diff --git a/pipelines/azure_command_center.py b/pipelines/azure_command_center.py
index 0479a013..ebc86d7f 100644
--- a/pipelines/azure_command_center.py
+++ b/pipelines/azure_command_center.py
@@ -17,6 +17,8 @@
 from pipelines.postprocess_forecast_batches import main as postprocess
 
 DEFAULT_RNG_KEY = 12345
+DEFAULT_TRAINING_DAYS = 150
+DEFAULT_EXCLUDE_LAST_N_DAYS = 1
 load_dotenv()
 console = Console()
 
@@ -54,8 +56,8 @@ def setup_job_append_id(
     additional_forecast_letters: str = "",
     container_image_name: str = "pyrenew-hew",
     container_image_version: str = "latest",
-    n_training_days: int = 150,
-    exclude_last_n_days: int = 1,
+    n_training_days: int = DEFAULT_TRAINING_DAYS,
+    exclude_last_n_days: int = DEFAULT_EXCLUDE_LAST_N_DAYS,
     rng_key: int = DEFAULT_RNG_KEY,
     locations_include: list[str] | None = None,
     locations_exclude: list[str] | None = None,
@@ -166,33 +168,52 @@ def ask_about_reruns():
         "How many days to exclude for H signal?", default=1
     )
     rng_key = IntPrompt.ask("RNG seed for reproducibility?", default=DEFAULT_RNG_KEY)
+    n_training_days = IntPrompt.ask(
+        "Number of training days?", default=DEFAULT_TRAINING_DAYS
+    )
 
     return {
         "locations_include": locations_include,
         "e_exclude_last_n_days": e_exclude_last_n_days,
         "h_exclude_last_n_days": h_exclude_last_n_days,
         "rng_key": rng_key,
+        "n_training_days": n_training_days,
     }
 
 
-def compute_skips(e_exclude_last_n_days: int, h_exclude_last_n_days: int, rng_key: int):
-    skip_e = e_exclude_last_n_days == 1 and rng_key == DEFAULT_RNG_KEY
-    skip_h = h_exclude_last_n_days == 1 and rng_key == DEFAULT_RNG_KEY
-    skip_he = (
-        max(e_exclude_last_n_days, h_exclude_last_n_days) == 1
-        and rng_key == DEFAULT_RNG_KEY
+def compute_skips(
+    e_exclude_last_n_days: int,
+    h_exclude_last_n_days: int,
+    rng_key: int,
+    n_training_days: int,
+):
+    run_due_to_param_change = (
+        n_training_days != DEFAULT_TRAINING_DAYS or rng_key != DEFAULT_RNG_KEY
     )
+    if run_due_to_param_change:
+        skip_e = False
+        skip_h = False
+        skip_he = False
+    else:
+        skip_e = e_exclude_last_n_days == DEFAULT_EXCLUDE_LAST_N_DAYS
+        skip_h = h_exclude_last_n_days == DEFAULT_EXCLUDE_LAST_N_DAYS
+        skip_he = skip_e and skip_h
     return {"skip_e": skip_e, "skip_h": skip_h, "skip_he": skip_he}
 
 
 def do_timeseries_reruns(
     locations_include: list[str] | None = None,
-    e_exclude_last_n_days: int = 1,
-    h_exclude_last_n_days: int = 1,
+    e_exclude_last_n_days: int = DEFAULT_EXCLUDE_LAST_N_DAYS,
+    h_exclude_last_n_days: int = DEFAULT_EXCLUDE_LAST_N_DAYS,
     rng_key: int = DEFAULT_RNG_KEY,  # not used, but kept for interface consistency
     append_id: str = "",
+    n_training_days: int = DEFAULT_TRAINING_DAYS,
 ):
-    skips = compute_skips(e_exclude_last_n_days, h_exclude_last_n_days, rng_key)
+    skips = compute_skips(
+        e_exclude_last_n_days, h_exclude_last_n_days, rng_key, n_training_days
+    )
+    he_exclude_last_n_days = max(e_exclude_last_n_days, h_exclude_last_n_days)
+    he_exclude_covered_by_e = he_exclude_last_n_days == e_exclude_last_n_days
 
