diff --git a/_delphi_utils_python/data_proc/geomap/geo_data_proc.py b/_delphi_utils_python/data_proc/geomap/geo_data_proc.py
index 893c5199d..d42de3261 100644
--- a/_delphi_utils_python/data_proc/geomap/geo_data_proc.py
+++ b/_delphi_utils_python/data_proc/geomap/geo_data_proc.py
@@ -217,6 +217,12 @@ def create_jhu_uid_fips_crosswalk():
             {"jhu_uid": "63072999", "fips": "72000", "weight": 1.0},
         ]
     )
+    cruise_ships = pd.DataFrame(
+        [
+            {"jhu_uid": "84088888", "fips": "88888", "weight": 1.0},
+            {"jhu_uid": "84099999", "fips": "99999", "weight": 1.0},
+        ]
+    )
 
     jhu_df = (
         pd.read_csv(JHU_FIPS_URL, dtype={"UID": str, "FIPS": str})
@@ -234,7 +240,7 @@ def create_jhu_uid_fips_crosswalk():
     # Drop the JHU UIDs that were hand-modified
     dup_ind = jhu_df["jhu_uid"].isin(
         pd.concat(
-            [hand_additions, unassigned_states, out_of_state, puerto_rico_unassigned]
+            [hand_additions, unassigned_states, out_of_state, puerto_rico_unassigned, cruise_ships]
         )["jhu_uid"].values
     )
     jhu_df.drop(jhu_df.index[dup_ind], inplace=True)
diff --git a/_delphi_utils_python/delphi_utils/data/jhu_uid_fips_table.csv b/_delphi_utils_python/delphi_utils/data/jhu_uid_fips_table.csv
index 427459b98..d69e014bc 100644
--- a/_delphi_utils_python/delphi_utils/data/jhu_uid_fips_table.csv
+++ b/_delphi_utils_python/delphi_utils/data/jhu_uid_fips_table.csv
@@ -82,8 +82,6 @@ jhu_uid,fips,weight
 63072149,72149,1.0
 63072151,72151,1.0
 63072153,72153,1.0
-84088888,88888,1.0
-84099999,99999,1.0
 84000001,01000,1.0
 84000002,02000,1.0
 84000004,04000,1.0
diff --git a/jhu/delphi_jhu/pull.py b/jhu/delphi_jhu/pull.py
index 18015a23c..3b5ddbe6f 100644
--- a/jhu/delphi_jhu/pull.py
+++ b/jhu/delphi_jhu/pull.py
@@ -14,7 +14,7 @@ def download_data(base_url: str, metric: str) -> pd.DataFrame:
     df = pd.read_csv(base_url.format(metric=metric))
     # Keep the UID and the time series columns only
     # The regex filters for columns with the date format MM-DD-YY or M-D-YY
-    df = df.filter(regex="\d{1,2}\/\d{1,2}\/\d{2}|UID").melt(
+    df = df.filter(regex=r"\d{1,2}\/\d{1,2}\/\d{2}|UID").melt(
         id_vars=["UID"], var_name="timestamp", value_name="cumulative_counts"
     )
     df["timestamp"] = pd.to_datetime(df["timestamp"])
diff --git a/jhu/delphi_jhu/run.py b/jhu/delphi_jhu/run.py
index c402a516f..ee9d1c85b 100644
--- a/jhu/delphi_jhu/run.py
+++ b/jhu/delphi_jhu/run.py
@@ -7,10 +7,8 @@
 from datetime import datetime
 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,