diff --git a/cara/models.py b/cara/models.py
index a55cb9d3..2d41f33b 100644
--- a/cara/models.py
+++ b/cara/models.py
@@ -425,19 +425,11 @@ class Virus:
     #: Pre-populated examples of Viruses.
     types: typing.ClassVar[typing.Dict[str, "Virus"]]
 
-    #: Pre-defined examples of virus distributions.
-    distributions: typing.ClassVar[typing.Dict[str, typing.Callable[[int], "Virus"]]]
-
     @property
     def decay_constant(self) -> _VectorisedFloat:
         # Viral inactivation per hour (h^-1)
         return np.log(2) / self.halflife
 
-    @property
-    def coefficient_of_infectivity(self) -> _VectorisedFloat:
-        # Ratio between infectious aerosols and dose to cause infection.
-        return 1/self.qID
-
 
 Virus.types = {
     'SARS_CoV_2': Virus(
@@ -461,35 +453,6 @@ Virus.types = {
     ),
 }
 
-@cached
-def _generate_virus_distribution(params: typing.Tuple[int, float]) -> Virus:
-    samples , qID = params
-    log_symptomatic_vl_frequencies = ((2.46032, 2.67431, 2.85434, 3.06155, 3.25856, 3.47256, 3.66957, 3.85979, 4.09927, 4.27081,
-                                   4.47631, 4.66653, 4.87204, 5.10302, 5.27456, 5.46478, 5.6533, 5.88428, 6.07281, 6.30549,
-                                   6.48552, 6.64856, 6.85407, 7.10373, 7.30075, 7.47229, 7.66081, 7.85782, 8.05653, 8.27053,
-                                   8.48453, 8.65607, 8.90573, 9.06878, 9.27429, 9.473, 9.66152, 9.87552),
-                                  (0.001206885, 0.007851618, 0.008078144, 0.01502491, 0.013258014, 0.018528495, 0.020053765,
-                                   0.021896167, 0.022047184, 0.018604005, 0.01547796, 0.018075445, 0.021503523, 0.022349217,
-                                   0.025097721, 0.032875078, 0.030594727, 0.032573045, 0.034717482, 0.034792991,
-                                   0.033267721, 0.042887485, 0.036846816, 0.03876473, 0.045016819, 0.040063473, 0.04883754,
-                                   0.043944602, 0.048142864, 0.041588741, 0.048762031, 0.027921732, 0.033871788,
-                                   0.022122693, 0.016927718, 0.008833228, 0.00478598, 0.002807662))
-    kde_model = KernelDensity(kernel='gaussian', bandwidth=0.1)
-    kde_model.fit(np.asarray(log_symptomatic_vl_frequencies)[0, :][:, np.newaxis],
-                  sample_weight=np.asarray(log_symptomatic_vl_frequencies)[1, :])
-    viral_load_distribution = 10 ** kde_model.sample(n_samples=samples)[:, 0]
-    return Virus(
-        halflife=1.1,
-        viral_load_in_sputum=viral_load_distribution,
-        qID=qID,
-    )
-
-Virus.distributions = {
-    'SARS_CoV_2': lambda n: _generate_virus_distribution((n, 100)),
-    'SARS_CoV_2_B117': lambda n: _generate_virus_distribution((n, 60)),
-    'SARS_CoV_2_P1': lambda n: _generate_virus_distribution((n, 100/2.25)),
-}
-
 
 @dataclass(frozen=True)
 class Mask:
@@ -621,10 +584,10 @@ class InfectedPopulation(Population):
         aerosols = self.expiration.aerosols(self.mask)
 
         ER = (self.virus.viral_load_in_sputum *
-              self.virus.coefficient_of_infectivity *
               self.activity.exhalation_rate *
               10 ** 6 *
-              aerosols)
+              aerosols /
+              self.virus.qID)
 
         # For superspreading event, where ejection_factor is infinite we fix the ER
         # based on Miller et al. (2020).