     if skips["skip_e"]:
         print("Skipping Timeseries-E re-fitting due to E")
@@ -201,26 +222,31 @@ def do_timeseries_reruns(
             append_id=append_id,
             locations_include=locations_include,
             exclude_last_n_days=e_exclude_last_n_days,
+            n_training_days=n_training_days,
         )
-    if skips["skip_h"]:
-        print("Skipping Timeseries-E re-fitting due to H")
+    if skips["skip_he"] or he_exclude_covered_by_e:
+        print("Skipping Timeseries-E re-fitting due to HE*")
     else:
         fit_timeseries_e(
             append_id=append_id,
             locations_include=locations_include,
-            exclude_last_n_days=h_exclude_last_n_days,
+            exclude_last_n_days=he_exclude_last_n_days,
+            n_training_days=n_training_days,
         )
 
 
 def do_pyrenew_reruns(
     locations_include: list[str] | None = None,
-    e_exclude_last_n_days: int = 1,
-    h_exclude_last_n_days: int = 1,
+    e_exclude_last_n_days: int = DEFAULT_EXCLUDE_LAST_N_DAYS,
+    h_exclude_last_n_days: int = DEFAULT_EXCLUDE_LAST_N_DAYS,
     rng_key: int = DEFAULT_RNG_KEY,
     append_id: str = "",
+    n_training_days: int = DEFAULT_TRAINING_DAYS,
 ):
     he_exclude_last_n_days = max(e_exclude_last_n_days, h_exclude_last_n_days)
-    skips = compute_skips(e_exclude_last_n_days, h_exclude_last_n_days, rng_key)
+    skips = compute_skips(
+        e_exclude_last_n_days, h_exclude_last_n_days, rng_key, n_training_days
+    )
 
     if skips["skip_e"]:
         print("Skipping PyRenew-E re-fitting")
@@ -230,6 +256,7 @@ def do_pyrenew_reruns(
             locations_include=locations_include,
             exclude_last_n_days=e_exclude_last_n_days,
             rng_key=rng_key,
+            n_training_days=n_training_days,
         )
 
     if skips["skip_h"]:
@@ -240,6 +267,7 @@ def do_pyrenew_reruns(
             locations_include=locations_include,
             exclude_last_n_days=h_exclude_last_n_days,
             rng_key=rng_key,
+            n_training_days=n_training_days,
         )
         # fit_pyrenew_hw(
         #     append_id=append_id,
@@ -256,6 +284,7 @@ def do_pyrenew_reruns(
             locations_include=locations_include,
             exclude_last_n_days=he_exclude_last_n_days,
             rng_key=rng_key,
+            n_training_days=n_training_days,
         )
 
         # fit_pyrenew_hew(
@@ -429,10 +458,16 @@ def ask_integer_choice(choices):
             # fit_pyrenew_hew(append_id=current_time)
         elif selected_choice == "Rerun Timeseries Models":
             ask_about_reruns_input = ask_about_reruns()
-            do_timeseries_reruns(append_id=current_time, **ask_about_reruns_input)
+            do_timeseries_reruns(
+                append_id=current_time,
+                **ask_about_reruns_input,
+            )
         elif selected_choice == "Rerun PyRenew Models":
             ask_about_reruns_input = ask_about_reruns()
-            do_pyrenew_reruns(append_id=current_time, **ask_about_reruns_input)
+            do_pyrenew_reruns(
+                append_id=current_time,
+                **ask_about_reruns_input,
+            )
         elif selected_choice == "Postprocess Forecast Batches":
             skip_existing = Confirm.ask(
                 "Skip processing for model batch directories that already have figures?",
diff --git a/pipelines/postprocess_forecast_batches.py b/pipelines/postprocess_forecast_batches.py
index 6bcad792..fa56a6f2 100644
--- a/pipelines/postprocess_forecast_batches.py
+++ b/pipelines/postprocess_forecast_batches.py
@@ -53,7 +53,7 @@ def process_model_batch_dir(model_batch_dir_path: Path, plot_ext: str = "pdf") -
 
 def main(
     base_forecast_dir: Path | str,
-    diseases: list[str] = ["COVID-19", "Influenza", "RSV"],
+    diseases: list[str] | set[str] = ["COVID-19", "Influenza", "RSV"],
     skip_existing: bool = True,
 ) -> None:
     logging.basicConfig(level=logging.INFO)