diff --git a/HowBadIsMyBatch.ipynb b/HowBadIsMyBatch.ipynb
index a117fcd1477..d99a9d62502 100644
--- a/HowBadIsMyBatch.ipynb
+++ b/HowBadIsMyBatch.ipynb
@@ -1148,6 +1148,84 @@
     "        'Australia'\n",
     "    ])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba02139d",
+   "metadata": {},
+   "source": [
+    "### Batch Clusters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9649a32d",
+   "metadata": {},
+   "source": [
+    "#### Pfizer Batches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "020b0d90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://www.howbadismybatch.com/clusters.html\n",
+    "\n",
+    "import re\n",
+    "\n",
+    "dataFrame = DataFrameFilter().filterByCovid19(vaers)\n",
+    "dataFrame = DataFrameFilter().filterBy(dataFrame, manufacturer = \"PFIZER\\BIONTECH\")\n",
+    "batchCodeTable = BatchCodeTableFactory._createSummationTableByVAX_LOT(dataFrame)[['ADRs']].reset_index()\n",
+    "batchCodeTable['VAX_LOT_PREFIX'] = batchCodeTable['VAX_LOT'].str[:2]\n",
+    "batchCodeTable = batchCodeTable.sort_values(by = 'VAX_LOT_PREFIX', ascending = True)\n",
+    "\n",
+    "twoLetters = re.compile(r'^[a-zA-Z]{2}')\n",
+    "batchCodeTable = batchCodeTable[batchCodeTable['VAX_LOT_PREFIX'].apply(lambda vax_lot_prefix: bool(twoLetters.match(vax_lot_prefix)))]\n",
+    "batchCodeTable = batchCodeTable[batchCodeTable['VAX_LOT_PREFIX'].isin(['EN', 'EP', 'ER', 'EW', 'FA', 'FC', 'FD', 'FE', 'FH'])]\n",
+    "batchCodeTable = batchCodeTable[batchCodeTable['ADRs'] > 400]\n",
+    "batchCodeTable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02201726",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set(rc = {'figure.figsize': (11.7, 8.27)})\n",
+    "sns.set_theme()\n",
+    "chart = sns.stripplot(x = \"VAX_LOT_PREFIX\", y = \"ADRs\", data = batchCodeTable)\n",
+    "# _ = chart.set_xticklabels(chart.get_xticklabels(), rotation = 90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6000b48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.pointplot(x = \"VAX_LOT_PREFIX\", y = \"ADRs\", data = batchCodeTable, estimator = np.mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf53c8c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.set_theme(style = \"ticks\", palette = \"pastel\")\n",
+    "\n",
+    "sns.boxplot(x = \"VAX_LOT_PREFIX\", y = \"ADRs\", data = batchCodeTable)"
+   ]
   }
  ],
  "metadata": {
diff --git a/help.txt b/help.txt
index 95b610d5197..60ae04122a3 100644
--- a/help.txt
+++ b/help.txt
@@ -4,7 +4,6 @@ get VAERS data:
 - download data (e.g. 2022VAERSData.zip) from https://vaers.hhs.gov/data/datasets.html and save and unzip in VAERS folder
 
 FK-TODO:
-- https://www.howbadismybatch.com/international.html nachprogrammieren
 - https://www.howbadismybatch.com/geography.html nachprogrammieren
 - handle VAX_DOSE_SERIES = 'UNK' or 'N/A' like '1'?
 - Format des jeweiligen Herstellers berücksichtigen und "verschmutzte" Einträge säubern, denn sie stellen alle dieselbe Charge dar: