Adding full algo tests with distributions, in particular on the concentration/dose/probability of infection
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Nicolas Mounet
parent
ef84f28ab6
commit
b11dd90e92
1 changed files with 197 additions and 50 deletions
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@ -244,7 +244,7 @@ class SimpleShortRangeModel:
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return dilution
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@method_cache
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# @method_cache
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def jet_concentration(self,conc_model: SimpleConcentrationModel) -> _VectorisedFloat:
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"""
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virion concentration at the origin of the jet (close to
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@ -267,13 +267,13 @@ class SimpleShortRangeModel:
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def concentration(self, conc_model: SimpleConcentrationModel, time: float) -> _VectorisedFloat:
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"""
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compute the short-range part of the concentration, and add it
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to the background concentration
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to the long-range concentration
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"""
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if self.interaction_interval.triggered(time):
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background_concentration = conc_model.concentration(time)
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lr_concentration = conc_model.concentration(time)
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S = self.dilution_factor()
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return (self.jet_concentration(conc_model)
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- background_concentration) / S
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- lr_concentration) / S
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else:
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return 0.
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@ -352,8 +352,17 @@ class SimpleExposureModel(SimpleConcentrationModel):
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epsabs=0.,limit=500)[0]
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* self.viral_load * self.breathing_rate)
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def total_concentration(self, t: float):
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"""
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total concentration at time t
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"""
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res = self.concentration(t)
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for sr_mod in self.sr_models:
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res += sr_mod.concentration(self,t)
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return res
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@method_cache
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def integrated_background_concentration(self,t1: float,t2: float,
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def integrated_longrange_concentration(self,t1: float,t2: float,
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evaporation: float) -> _VectorisedFloat:
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"""
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background (long-range) concentration integrated from t1 to t2
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@ -417,7 +426,7 @@ class SimpleExposureModel(SimpleConcentrationModel):
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epsabs=0.,limit=500)[0]
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* self.viral_load * 1e-6 * (t2-t1) )
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result += sr_model.breathing_rate * (
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res-self.integrated_background_concentration(t1,t2,evaporation)
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res-self.integrated_longrange_concentration(t1,t2,evaporation)
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)/sr_model.dilution_factor()
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return result
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@ -429,7 +438,7 @@ class SimpleExposureModel(SimpleConcentrationModel):
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"""
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result = 0.
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for t1,t2 in self.infected_presence.boundaries():
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result += (self.integrated_background_concentration(t1,t2,self.evaporation)
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result += (self.integrated_longrange_concentration(t1,t2,self.evaporation)
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* self.breathing_rate)
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result += self.integrated_shortrange_concentration()
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@ -468,6 +477,25 @@ def c_model() -> mc.ConcentrationModel:
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).build_model(SAMPLE_SIZE)
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@pytest.fixture
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def c_model_distr() -> mc.ConcentrationModel:
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return mc.ConcentrationModel(
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room=models.Room(volume=50, humidity=0.3),
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ventilation=models.AirChange(active=models.PeriodicInterval(
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period=120, duration=120), air_exch=1.),
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infected=mc.InfectedPopulation(
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number=1,
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presence=presence,
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virus=virus_distributions['SARS_CoV_2_DELTA'],
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mask=models.Mask.types['No mask'],
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activity=activity_distributions['Seated'],
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expiration=expiration_distributions['Breathing'],
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host_immunity=0.,
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),
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evaporation_factor=0.3,
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).build_model(SAMPLE_SIZE)
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@pytest.fixture
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def sr_models() -> typing.Tuple[mc.ShortRangeModel, ...]:
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return (
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@ -516,10 +544,107 @@ def simple_sr_models() -> typing.Tuple[SimpleShortRangeModel, ...]:
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)
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@pytest.fixture
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def expo_sr_model(c_model,sr_models) -> mc.ExposureModel:
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return mc.ExposureModel(
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concentration_model=c_model,
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short_range=sr_models,
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exposed=mc.Population(
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number=1,
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presence=presence,
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mask=models.Mask.types['No mask'],
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activity=models.Activity.types['Seated'],
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host_immunity=0.,
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),
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).build_model(SAMPLE_SIZE)
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@pytest.fixture
