diff --git a/HowBadIsMyBatch.ipynb b/HowBadIsMyBatch.ipynb
index 1921ab0a25e..7a636cd246d 100644
--- a/HowBadIsMyBatch.ipynb
+++ b/HowBadIsMyBatch.ipynb
@@ -97,16 +97,37 @@
    "outputs": [],
    "source": [
     "def createBatchCodeTable(df : pd.DataFrame):\n",
-    "    def filter(df, col):\n",
+    "    def filterDataFrame(df, col):\n",
     "        return df[df[col] == 'Y'][['VAX_LOT']]\n",
     "\n",
     "    batchCodeTableDict = {\n",
     "        'ADRs': df[['VAX_LOT']].value_counts(),\n",
-    "        'DEATHS': filter(df, 'DIED').value_counts(),\n",
-    "        'DISABILITIES': filter(df, 'DISABLE').value_counts(),\n",
-    "        'LIFE THREATENING ILLNESSES': filter(df, 'L_THREAT').value_counts()\n",
+    "        'DEATHS': filterDataFrame(df, 'DIED').value_counts(),\n",
+    "        'DISABILITIES': filterDataFrame(df, 'DISABLE').value_counts(),\n",
+    "        'LIFE THREATENING ILLNESSES': filterDataFrame(df, 'L_THREAT').value_counts()\n",
     "    }\n",
-    "    return pd.concat(batchCodeTableDict, axis = 'columns').replace(to_replace = np.nan, value = 0)\n"
+    "    return pd.concat(batchCodeTableDict, axis = 'columns').replace(to_replace = np.nan, value = 0)\n",
+    "\n",
+    "def getManufacturerOfBatchCode(df, batchCode):\n",
+    "    return df[df['VAX_LOT'] == batchCode].iloc[0]['VAX_MANU']\n",
+    "\n",
+    "def createSevereEffectsBatchCodeTable(df):\n",
+    "    def filterDataFrame(df, col):\n",
+    "        return df[df[col] == 'Y'][['VAX_LOT']]\n",
+    "\n",
+    "    batchCodeTableDict = {\n",
+    "        'ADRs': df[['VAX_LOT']].value_counts(),\n",
+    "        'DEATHS': filterDataFrame(df, 'DIED').value_counts(),\n",
+    "        'DISABILITIES': filterDataFrame(df, 'DISABLE').value_counts(),\n",
+    "        'LIFE THREATENING ILLNESSES': filterDataFrame(df, 'L_THREAT').value_counts(),\n",
+    "        'HOSPITALISATIONS': filterDataFrame(df, 'HOSPITAL').value_counts(),\n",
+    "        'EMERGENCY ROOM OR DOCTOR VISITS': filterDataFrame(df, 'ER_VISIT').value_counts()\n",
+    "    }\n",
+    "    batchCodeTable = pd.concat(batchCodeTableDict, axis = 'columns')\n",
+    "    batchCodeTable['COMPANY'] = batchCodeTable.apply(\n",
+    "            lambda row: getManufacturerOfBatchCode(df, row.name[0]),\n",
+    "            axis = 'columns')\n",
+    "    return batchCodeTable.replace(to_replace = np.nan, value = 0)\n"
    ]
   },
   {
@@ -365,6 +386,61 @@
     "        return pd.DataFrame(index = index, columns = columns, data = data).astype(dtypes)\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ded70c87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas.testing import assert_frame_equal\n",
+    "\n",
+    "class SevereEffectsBatchCodeTableTest(unittest.TestCase):\n",
+    "\n",
+    "    def test_createSevereEffectsBatchCodeTable(self):\n",
+    "        dataFrame = createDataFrameSevereEffectsFromDescrs(\n",
+    "            [\n",
+    "               {\n",
+    "                   'VAERSDATA': self.createDataFrame(\n",
+    "                       columns = ['DIED', 'L_THREAT', 'DISABLE', 'HOSPITAL', 'ER_VISIT'],\n",
+    "                       data = [  ['Y',    'Y',        np.NaN,    'Y',        'Y'],\n",
+    "                                 [np.NaN, np.NaN,     'Y',       np.NaN,     'Y']],\n",
+    "                       index = [\n",
+    "                           \"0916600\",\n",
+    "                           \"0916601\"]),\n",
+    "                   'VAERSVAX': self.createDataFrame(\n",
+    "                       columns = ['VAX_TYPE', 'VAX_MANU',        'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
+    "                       data = [  ['COVID19',  'MODERNA',         '037K20A', '1'],\n",
+    "                                 ['COVID19',  'PFIZER\\BIONTECH', '025L20A', '1']],\n",
+    "                       index = [\n",
+    "                           \"0916600\",\n",
+    "                           \"0916601\"],\n",
+    "                       dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
+    "               }\n",
+    "            ],\n",
+    "            '1')\n",
+    "\n",
+    "        # When\n",
+    "        batchCodeTable = createSevereEffectsBatchCodeTable(dataFrame)\n",
+    "\n",
+    "        # Then\n",
+    "        batchCodeTableExpected = pd.DataFrame(\n",
+    "            data = {\n",
+    "                'ADRs': [1, 1],\n",
+    "                'DEATHS': [0, 1],\n",
+    "                'DISABILITIES': [1, 0],\n",
+    "                'LIFE THREATENING ILLNESSES': [0, 1],\n",
+    "                'HOSPITALISATIONS': [0, 1],\n",
+    "                'EMERGENCY ROOM OR DOCTOR VISITS': [1, 1],\n",
+    "                'COMPANY': ['PFIZER\\BIONTECH', 'MODERNA']\n",
+    "            },\n",
+    "            index = pd.MultiIndex.from_arrays([['025L20A', '037K20A']], names = ('VAX_LOT',)))\n",
+    "        assert_frame_equal(batchCodeTable, batchCodeTableExpected, check_dtype = False)\n",
+    "\n",
+    "    def createDataFrame(self, index, columns, data, dtypes = {}):\n",
+    "        return pd.DataFrame(index = index, columns = columns, data = data).astype(dtypes)\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,