples
-}
diff --git a/hewr/man/process_model_samples.timeseries.Rd b/hewr/man/process_model_samples.timeseries.Rd
deleted file mode 100644
index 40edc46b..00000000
--- a/hewr/man/process_model_samples.timeseries.Rd
+++ /dev/null
@@ -1,37 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/process_loc_forecast.R
-\name{process_model_samples.timeseries}
-\alias{process_model_samples.timeseries}
-\title{Process timeseries model samples}
-\usage{
-\method{process_model_samples}{timeseries}(
-  model_type,
-  model_run_dir,
-  model_name,
-  ts_samples = NULL,
-  required_columns_e,
-  n_forecast_days,
-  ...
-)
-}
-\arguments{
-\item{model_type}{Character string indicating model type}
-
-\item{model_run_dir}{Model run directory}
-
-\item{model_name}{Name of directory containing model outputs}
-
-\item{ts_samples}{Timeseries samples (required for this method)}
-
-\item{required_columns_e}{Required columns for output}
-
-\item{n_forecast_days}{Number of forecast days}
-
-\item{...}{Additional arguments (unused)}
-}
-\value{
-Tibble of timeseries model samples
-}
-\description{
-Process timeseries model samples
-}
diff --git a/hewr/tests/testthat/test_process_loc_forecast.R b/hewr/tests/testthat/test_process_loc_forecast.R
index 434c4c11..48660ee9 100644
--- a/hewr/tests/testthat/test_process_loc_forecast.R
+++ b/hewr/tests/testthat/test_process_loc_forecast.R
@@ -1,20 +1,3 @@
-# Common test fixtures
-fake_dir <- "/fake/dir"
-minimal_required_columns <- c("date", ".value")
-full_required_columns <- c(
-  ".chain",
-  ".iteration",
-  ".draw",
-  "date",
-  "geo_value",
-  "disease",
-  ".variable",
-  ".value",
-  "resolution",
-  "aggregated_numerator",
-  "aggregated_denominator"
-)
-
 example_train_dat <- tibble::tibble(
   geo_value = "CA",
   disease = "COVID-19",
@@ -98,154 +81,3 @@ test_that("to_tidy_draws_timeseries() works as expected", {
 
   expect_equal(result, expected)
 })
-
-test_that("detect_model_type() correctly identifies model types", {
-  # Test timeseries detection with ts_ prefix
-  expect_equal(detect_model_type("ts_model_v1"), "timeseries")
-  expect_equal(detect_model_type("ts_ensemble"), "timeseries")
-
-  # Test timeseries detection with name containing "timeseries"
-  expect_equal(detect_model_type("timeseries_model"), "timeseries")
-  expect_equal(detect_model_type("my_TimeSeries_model"), "timeseries")
-
-  # Test epiautogp detection
-  expect_equal(detect_model_type("epiautogp_model"), "epiautogp")
-  expect_equal(detect_model_type("EpiAutoGP_v2"), "epiautogp")
-
-  # Test default to pyrenew
-  expect_equal(detect_model_type("pyrenew_hew"), "pyrenew")
-  expect_equal(detect_model_type("pyrenew_e"), "pyrenew")
-  expect_equal(detect_model_type("some_other_model"), "pyrenew")
-})
-
-test_that("process_model_samples S3 dispatch works correctly", {
-  # Test that the generic function exists and has the right class
-  expect_true(is.function(process_model_samples))
-
-  # Test that methods exist for expected classes
-  expect_true(
-    "process_model_samples.pyrenew" %in%
-      methods("process_model_samples")
-  )
-  expect_true(
-    "process_model_samples.timeseries" %in%
-      methods("process_model_samples")
-  )
-})
-
-test_that("process_model_samples.timeseries validates ts_samples", {
-  # Should error when ts_samples is NULL
-  expect_error(
-    process_model_samples.timeseries(
-      model_type = "timeseries",
-      model_run_dir = fake_dir,
-      model_name = "ts_model",
-      ts_samples = NULL,
-      required_columns_e = minimal_required_columns,
-      n_forecast_days = 7
-    ),
-    "ts_samples must be provided for timeseries model type"
-  )
-})
-
-test_that("process_model_samples.timeseries returns ts_samples", {
-  # Create mock ts_samples
-  mock_ts_samples <- tibble::tibble(
-    .chain = 1,
-    .iteration = 1,
-    .draw = 1,
-    date = as.Date("2024-01-01"),
-    geo_value = "US",
-    disease = "COVID-19",
-    .variable = "other_ed_visits",
-    .value = 100,
-    resolution = "daily",
