moved default infectiousness_days value to SARSCoV2 class and added another test scenario

caimira/apps/calculator/report_generator.py

@@ -278,7 +278,7 @@ def scenario_statistics(mc_model: mc.ExposureModel, sample_times: typing.List[fl
     model = mc_model.build_model(size=_DEFAULT_MC_SAMPLE_SIZE)
     if (compute_prob_exposure):
         # It means we have data to calculate the total_probability_rule
-        prob_probabilistic_exposure = np.array(model.total_probability_rule()).mean()
+        prob_probabilistic_exposure = model.total_probability_rule()
     else:
         prob_probabilistic_exposure = 0.
 

caimira/models.py

@@ -440,7 +440,7 @@ class Virus:
     types: typing.ClassVar[typing.Dict[str, "Virus"]]
 
     #: Number of days the infector is contagious
-    infectiousness_days: int = 14
+    infectiousness_days: int
 
     def halflife(self, humidity: _VectorisedFloat, inside_temp: _VectorisedFloat) -> _VectorisedFloat:
         # Biological decay (inactivation of the virus in air) - virus 
@@ -454,7 +454,9 @@ class Virus:
 
 @dataclass(frozen=True)
 class SARSCoV2(Virus):
-
+    #: Number of days the infector is contagious
+    infectiousness_days: int = 14
+    
     def halflife(self, humidity: _VectorisedFloat, inside_temp: _VectorisedFloat) -> _VectorisedFloat:
         """
         Half-life changes with humidity level. Here is implemented a simple

caimira/tests/models/test_exposure_model.py

@@ -275,7 +275,9 @@ def test_prob_meet_infected_person(pop, cases, AB, exposed, infected, prob_meet_
 @pytest.mark.parametrize(
     "exposed_population, cm, pop, cases, AB, probabilistic_exposure_probability",[
         [10, known_concentrations(lambda t: 36.),
-        100000, 68, 5, 41.51920685],
+        100000, 68, 5, 41.50971131],
+        [10, known_concentrations(lambda t: 0.2),
+        100000, 68, 5, 2.185785075],
         [20, known_concentrations(lambda t: 72.),
         100000, 68, 5, 64.09068488],
         [30, known_concentrations(lambda t: 1.2),
@@ -289,7 +291,6 @@ def test_probabilistic_exposure_probability(exposed_population, cm,
         models.Activity.types['Standing'], host_immunity=0.,)
     model = ExposureModel(cm, (), population, models.Cases(geographic_population=pop,
         geographic_cases=cases, ascertainment_bias=AB),)
-
     np.testing.assert_allclose(
         model.total_probability_rule(), probabilistic_exposure_probability, rtol=0.05
     )

caimira/tests/test_infected_population.py

@@ -29,7 +29,7 @@ def test_infected_population_vectorisation(override_params):
                 0.51,
                 defaults['exhalation_rate'],
             ),
-            virus=caimira.models.Virus(
+            virus=caimira.models.SARSCoV2(
                 viral_load_in_sputum=defaults['viral_load_in_sputum'],
                 infectious_dose=50.,
                 viable_to_RNA_ratio = 0.5,