diff --git a/src/iddata/loader.py b/src/iddata/loader.py
index 21ac167..9796149 100644
--- a/src/iddata/loader.py
+++ b/src/iddata/loader.py
@@ -10,7 +10,7 @@
 from iddata import utils
 
 
-class FluDataLoader():
+class DiseaseDataLoader():
   def __init__(self) -> None:
     self.data_raw = "https://infectious-disease-data.s3.amazonaws.com/data-raw/"
 
@@ -303,10 +303,7 @@ def load_ilinet(self,
     return dat
 
 
-  def load_nhsn(self, rates=True, drop_pandemic_seasons=True, as_of=None):
-    if not drop_pandemic_seasons:
-      raise NotImplementedError("Functionality for loading all seasons of NHSN data with specified as_of date is not implemented.")
-    
+  def load_nhsn(self, disease="flu", rates=True, drop_pandemic_seasons=True, as_of=None):
     if as_of is None:
       as_of = datetime.date.today()
     
@@ -314,9 +311,15 @@ def load_nhsn(self, rates=True, drop_pandemic_seasons=True, as_of=None):
       as_of = datetime.date.fromisoformat(as_of)
 
     if as_of < datetime.date.fromisoformat("2024-11-15"):
+      if not drop_pandemic_seasons:
+        raise NotImplementedError("Functionality for loading all seasons of NHSN data with specified as_of date is not implemented.")
+      
+      if disease != "flu":
+        raise NotImplementedError(f"When loading NHSN data with an as_of date prior to 2024-11-15, only disease='flu' is supported; got {str(disease)}.")
       return self.load_nhsn_from_hhs(rates=rates, as_of=as_of)
     else:
       return self.load_nhsn_from_nhsn(
+        disease=disease,
         rates=rates,
         as_of=as_of,
         drop_pandemic_seasons=drop_pandemic_seasons
@@ -353,7 +356,11 @@ def load_nhsn_from_hhs(self, rates=True, as_of=None):
     return dat
 
 
-  def load_nhsn_from_nhsn(self, rates=True, as_of=None, drop_pandemic_seasons=True):
+  def load_nhsn_from_nhsn(self, disease="flu", rates=True, as_of=None, drop_pandemic_seasons=True):
+    valid_diseases = ["flu", "covid"]
+    if disease not in valid_diseases:
+        raise ValueError("For NHSN data, the only supported diseases are 'flu' and 'covid'.")
+    
     # find the largest stored file dated on or before the as_of date
     as_of_file_path = f"influenza-nhsn/nhsn-{str(as_of)}.csv"
     glob_results = s3fs.S3FileSystem(anon=True) \
@@ -363,7 +370,11 @@ def load_nhsn_from_nhsn(self, rates=True, as_of=None, drop_pandemic_seasons=True
     file_path = all_file_paths[-1]
     
     dat = pd.read_csv(self._construct_data_raw_url(file_path))
-    dat = dat[["Geographic aggregation", "Week Ending Date", "Total Influenza Admissions"]]
+    if disease == "flu":
+        inc_colname = "Total Influenza Admissions"
+    elif disease == "covid":
+        inc_colname = "Total COVID-19 Admissions"
+    dat = dat[["Geographic aggregation", "Week Ending Date"] + [inc_colname]]
     dat.columns = ["abbreviation", "wk_end_date", "inc"]
     
     # rename USA to US
diff --git a/tests/iddata/unit/test_load_data.py b/tests/iddata/unit/test_load_data.py
index d3c0efe..e7d1758 100644
--- a/tests/iddata/unit/test_load_data.py
+++ b/tests/iddata/unit/test_load_data.py
@@ -2,11 +2,11 @@
 
 import numpy as np
 import pytest
-from iddata.loader import FluDataLoader
+from iddata.loader import DiseaseDataLoader
 
 
 def test_load_data_sources():
-    fdl = FluDataLoader()
+    fdl = DiseaseDataLoader()
     
     sources_options = [
         ["nhsn"],
@@ -29,7 +29,7 @@ def test_load_data_sources():
         "2022/23", "2023-12-23")
 ])
 def test_load_data_nhsn_kwargs(test_kwargs, season_expected, wk_end_date_expected):
-    fdl = FluDataLoader()
+    fdl = DiseaseDataLoader()
     
     df = fdl.load_data(sources=["nhsn"], nhsn_kwargs=test_kwargs)
     
@@ -47,7 +47,7 @@ def test_load_data_nhsn_kwargs(test_kwargs, season_expected, wk_end_date_expecte
     ({"drop_pandemic_seasons": True}, True)
 ])
 def test_load_data_ilinet_kwargs(test_kwargs, expect_all_na):
-    fdl = FluDataLoader()
+    fdl = DiseaseDataLoader()
     
     df = fdl.load_data(sources=["ilinet"], ilinet_kwargs=test_kwargs)
     
@@ -63,7 +63,7 @@ def test_load_data_ilinet_kwargs(test_kwargs, expect_all_na):
     ({"locations": ["California", "Colorado", "Connecticut"]})
 ])
 def test_load_data_flusurvnet_kwargs(test_kwargs):
-    fdl = FluDataLoader()
+    fdl = DiseaseDataLoader()
     
     #flusurv_kwargs
     df = fdl.load_data(sources=["flusurvnet"], flusurvnet_kwargs=test_kwargs)