-    aggregated_numerator = FALSE,
-    aggregated_denominator = NA
-  )
-
-  result <- process_model_samples.timeseries(
-    model_type = "timeseries",
-    model_run_dir = fake_dir,
-    model_name = "ts_model",
-    ts_samples = mock_ts_samples,
-    required_columns_e = minimal_required_columns,
-    n_forecast_days = 7
-  )
-
-  # Should return the ts_samples unchanged
-  expect_equal(result, mock_ts_samples)
-})
-
-test_that("process_model_samples.pyrenew dispatches correctly", {
-  # This test just verifies the S3 method exists and dispatches
-  # We expect it to error since we're not providing real data/files
-  # The key is that it calls the method (for code coverage)
-
-  expect_error(
-    process_model_samples.pyrenew(
-      model_type = "pyrenew",
-      model_run_dir = "any_path",
-      model_name = "pyrenew_h",
-      ts_samples = NULL,
-      required_columns_e = minimal_required_columns,
-      n_forecast_days = 7
-    )
-  )
-
-  # Verify the method exists
-  expect_true(
-    "process_model_samples.pyrenew" %in% methods("process_model_samples")
-  )
-})
-
-test_that("process_loc_forecast delegates correctly", {
-  # Test that process_loc_forecast calls process_forecast when
-  # model_name is provided by checking that it doesn't use the
-  # legacy code path
-
-  # Create a simple test: when model_name is provided, function
-  # should attempt to call process_forecast, which will try to
-  # read files. When model_name is NA, it uses the legacy path
-  # with different error message
-
-  # Test with model_name provided (new interface)
-  expect_error(
-    process_loc_forecast(
-      model_run_dir = fake_dir,
-      n_forecast_days = 7,
-      model_name = "test_model",
-      save = FALSE
-    ),
-    # This error comes from process_forecast trying to read
-    # training data
-    "does not exist"
-  )
-
-  # Test with legacy interface - should give different error
-  expect_error(
-    process_loc_forecast(
-      model_run_dir = fake_dir,
-      n_forecast_days = 7,
-      model_name = NA,
-      pyrenew_model_name = NA,
-      timeseries_model_name = NA
-    ),
-    "Either `model_name` or `pyrenew_model_name`"
-  )
-})
-
-test_that("process_loc_forecast validates legacy interface parameters", {
-  # Should error when neither model_name nor pyrenew/timeseries
-  # names provided
-  expect_error(
-    process_loc_forecast(
-      model_run_dir = fake_dir,
-      n_forecast_days = 7,
-      model_name = NA,
-      pyrenew_model_name = NA,
-      timeseries_model_name = NA
-    ),
-    "Either `model_name` or `pyrenew_model_name`"
-  )
-})
diff --git a/hewr/tests/testthat/test_timeseries_utils.R b/hewr/tests/testthat/test_timeseries_utils.R
deleted file mode 100644
index e4f54218..00000000
--- a/hewr/tests/testthat/test_timeseries_utils.R
+++ /dev/null
@@ -1,183 +0,0 @@
-# Common test data fixtures
-common_required_columns <- c(
-  ".draw",
-  "date",
-  "geo_value",
-  "disease",
-  ".variable",
-  ".value",
-  "resolution",
-  "aggregated_numerator",
-  "aggregated_denominator"
-)
-
-base_date <- as.Date("2024-01-01")
-
-test_that("format_timeseries_output formats forecast data correctly", {
-  # Create minimal test data
-  forecast_data <- tibble::tibble(
-    date = base_date + 0:2,
-    .draw = c(1, 1, 1),
-    observed_ed_visits = c(10, 15, 20),
-    other_ed_visits = c(5, 7, 9)
-  )
-
-  result <- format_timeseries_output(
-    forecast_data = forecast_data,
-    geo_value = "US",
-    disease = "COVID-19",
-    resolution = "daily",
-    output_type_id = ".draw"
-  )
-
-  # Check that output has expected structure
-  expect_s3_class(result, "data.frame")
-  expect_true(all(
-    c(
-      "date",
-      "geo_value",
-      "disease",
-      "resolution",
-      "aggregated_numerator",
-      "aggregated_denominator",
-      ".variable",
-      ".draw",
-      ".value"
-    ) %in%
-      colnames(result)
-  ))
-
-  # Check that geo_value and disease are set correctly
-  expect_true(all(result$geo_value == "US"))
