diff --git a/workflow/notebooks/plot-all-strains-pangolin.py.ipynb b/workflow/notebooks/plot-all-strains-pangolin.py.ipynb
index 620043c68..502048479 100644
--- a/workflow/notebooks/plot-all-strains-pangolin.py.ipynb
+++ b/workflow/notebooks/plot-all-strains-pangolin.py.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "for f in snakemake.input:\n",
     "    calls = pd.read_csv(f)\n",
-    "    calls = calls[calls[\"status\"] != \"fail\"]\n",
+    "    calls = calls[calls[\"qc_status\"] != \"fail\"]\n",
     "    all_calls.append(calls)\n",
     "\n",
     "all_calls = pd.concat(all_calls)\n",
diff --git a/workflow/scripts/aggregate-pangolin-calls-per-stage.py b/workflow/scripts/aggregate-pangolin-calls-per-stage.py
index 228b1b7de..f404bf1dc 100644
--- a/workflow/scripts/aggregate-pangolin-calls-per-stage.py
+++ b/workflow/scripts/aggregate-pangolin-calls-per-stage.py
@@ -25,7 +25,7 @@
 
 pangolin_calls_by_stage = pd.concat(pangolin_calls, axis=0, ignore_index=True)
 
-failed_called = (pangolin_calls_by_stage["status"] == "fail") | (
+failed_called = (pangolin_calls_by_stage["qc_status"] == "fail") | (
     pangolin_calls_by_stage["lineage"] == "None"
 )
 pangolin_calls_by_stage.loc[failed_called, "lineage"] = (
diff --git a/workflow/scripts/evaluate-strain-call-error.py b/workflow/scripts/evaluate-strain-call-error.py
index 112d9d3a9..cca966c95 100644
--- a/workflow/scripts/evaluate-strain-call-error.py
+++ b/workflow/scripts/evaluate-strain-call-error.py
@@ -46,7 +46,7 @@ def load_pangolin_df(i, path):
         inplace=True,
     )
     pangolin_df.drop(
-        columns=["taxon", "pangoLEARN_version", "status", "note"], inplace=True
+        columns=["taxon", "pangoLEARN_version", "qc_status", "note"], inplace=True
     )
     pangolin_df["mix"] = i
     return pangolin_df
diff --git a/workflow/scripts/summarize-non-cov2.py b/workflow/scripts/summarize-non-cov2.py
index 32d5f3917..d517eaa70 100644
--- a/workflow/scripts/summarize-non-cov2.py
+++ b/workflow/scripts/summarize-non-cov2.py
@@ -15,10 +15,10 @@ def analyize_pangolin(sm_input, accessions):
             pango_df = pd.read_csv(pango_file)
             if pango_df.loc[0, "note"] == "seq_len:1":
                 temp_dict[sample] = "assembly failed"
-            elif pango_df.loc[0, "status"] == "fail":
+            elif pango_df.loc[0, "qc_status"] == "fail":
                 temp_dict[sample] = "is non-sars-cov-2"
             elif (
-                pango_df.loc[0, "status"] == "pass"
+                pango_df.loc[0, "qc_status"] == "pass"
                 and pango_df.loc[0, "lineage"] == "None"
             ):
                 temp_dict[sample] = "is non-sars-cov-2"