diff --git a/cara/apps/calculator/model_generator.py b/cara/apps/calculator/model_generator.py
index ad65795e..68b060ab 100644
--- a/cara/apps/calculator/model_generator.py
+++ b/cara/apps/calculator/model_generator.py
@@ -8,7 +8,9 @@ import numpy as np
 
 from cara import models
 from cara import data
+import cara.monte_carlo as mc
 from .. import calculator
+from cara.monte_carlo.data import activity_distributions, virus_distributions
 
 
 LOG = logging.getLogger(__name__)
@@ -20,7 +22,7 @@ minutes_since_midnight = typing.NewType('minutes_since_midnight', int)
 # Used to declare when an attribute of a class must have a value provided, and
 # there should be no default value used.
 _NO_DEFAULT = object()
-
+_SAMPLE_SIZE = 50000
 
 @dataclass
 class FormData:
@@ -275,9 +277,9 @@ class FormData:
         mask = models.Mask.types[self.mask_type if self.mask_wearing_option == "mask_on" else 'No mask']
         return mask
 
-    def infected_population(self) -> models.InfectedPopulation:
+    def infected_population(self) -> mc.InfectedPopulation:
         # Initializes the virus
-        virus = models.Virus.types[self.virus_type]
+        virus = virus_distributions[self.virus_type]
 
         scenario_activity_and_expiration = {
             'office': (
@@ -315,12 +317,12 @@ class FormData:
         }
 
         [activity_defn, expiration_defn] = scenario_activity_and_expiration[self.activity_type]
-        activity = models.Activity.types[activity_defn]
+        activity = activity_distributions[activity_defn]
         expiration = build_expiration(expiration_defn)
 
         infected_occupants = self.infected_people
 
-        infected = models.InfectedPopulation(
+        infected = mc.InfectedPopulation(
             number=infected_occupants,
             virus=virus,
             presence=self.infected_present_interval(),
@@ -330,7 +332,7 @@ class FormData:
         )
         return infected
 
-    def exposed_population(self) -> models.Population:
+    def exposed_population(self) -> mc.Population:
         scenario_activity = {
             'office': 'Seated',
             'controlroom-day': 'Seated',
@@ -345,14 +347,14 @@ class FormData:
         }
 
         activity_defn = scenario_activity[self.activity_type]
-        activity = models.Activity.types[activity_defn]
+        activity = activity_distributions[activity_defn]
 
         infected_occupants = self.infected_people
         # The number of exposed occupants is the total number of occupants
         # minus the number of infected occupants.
         exposed_occupants = self.total_people - infected_occupants
 
-        exposed = models.Population(
+        exposed = mc.Population(
             number=exposed_occupants,
             presence=self.exposed_present_interval(),
             activity=activity,
@@ -560,14 +562,14 @@ def model_from_form(form: FormData) -> models.ExposureModel:
     room = models.Room(volume=volume, humidity=humidity)
 
     # Initializes and returns a model with the attributes defined above
-    return models.ExposureModel(
-        concentration_model=models.ConcentrationModel(
+    return mc.ExposureModel(
+        concentration_model=mc.ConcentrationModel(
             room=room,
             ventilation=form.ventilation(),
             infected=form.infected_population(),
         ),
         exposed=form.exposed_population()
-    )
+    ).build_model(size=_SAMPLE_SIZE)
 
 
 def baseline_raw_form_data():
diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py
index ea22cbb6..6cead28c 100644
--- a/cara/apps/calculator/report_generator.py
+++ b/cara/apps/calculator/report_generator.py
@@ -38,12 +38,13 @@ def calculate_report_data(model: models.ExposureModel):
 
     t_start, t_end = model_start_end(model)
     times = list(np.linspace(t_start, t_end, resolution))
-    concentrations = [model.concentration_model.concentration(time) for time in times]
+    concentrations = [np.mean(model.concentration_model.concentration(time))
+                      for time in times]
     highest_const = max(concentrations)
-    prob = model.infection_probability()
-    er = model.concentration_model.infected.emission_rate_when_present()
+    prob = np.mean(model.infection_probability())
+    er = np.mean(model.concentration_model.infected.emission_rate_when_present())
     exposed_occupants = model.exposed.number
-    expected_new_cases = model.expected_new_cases()
+    expected_new_cases = np.mean(model.expected_new_cases())
 
     repeated_events = []
     for n in [1, 2, 3, 4, 5]:
@@ -52,8 +53,8 @@ def calculate_report_data(model: models.ExposureModel):
         repeated_events.append(
             RepeatEvents(
                 repeats=n,
-                probability_of_infection=repeat_model.infection_probability(),
-                expected_new_cases=repeat_model.expected_new_cases(),
+                probability_of_infection=np.mean(repeat_model.infection_probability()),
+                expected_new_cases=np.mean(repeat_model.expected_new_cases()),
             )
         )
 
@@ -243,7 +244,8 @@ def comparison_plot(scenarios: typing.Dict[str, models.ExposureModel]):
             t_start, t_end = model_start_end(model)
             times = np.linspace(t_start, t_end, resolution)
         datetimes = [datetime(1970, 1, 1) + timedelta(hours=time) for time in times]
-        concentrations = [model.concentration_model.concentration(time) for time in times]
+        concentrations = [np.mean(model.concentration_model.concentration(time))
+                          for time in times]
 
         if name in dash_styled_scenarios:
             ax.plot(datetimes, concentrations, label=name, linestyle='--')
@@ -267,8 +269,8 @@ def comparison_report(scenarios: typing.Dict[str, models.ExposureModel]):
     statistics = {}
     for name, model in scenarios.items():
         statistics[name] = {
-            'probability_of_infection': model.infection_probability(),
-            'expected_new_cases': model.expected_new_cases(),
+            'probability_of_infection': np.mean(model.infection_probability()),
+            'expected_new_cases': np.mean(model.expected_new_cases()),
         }
     return {
         'plot': img2base64(_figure2bytes(comparison_plot(scenarios))),