diff --git a/_targets_eval_postprocessing.R b/_targets_eval_postprocessing.R
index 8a6bffdc..410bce14 100644
--- a/_targets_eval_postprocessing.R
+++ b/_targets_eval_postprocessing.R
@@ -302,13 +302,8 @@ head_to_head_targets <- list(
   # forecast dates with sufficient wastewater for both ww model and hosp only
   # model. Then join the convergence df
   tar_target(
-    name = max_nowcast_days,
-    command = as.numeric(
-      max(all_ww_hosp_quantiles$forecast_date) -
-        max(all_ww_hosp_quantiles$date[!is.na(
-          all_ww_hosp_quantiles$calib_data
-        )])
-    )
+    name = last_hosp_data_date_map,
+    command = get_last_hosp_data_date_map(all_hosp_model_quantiles)
   ),
   tar_target(
     name = hosp_quantiles_filtered,
@@ -329,6 +324,10 @@ head_to_head_targets <- list(
           "forecast_date"
         )
       ) |>
+      dplyr::left_join(
+        last_hosp_data_date_map,
+        by = c("location", "forecast_date")
+      ) |>
       dplyr::left_join(
         epidemic_phases,
         by = c(
@@ -336,7 +335,7 @@ head_to_head_targets <- list(
           "date" = "reference_date"
         )
       ) |>
-      add_horizons(max_nowcast_days = max_nowcast_days)
+      add_horizons()
   ),
   # Do the same thing for the sampled scores, combining ww and hosp under
   # the status quo scenario, filtering to the locations and forecast dates
@@ -359,6 +358,10 @@ head_to_head_targets <- list(
           "forecast_date"
         )
       ) |>
+      dplyr::left_join(
+        last_hosp_data_date_map,
+        by = c("location", "forecast_date")
+      ) |>
       dplyr::left_join(
         epidemic_phases,
         by = c(
@@ -366,7 +369,7 @@ head_to_head_targets <- list(
           "date" = "reference_date"
         )
       ) |>
-      add_horizons(max_nowcast_days = max_nowcast_days)
+      add_horizons()
   ),
   # Repeat for the quantile-based scores
   tar_target(
@@ -387,6 +390,10 @@ head_to_head_targets <- list(
           "forecast_date"
         )
       ) |>
+      dplyr::left_join(
+        last_hosp_data_date_map,
+        by = c("location", "forecast_date")
+      ) |>
       dplyr::left_join(
         epidemic_phases,
         by = c(
@@ -394,7 +401,7 @@ head_to_head_targets <- list(
           "date" = "reference_date"
         )
       ) |>
-      add_horizons(max_nowcast_days = max_nowcast_days)
+      add_horizons()
   )
 )
 
diff --git a/wweval/NAMESPACE b/wweval/NAMESPACE
index 647e3e2c..f4272eef 100644
--- a/wweval/NAMESPACE
+++ b/wweval/NAMESPACE
@@ -27,6 +27,7 @@ export(get_hosp_values)
 export(get_input_hosp_data)
 export(get_input_ww_data)
 export(get_last_hosp_data_date)
+export(get_last_hosp_data_date_map)
 export(get_model_draws_w_data)
 export(get_model_path)
 export(get_n_states_improved_plot)
diff --git a/wweval/R/add_horizons.R b/wweval/R/add_horizons.R
index 7bb44090..15c4d6da 100644
--- a/wweval/R/add_horizons.R
+++ b/wweval/R/add_horizons.R
@@ -2,31 +2,27 @@
 #'
 #' @description
 #' This function takes in a tibble of scores on the nowcasts/forecasts
-#' containing the columns `date` and `forecast_date` and adds the following
-#' columns: `horizon_days` (an integer) and `horizon` (a string to be used for
-#' categorical grouping of horizons)
+#' containing the columns `date`, `forecast_date`, and `last_hosp_data_date`,
+#' and adds the following columns: `horizon_days` (an integer) and
+#'  `horizon` (a string to be used for categorical grouping of horizons)
 #'
 #'
 #' @param df A tibble containing either forecasts or scores (or both)
-#' and the following required columns: `date`and `forecast_date`
-#' @param max_nowcast_days An integer indicating the expected maximum number
-#' of weeks where date < forecast date. If the output contains values beyond
-#' this threshold, an error will be thrown which may indicate that the scores
-#' contain scores for calibrated data.