-  expect_true(all(result$disease == "COVID-19"))
-  expect_true(all(result$resolution == "daily"))
-
-  # Check aggregation flags
-  expect_true(all(result$aggregated_numerator == FALSE))
-
-  # Check that data was pivoted (should have 2 variables x 3 dates = 6 rows)
-  expect_equal(nrow(result), 6)
-})
-
-test_that("format_timeseries_output handles proportion variables", {
-  forecast_data <- tibble::tibble(
-    date = base_date,
-    .draw = 1,
-    prop_disease_ed_visits = 0.5
-  )
-
-  result <- format_timeseries_output(
-    forecast_data = forecast_data,
-    geo_value = "CA",
-    disease = "Influenza",
-    resolution = "epiweekly",
-    output_type_id = ".draw"
-  )
-
-  # Proportion variables should have aggregated_denominator = FALSE
-  prop_row <- result[result$.variable == "prop_disease_ed_visits", ]
-  expect_equal(prop_row$aggregated_denominator, FALSE)
-})
-
-test_that("prop_from_timeseries calculates proportions correctly", {
-  e_denominator_samples <- tibble::tibble(
-    resolution = "daily",
-    .draw = 1:3,
-    date = base_date,
-    geo_value = "US",
-    disease = "COVID-19",
-    other_ed_visits = c(5, 10, 15)
-  )
-
-  e_numerator_samples <- tibble::tibble(
-    resolution = "daily",
-    .draw = 1:3,
-    date = base_date,
-    geo_value = "US",
-    disease = "COVID-19",
-    observed_ed_visits = c(10, 20, 30),
-    aggregated_numerator = FALSE
-  )
-
-  result <- prop_from_timeseries(
-    e_denominator_samples,
-    e_numerator_samples,
-    common_required_columns
-  )
-
-  # Check that proportions are calculated correctly
-  # prop = {observed \over observed + other}
-  # For draw 1: 10 / (10 + 5) = 0.6666...
-  # For draw 2: 20 / (20 + 10) = 0.6666...
-  # For draw 3: 30 / (30 + 15) = 0.6666...
-  expect_equal(nrow(result), 3)
-  expect_true(all(result$.variable == "prop_disease_ed_visits"))
-  expect_equal(result$.value, rep(2 / 3, 3), tolerance = 1e-10)
-})
-
-test_that("epiweekly_samples_from_daily aggregates correctly", {
-  # Create test data - simple smoke test
-  # Use dates that align with epiweeks (Sunday to Saturday)
-  daily_samples <- tibble::tibble(
-    .draw = rep(1, 7),
-    date = as.Date("2024-01-07") + 0:6, # One complete epiweek
-    geo_value = "US",
-    disease = "COVID-19",
-    .variable = "observed_ed_visits",
-    .value = rep(10, 7),
-    resolution = "daily",
-    aggregated_numerator = FALSE,
-    aggregated_denominator = NA
-  )
-
-  result <- epiweekly_samples_from_daily(
-    daily_samples = daily_samples,
-    variables_to_aggregate = "observed_ed_visits",
-    required_columns = common_required_columns
-  )
-
-  # Basic smoke test - should aggregate successfully
-  expect_s3_class(result, "data.frame")
-  expect_true(nrow(result) >= 1)
-  expect_true(all(result$resolution == "epiweekly"))
-  expect_true(all(result$aggregated_numerator == TRUE))
-  expect_true(all(result$.variable == "observed_ed_visits"))
-})
-
-test_that("to_tidy_draws_timeseries combines forecast and observed", {
-  # Create minimal forecast data - 3 forecast dates x 2 draws = 6 rows
-  tidy_forecast <- tibble::tibble(
-    date = rep(as.Date("2024-01-08") + 0:2, each = 2),
-    .draw = rep(1:2, 3),
-    .variable = rep("observed_ed_visits", 6),
-    .value = c(15, 16, 17, 18, 19, 20),
-    resolution = "daily"
-  )
-
-  observed <- tibble::tibble(
-    date = base_date + 0:6,
-    .variable = "observed_ed_visits",
-    .value = 10:16
-  )
-
-  result <- to_tidy_draws_timeseries(
-    tidy_forecast = tidy_forecast,
-    observed = observed
-  )
-
-  # Should combine observed (7 days * 2 draws = 14) + forecast (6 rows)
-  expect_equal(nrow(result), 20)
-
-  # Should have .draw column first
-  expect_equal(colnames(result)[1], ".draw")
-
-  # All rows should have .draw values
-  expect_true(all(!is.na(result$.draw)))
-
-  # Check observed dates all have same value across draws
-  obs_dates <- observed$date
-  for (d in obs_dates) {
-    date_rows <- result[result$date == d, ]
-    expect_equal(length(unique(date_rows$.value)), 1)
-  }
-})