diff --git a/src/HowBadIsMyBatch.ipynb b/src/HowBadIsMyBatch.ipynb
index 29e3fc3637f..dcd5bb9f928 100644
--- a/src/HowBadIsMyBatch.ipynb
+++ b/src/HowBadIsMyBatch.ipynb
@@ -665,6 +665,7 @@
    "outputs": [],
    "source": [
     "from SymptomsCausedByVaccines.MultiLineFitting.MultiLineFitter import MultiLineFitter\n",
+    "from SymptomsCausedByVaccines.MultiLineFitting.SymptomCombinationsProvider import SymptomCombinationsProvider\n",
     "import numpy as np\n",
     "from matplotlib import pyplot as plt\n",
     "from skspatial.objects import Line\n"
@@ -676,8 +677,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "symptomX = 'Immunosuppression' # HIV test' # 'Immunosuppression'\n",
-    "symptomY = 'Pneumonia' # 'Infection' # 'Immunoglobulin therapy'"
+    "symptomX = 'Abdominal abscess' # HIV test' # 'Immunosuppression'\n",
+    "symptomY = 'Abdominal discomfort' # 'Infection' # 'Immunoglobulin therapy'"
    ]
   },
   {
@@ -691,6 +692,27 @@
     "df"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "symptomCombinations = SymptomCombinationsProvider.generateSymptomCombinations(\n",
+    "    prrByLotAndSymptom[prrByLotAndSymptom.columns[:500]],\n",
+    "    dataFramePredicate = lambda df: 30 <= len(df) <= 35)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for symptomCombination in symptomCombinations:\n",
+    "    print(list(symptomCombination.columns))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -745,7 +767,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "draw(points, clustersAscending, linesAscending, symptomX, symptomY, minClusterSize = 3)"
+    "draw(points, clustersAscending, linesAscending, symptomX, symptomY, minClusterSize = 2)"
    ]
   },
   {
@@ -754,7 +776,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "clusters, lines = MultiLineFitter.fitPointsByLines(points, consensusThreshold = 0.001)"
+    "clusters, lines = MultiLineFitter.fitPointsByLines(points, consensusThreshold = 0.1)"
    ]
   },
   {
@@ -763,7 +785,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "draw(points, clusters, lines, symptomX, symptomY,  minClusterSize = 3)"
+    "draw(points, clusters, lines, symptomX, symptomY,  minClusterSize = 5)"
    ]
   },
   {
diff --git a/src/SymptomsCausedByVaccines/MultiLineFitting/SymptomCombinationsProvider.py b/src/SymptomsCausedByVaccines/MultiLineFitting/SymptomCombinationsProvider.py
new file mode 100644
index 00000000000..92c00b46b76
--- /dev/null
+++ b/src/SymptomsCausedByVaccines/MultiLineFitting/SymptomCombinationsProvider.py
@@ -0,0 +1,22 @@
+from SymptomsCausedByVaccines.MultiLineFitting.Utils import generatePairs
+
+class SymptomCombinationsProvider:
+
+    @staticmethod
+    def generateSymptomCombinations(prrByLotAndSymptom, dataFramePredicate):
+        symptomPairs = SymptomCombinationsProvider._generatePairs(prrByLotAndSymptom.columns)
+        symptomCombinations = (SymptomCombinationsProvider._generateSymptomCombination(prrByLotAndSymptom, symptomX, symptomY) for (symptomY, symptomX) in symptomPairs)
+        return SymptomCombinationsProvider._filter(symptomCombinations, dataFramePredicate)
+
+    @staticmethod
+    def _generatePairs(symptoms):
+        return ((symptoms[i], symptoms[j]) for (i, j) in generatePairs(len(symptoms)))
+
+    @staticmethod
+    def _generateSymptomCombination(prrByLotAndSymptom, symptomX, symptomY):
+        df = prrByLotAndSymptom[[symptomX, symptomY]]
+        return df[(df[symptomX] != 0) & (df[symptomY] != 0)]
+
+    @staticmethod
+    def _filter(dataFrames, dataFramePredicate):
+        return (dataFrame for dataFrame in dataFrames if dataFramePredicate(dataFrame))
diff --git a/src/SymptomsCausedByVaccines/MultiLineFitting/SymptomCombinationsProviderTest.py b/src/SymptomsCausedByVaccines/MultiLineFitting/SymptomCombinationsProviderTest.py
new file mode 100644
index 00000000000..fe291d0f40b
--- /dev/null
+++ b/src/SymptomsCausedByVaccines/MultiLineFitting/SymptomCombinationsProviderTest.py
@@ -0,0 +1,113 @@
+import unittest
+from pandas.testing import assert_frame_equal
+from TestHelper import TestHelper
+import pandas as pd