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def simple_expo_sr_model(simple_sr_models) -> SimpleExposureModel:
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return SimpleExposureModel(
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infected_presence = presence,
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viral_load = models.Virus.types['SARS_CoV_2_DELTA'].viral_load_in_sputum,
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breathing_rate = models.Activity.types['Seated'].exhalation_rate,
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room_volume = 50.,
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lambda_ventilation= 1.,
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BLO_factors = expiration_BLO_factors['Breathing'],
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finf = models.Virus.types['SARS_CoV_2_DELTA'].viable_to_RNA_ratio,
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HI = 0.,
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ID50 = models.Virus.types['SARS_CoV_2_DELTA'].infectious_dose,
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transmissibility = models.Virus.types['SARS_CoV_2_DELTA'].transmissibility_factor,
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sr_models = simple_sr_models,
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)
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@pytest.fixture
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def expo_sr_model_distr(c_model_distr) -> mc.ExposureModel:
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return mc.ExposureModel(
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concentration_model=c_model_distr,
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short_range=(
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mc.ShortRangeModel(
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expiration = short_range_expiration_distributions['Breathing'],
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activity = activity_distributions['Seated'],
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presence = interaction_intervals[0],
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distance = short_range_distances,
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).build_model(SAMPLE_SIZE),
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mc.ShortRangeModel(
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expiration = short_range_expiration_distributions['Speaking'],
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activity = activity_distributions['Seated'],
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presence = interaction_intervals[1],
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distance = short_range_distances,
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).build_model(SAMPLE_SIZE),
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),
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exposed=mc.Population(
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number=1,
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presence=presence,
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mask=models.Mask.types['No mask'],
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activity=models.Activity.types['Seated'],
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host_immunity=0.,
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),
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).build_model(SAMPLE_SIZE)
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@pytest.fixture
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def simple_expo_sr_model_distr(c_model_distr) -> SimpleExposureModel:
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return SimpleExposureModel(
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infected_presence = presence,
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viral_load = virus_distributions['SARS_CoV_2_DELTA'
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].build_model(SAMPLE_SIZE).viral_load_in_sputum,
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breathing_rate = activity_distributions['Seated'].build_model(
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SAMPLE_SIZE).exhalation_rate,
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room_volume = 50.,
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lambda_ventilation= 1.,
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BLO_factors = expiration_BLO_factors['Breathing'],
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finf = virus_distributions['SARS_CoV_2_DELTA'
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].build_model(SAMPLE_SIZE).viable_to_RNA_ratio,
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HI = 0.,
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ID50 = virus_distributions['SARS_CoV_2_DELTA'
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].build_model(SAMPLE_SIZE).infectious_dose,
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transmissibility = virus_distributions['SARS_CoV_2_DELTA'
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].transmissibility_factor,
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sr_models = (
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SimpleShortRangeModel(
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interaction_interval = interaction_intervals[0],
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distance = short_range_distances.generate_samples(SAMPLE_SIZE),
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breathing_rate = activity_distributions['Seated'].build_model(
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SAMPLE_SIZE).exhalation_rate,
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BLO_factors = expiration_BLO_factors['Breathing'],
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),
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SimpleShortRangeModel(
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interaction_interval = interaction_intervals[1],
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distance = short_range_distances.generate_samples(SAMPLE_SIZE),
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breathing_rate = activity_distributions['Seated'].build_model(
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SAMPLE_SIZE).exhalation_rate,
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BLO_factors = expiration_BLO_factors['Speaking'],
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)
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),
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)
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@pytest.mark.parametrize(
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"time", np.linspace(8.5,17.5,12),
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)
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def test_background_concentration(time,c_model,simple_c_model):
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def test_longrange_concentration(time,c_model,simple_c_model):
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npt.assert_allclose(
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c_model.concentration(time).mean(),
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simple_c_model.concentration(time), rtol=TOLERANCE
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@ -542,7 +667,7 @@ def test_shortrange_concentration(time,c_model,simple_c_model,
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)
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def test_background_exposure(c_model):
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def test_longrange_exposure(c_model):
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simple_expo_model = SimpleExposureModel(
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infected_presence = presence,
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viral_load = models.Virus.types['SARS_CoV_2_DELTA'].viral_load_in_sputum,
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@ -577,7 +702,27 @@ def test_background_exposure(c_model):
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)
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def test_background_exposure_with_distributions():
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@pytest.mark.parametrize(
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"time", [11., 12.5, 17.]
