diff --git a/usafacts/delphi_usafacts/run.py b/usafacts/delphi_usafacts/run.py
index cc3e661c9..fd6e8cb47 100644
--- a/usafacts/delphi_usafacts/run.py
+++ b/usafacts/delphi_usafacts/run.py
@@ -6,28 +6,22 @@
 """
 from datetime import datetime, date, time, timedelta
 from itertools import product
-from functools import partial
 from os.path import join
 
 import numpy as np
 import pandas as pd
 from delphi_utils import (
-    read_params,
     create_export_csv,
+    read_params,
+    GeoMapper,
     S3ArchiveDiffer,
-    GeoMapper
+    Smoother
 )
 
 from .geo import geo_map
 from .pull import pull_usafacts_data
-from .smooth import (
-    identity,
-    kday_moving_average,
-)
-
 
 # global constants
-seven_day_moving_average = partial(kday_moving_average, k=7)
 METRICS = [
     "confirmed",
     "deaths",
@@ -55,9 +49,10 @@
 #     "incidence":            ("incid_prop", False),
 #     "cumulative_prop":      ("cumul_prop", False),
 # }
+
 SMOOTHERS_MAP = {
-    "unsmoothed":           (identity, '', False, lambda d: d - timedelta(days=7)),
-    "seven_day_average":    (seven_day_moving_average, '7dav_', True, lambda d: d),
+    "unsmoothed": (Smoother("identity"), "", False, lambda d: d - timedelta(days=7)),
+    "seven_day_average": (Smoother("moving_average", window_length=7), "7dav_", True, lambda d: d),
 }
 GEO_RESOLUTIONS = [
     "county",
@@ -99,7 +94,7 @@ def run_module():
         df = dfs[metric]
         # Aggregate to appropriate geographic resolution
         df = geo_map(df, geo_res, map_df, sensor)
-        df["val"] = SMOOTHERS_MAP[smoother][0](df[sensor].values)
+        df["val"] = SMOOTHERS_MAP[smoother][0].smooth(df[sensor].values)
         df["se"] = np.nan
         df["sample_size"] = np.nan
         # Drop early entries where data insufficient for smoothing
diff --git a/usafacts/delphi_usafacts/smooth.py b/usafacts/delphi_usafacts/smooth.py
deleted file mode 100644
index 20524b340..000000000
--- a/usafacts/delphi_usafacts/smooth.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""Functions for smoothing signals."""
-# -*- coding: utf-8 -*-
-"""Smoothing functions."""
-import numpy as np
-
-def identity(x):
-    """Trivial "smoother" that does no smoothing.
-
-    Parameters
-    ----------
-    x: np.ndarray
-        Input array
-
-    Returns
-    -------
-    np.ndarray:
-        Same as x
-    """
-    return x
-
-def kday_moving_average(x, k):
-    """Compute k-day moving average on x.
-
-    Parameters
-    ----------
-    x: np.ndarray
-        Input array
-
-    Returns
-    -------
-    np.ndarray:
-        k-day moving average.  The first k-1 entries are np.nan.
-    """
-    if not isinstance(k, int):
-        raise ValueError('k must be int.')
-    # temp = np.append(np.zeros(k - 1), x)
-    temp = np.append(np.nan*np.ones(k-1), x)
-    y = np.convolve(temp, np.ones(k, dtype=int), 'valid') / k
-    return y
diff --git a/usafacts/tests/test_smooth.py b/usafacts/tests/test_smooth.py
deleted file mode 100644
index b50089530..000000000
--- a/usafacts/tests/test_smooth.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import pytest
-
-from os import listdir
-from os.path import join
-
-import numpy as np
-import pandas as pd
-from delphi_usafacts.run import run_module
-
-class TestSmooth:
-    def test_output_files_smoothed(self, run_as_module):
-
-        dates = [str(x) for x in range(20200304, 20200311)]
-
-        smoothed = pd.read_csv(
-            join("receiving",
-                f"{dates[-1]}_state_confirmed_7dav_cumulative_num.csv")
-        )
-
-        raw = pd.concat([
-            pd.read_csv(
-                join("receiving",
-                    f"{date}_state_confirmed_cumulative_num.csv")
-            ) for date in dates
-        ])
-
-        raw = raw.groupby('geo_id')['val'].mean()
-        df = pd.merge(smoothed, raw, on='geo_id', suffixes=('_smoothed', '_raw'))
-        
-        assert np.allclose(df['val_smoothed'].values, df['val_raw'].values)