diff --git a/cara/tests/test_predefined_distributions.py b/cara/tests/test_predefined_distributions.py
new file mode 100644
index 00000000..1e066432
--- /dev/null
+++ b/cara/tests/test_predefined_distributions.py
@@ -0,0 +1,47 @@
+import numpy as np
+import numpy.testing as npt
+import pytest
+
+from cara.monte_carlo.data import activity_distributions, virus_distributions
+
+# TODO: seed better the random number generators
+np.random.seed(0)
+
+
+# mean & std deviations from CERN-OPEN-2021-04 (Table 4)
+# NOTE: a mistake was corrected for the std deviation of the
+# "Moderate exercise" case (0.37 in the report, but should be 0.34)
+@pytest.mark.parametrize(
+    "distribution, mean, std",[
+        ['Seated', 0.51, 0.053],
+
+        ['Standing', 0.57, 0.053],
+
+        ['Light activity', 1.24, 0.12],
+
+        ['Moderate activity', 1.77, 0.34],
+
+        ['Heavy exercise', 3.28, 0.72,],
+    ]
+)
+def test_activity_distributions(distribution, mean, std):
+    activity = activity_distributions[distribution].build_model(size=1000000)
+    npt.assert_allclose(activity.inhalation_rate.mean(), mean, atol=0.01)
+    npt.assert_allclose(activity.inhalation_rate.std(), std, atol=0.01)
+
+
+# mean & std deviations from CERN-OPEN-2021-04 (Table 4) - with a refined
+# precision on the values
+@pytest.mark.parametrize(
+    "distribution, mean, std",[
+        ['SARS_CoV_2', 6.59, 1.74],
+
+        ['SARS_CoV_2_B117', 6.59, 1.74],
+
+        ['SARS_CoV_2_P1', 6.59, 1.74],
+    ]
+)
+def test_viral_load_logdistribution(distribution, mean, std):
+    virus = virus_distributions[distribution].build_model(size=1000000)
+    npt.assert_allclose(np.log10(virus.viral_load_in_sputum).mean(), mean, atol=0.01)
+    npt.assert_allclose(np.log10(virus.viral_load_in_sputum).std(), std, atol=0.01)
diff --git a/cara/tests/test_sampleable_distribution.py b/cara/tests/test_sampleable_distribution.py
index e3801c7e..585b4dde 100644
--- a/cara/tests/test_sampleable_distribution.py
+++ b/cara/tests/test_sampleable_distribution.py
@@ -3,9 +3,11 @@ import numpy.testing as npt
 import pytest
 
 from cara.monte_carlo import sampleable
+
 # TODO: seed better the random number generators
 np.random.seed(2000)
 
+
 @pytest.mark.parametrize(
     "mean, std",[
         [1., 0.5],