diff --git a/hewr/DESCRIPTION b/hewr/DESCRIPTION
index 4a451dd7..8d15ab91 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: 7.3.2
+RoxygenNote: 7.3.3
 Imports:
     argparser,
     cowplot,
diff --git a/hewr/NAMESPACE b/hewr/NAMESPACE
index 316f2573..83d59c73 100644
--- a/hewr/NAMESPACE
+++ b/hewr/NAMESPACE
@@ -1,5 +1,7 @@
 # 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)
@@ -14,7 +16,9 @@ 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 4c2368a9..73e4a108 100644
--- a/hewr/R/process_loc_forecast.R
+++ b/hewr/R/process_loc_forecast.R
@@ -278,6 +278,101 @@ 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,
@@ -382,38 +477,52 @@ 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
+#'   model outputs (legacy interface)
 #' @param timeseries_model_name Name of directory containing timeseries
-#' model outputs
-#' @param n_forecast_days An integer specifying the number of days to forecast.
+#'   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"
 #' @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 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`
+#'   to parquet files. Default `TRUE`.
+#' @return a list of 2 tibbles: `samples` and `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 `pyrenew_model_name` or `timeseries_model_name`",
-      "must be provided."
+      "Either `model_name` or `pyrenew_model_name`/",
+      "`timeseries_model_name` must be provided."
     )
   }
   model_name <- dplyr::if_else(
@@ -518,3 +627,131 @@ 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
new file mode 100644
index 00000000..bb3cb446
--- /dev/null
+++ b/hewr/man/detect_model_type.Rd
@@ -0,0 +1,18 @@
+% 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
new file mode 100644
index 00000000..58209728
--- /dev/null
+++ b/hewr/man/process_forecast.Rd
@@ -0,0 +1,41 @@
+% 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 6787fc3a..260b690e 100644
--- a/hewr/man/process_loc_forecast.Rd
+++ b/hewr/man/process_loc_forecast.Rd
@@ -7,8 +7,10 @@
 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
 )
@@ -18,11 +20,20 @@ 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}
+model outputs (legacy interface)}
 
 \item{timeseries_model_name}{Name of directory containing timeseries
-model outputs}
+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"}
 
 \item{ci_widths}{Vector of probabilities indicating one or more
 central credible intervals to compute. Passed as the \code{.width}
@@ -32,15 +43,7 @@ argument to \code{\link[ggdist:point_interval]{ggdist::median_qi()}}. Default \c
 to parquet files. Default \code{TRUE}.}
 }
 \value{
-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}
+a list of 2 tibbles: \code{samples} and \code{ci}
 }
 \description{
 Process loc forecast
diff --git a/hewr/man/process_model_samples.Rd b/hewr/man/process_model_samples.Rd
new file mode 100644
index 00000000..9941a8a0
--- /dev/null
+++ b/hewr/man/process_model_samples.Rd
@@ -0,0 +1,24 @@
+% 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
new file mode 100644
index 00000000..f92c8577
--- /dev/null
+++ b/hewr/man/process_model_samples.pyrenew.Rd
@@ -0,0 +1,37 @@
+% 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
new file mode 100644
index 00000000..40edc46b
--- /dev/null
+++ b/hewr/man/process_model_samples.timeseries.Rd
@@ -0,0 +1,37 @@
+% 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 48660ee9..434c4c11 100644
--- a/hewr/tests/testthat/test_process_loc_forecast.R
+++ b/hewr/tests/testthat/test_process_loc_forecast.R
@@ -1,3 +1,20 @@
+# 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",
@@ -81,3 +98,154 @@ 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
new file mode 100644
index 00000000..e4f54218
--- /dev/null
+++ b/hewr/tests/testthat/test_timeseries_utils.R
@@ -0,0 +1,183 @@
+# 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)
+  }
+})
diff --git a/pipelines/plot_and_save_loc_forecast.R b/pipelines/plot_and_save_loc_forecast.R
index ed363f47..7536409b 100644
--- a/pipelines/plot_and_save_loc_forecast.R
+++ b/pipelines/plot_and_save_loc_forecast.R
@@ -65,10 +65,10 @@ save_forecast_figures <- function(
 
   parsed_model_run_dir <- parse_model_run_dir_path(model_run_dir)
   processed_forecast <- process_loc_forecast(
-    model_run_dir,
-    n_forecast_days,
-    pyrenew_model_name,
-    timeseries_model_name,
+    model_run_dir = model_run_dir,
+    n_forecast_days = n_forecast_days,
+    pyrenew_model_name = pyrenew_model_name,
+    timeseries_model_name = timeseries_model_name,
     save = TRUE
   )