+#' and the following required columns: `date`,`forecast_date`,
+#' `last_hosp_data_date`
 #'
 #' @return a tibble containing the same columns as `df` plus
 #' `horizon_days` and `horizon`
 #' @export
-add_horizons <- function(df,
-                         max_nowcast_days) {
+add_horizons <- function(df) {
   df_w_horizons <- df |>
     dplyr::mutate(
       horizon_days = as.numeric(date - forecast_date)
     ) |>
     dplyr::mutate(
       horizon = dplyr::case_when(
-        horizon_days <= -!!max_nowcast_days ~ "calibration",
-        horizon_days > -!!max_nowcast_days & horizon_days <= 0 ~ "nowcast",
+        date <= last_hosp_data_date & horizon_days <= 0 ~ "calibration",
+        date > last_hosp_data_date & horizon_days <= 0 ~ "nowcast",
         horizon_days > 0 & horizon_days <= 7 ~ "1 wk",
         horizon_days > 7 & horizon_days <= 14 ~ "2 wks",
         horizon_days > 14 & horizon_days <= 21 ~ "3 wks",
@@ -37,3 +33,25 @@ add_horizons <- function(df,
 
   return(df_w_horizons)
 }
+
+#' Get a map of the location, forecast date, and last hospital admissions data
+#' date
+#'
+#' @param df A tibble containing the following columns: `forecast_date`,
+#' `location`,`date`, `calib_data`
+#'
+#' @return A tibble with that maps the unique combinations of `location` and
+#' `forecast` date to the last hospital admissions data date
+#' `last_hosp_data_date`
+#' @export
+get_last_hosp_data_date_map <- function(df) {
+  map <- df |>
+    dplyr::group_by(forecast_date, location) |>
+    dplyr::filter(!is.na(calib_data)) |>
+    dplyr::summarise(
+      last_hosp_data_date = max(date)
+    ) |>
+    dplyr::ungroup()
+
+  return(map)
+}
diff --git a/wweval/man/add_horizons.Rd b/wweval/man/add_horizons.Rd
index 005a213c..40660a3d 100644
--- a/wweval/man/add_horizons.Rd
+++ b/wweval/man/add_horizons.Rd
@@ -4,16 +4,12 @@
 \alias{add_horizons}
 \title{Add columns indicating the horizon to the scores}
 \usage{
-add_horizons(df, max_nowcast_days)
+add_horizons(df)
 }
 \arguments{
 \item{df}{A tibble containing either forecasts or scores (or both)
-and the following required columns: \code{date}and \code{forecast_date}}
-
-\item{max_nowcast_days}{An integer indicating the expected maximum number
-of weeks where date < forecast date. If the output contains values beyond
-this threshold, an error will be thrown which may indicate that the scores
-contain scores for calibrated data.}
+and the following required columns: \code{date},\code{forecast_date},
+\code{last_hosp_data_date}}
 }
 \value{
 a tibble containing the same columns as \code{df} plus
@@ -21,7 +17,7 @@ a tibble containing the same columns as \code{df} plus
 }
 \description{
 This function takes in a tibble of scores on the nowcasts/forecasts
-containing the columns \code{date} and \code{forecast_date} and adds the following
-columns: \code{horizon_days} (an integer) and \code{horizon} (a string to be used for
-categorical grouping of horizons)
+containing the columns \code{date}, \code{forecast_date}, and \code{last_hosp_data_date},
+and adds the following columns: \code{horizon_days} (an integer) and
+\code{horizon} (a string to be used for categorical grouping of horizons)
 }