+from SymptomsCausedByVaccines.MultiLineFitting.SymptomCombinationsProvider import SymptomCombinationsProvider
+
+class SymptomCombinationsProviderTest(unittest.TestCase):
+
+    def test_generateSymptomCombinations(self):
+        # Given
+        prrByLotAndSymptom = TestHelper.createDataFrame(
+                columns = ['SymptomA', 'SymptomB', 'SymptomC', 'SymptomD'],
+                data = [  [0.6,        1.5,        1.2,        0.0]],
+                index = pd.Index(
+                    name = 'VAX_LOT',
+                    data = [
+                        'LOT-1'
+                        ]))
+        
+        # When
+        symptomCombinations = list(
+            SymptomCombinationsProvider.generateSymptomCombinations(
+                prrByLotAndSymptom,
+                dataFramePredicate = lambda df: len(df) >= 1))
+        
+        # Then
+        self.assertEqual(len(symptomCombinations), 3)
+        assert_frame_equal(
+            symptomCombinations[0],
+            TestHelper.createDataFrame(
+                columns = ['SymptomA', 'SymptomB'],
+                data = [  [0.6,        1.5]],
+                index = pd.Index(
+                    name = 'VAX_LOT',
+                    data = [
+                        'LOT-1'
+                        ])))
+        assert_frame_equal(
+            symptomCombinations[1],
+            TestHelper.createDataFrame(
+                columns = ['SymptomA', 'SymptomC'],
+                data = [  [0.6,        1.2]],
+                index = pd.Index(
+                    name = 'VAX_LOT',
+                    data = [
+                        'LOT-1'
+                        ])))
+        assert_frame_equal(
+            symptomCombinations[2],
+            TestHelper.createDataFrame(
+                columns = ['SymptomB', 'SymptomC'],
+                data = [  [1.5,        1.2]],
+                index = pd.Index(
+                    name = 'VAX_LOT',
+                    data = [
+                        'LOT-1'
+                        ])))
+
+    def test_generateSymptomCombinations_minSizeOfDataFrame_2(self):
+        # Given
+        prrByLotAndSymptom = TestHelper.createDataFrame(
+                columns = ['SymptomA', 'SymptomB'],
+                data = [  [0.6,        1.5],
+                          [1.6,        2.5]],
+                index = pd.Index(
+                    name = 'VAX_LOT',
+                    data = [
+                        'LOT-1',
+                        'LOT-2'
+                        ]))
+        
+        # When
+        symptomCombinations = list(
+            SymptomCombinationsProvider.generateSymptomCombinations(
+                prrByLotAndSymptom,
+                dataFramePredicate = lambda df: len(df) >= 2))
+        
+        # Then
+        self.assertEqual(len(symptomCombinations), 1)
+        assert_frame_equal(
+            symptomCombinations[0],
+            TestHelper.createDataFrame(
+                columns = ['SymptomA', 'SymptomB'],
+                data = [  [0.6,        1.5],
+                          [1.6,        2.5]],
+                index = pd.Index(
+                    name = 'VAX_LOT',
+                    data = [
+                        'LOT-1',
+                        'LOT-2'
+                        ])))
+
+    def test_generateSymptomCombinations_minSizeOfDataFrame_3(self):
+        # Given
+        prrByLotAndSymptom = TestHelper.createDataFrame(
+                columns = ['SymptomA', 'SymptomB'],
+                data = [  [0.6,        1.5],
+                          [1.6,        2.5]],
+                index = pd.Index(
+                    name = 'VAX_LOT',
+                    data = [
+                        'LOT-1',
+                        'LOT-2'
+                        ]))
+        
+        # When
+        symptomCombinations = list(
+            SymptomCombinationsProvider.generateSymptomCombinations(
+                prrByLotAndSymptom,
+                dataFramePredicate = lambda df: len(df) >= 3))
+        
+        # Then
+        self.assertEqual(len(symptomCombinations), 0)