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)
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def test_longrange_concentration_with_distributions(c_model_distr,time):
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simple_expo_model = SimpleConcentrationModel(
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infected_presence = presence,
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viral_load = virus_distributions['SARS_CoV_2_DELTA'
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].build_model(SAMPLE_SIZE).viral_load_in_sputum,
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breathing_rate = activity_distributions['Seated'].build_model(
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SAMPLE_SIZE).exhalation_rate,
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room_volume = 50.,
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lambda_ventilation= 1.,
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BLO_factors = expiration_BLO_factors['Breathing'],
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)
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npt.assert_allclose(
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c_model_distr.concentration(time).mean(),
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simple_expo_model.concentration(time).mean(), rtol=TOLERANCE
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)
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def test_longrange_exposure_with_distributions(c_model_distr):
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simple_expo_model = SimpleExposureModel(
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infected_presence = presence,
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viral_load = virus_distributions['SARS_CoV_2_DELTA'
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@ -597,21 +742,7 @@ def test_background_exposure_with_distributions():
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sr_models = (),
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)
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expo_model = mc.ExposureModel(
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concentration_model=mc.ConcentrationModel(
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room=models.Room(volume=50, humidity=0.3),
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ventilation=models.AirChange(active=models.PeriodicInterval(
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period=120, duration=120), air_exch=1.),
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infected=mc.InfectedPopulation(
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number=1,
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presence=presence,
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virus=virus_distributions['SARS_CoV_2_DELTA'],
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mask=models.Mask.types['No mask'],
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activity=activity_distributions['Seated'],
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expiration=expiration_distributions['Breathing'],
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host_immunity=0.,
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),
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evaporation_factor=0.3,
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),
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concentration_model=c_model_distr,
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short_range=(),
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exposed=mc.Population(
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number=1,
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@ -631,31 +762,21 @@ def test_background_exposure_with_distributions():
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)
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def test_exposure_with_shortrange(c_model,sr_models,simple_sr_models):
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simple_expo_sr_model = SimpleExposureModel(
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infected_presence = presence,
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viral_load = models.Virus.types['SARS_CoV_2_DELTA'].viral_load_in_sputum,
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breathing_rate = models.Activity.types['Seated'].exhalation_rate,
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room_volume = 50.,
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lambda_ventilation= 1.,
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BLO_factors = expiration_BLO_factors['Breathing'],
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finf = models.Virus.types['SARS_CoV_2_DELTA'].viable_to_RNA_ratio,
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HI = 0.,
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ID50 = models.Virus.types['SARS_CoV_2_DELTA'].infectious_dose,
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transmissibility = models.Virus.types['SARS_CoV_2_DELTA'].transmissibility_factor,
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sr_models = simple_sr_models,
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)
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expo_sr_model = mc.ExposureModel(
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concentration_model=c_model,
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short_range=sr_models,
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exposed=mc.Population(
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number=1,
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presence=presence,
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mask=models.Mask.types['No mask'],
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activity=models.Activity.types['Seated'],
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host_immunity=0.,
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),
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).build_model(SAMPLE_SIZE)
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# tests on the concentration with short-range should be skipped until
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# one finds a way to avoid the large variability of the concentration
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# with short-range 'Speaking' or 'Shouting' interactions
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@pytest.mark.skip
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@pytest.mark.parametrize(
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"time", [10.75, 14.75, 16.]
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)
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def test_concentration_with_shortrange(expo_sr_model,simple_expo_sr_model,time):
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npt.assert_allclose(
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expo_sr_model.concentration(time).mean(),
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simple_expo_sr_model.total_concentration(time).mean(), rtol=TOLERANCE
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)
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def test_exposure_with_shortrange(expo_sr_model,simple_expo_sr_model):
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npt.assert_allclose(
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expo_sr_model.deposited_exposure().mean(),
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simple_expo_sr_model.dose().mean(), rtol=TOLERANCE
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@ -665,3 +786,29 @@ def test_exposure_with_shortrange(c_model,sr_models,simple_sr_models):
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simple_expo_sr_model.probability_infection().mean(), rtol=TOLERANCE
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)
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@pytest.mark.skip
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@pytest.mark.parametrize(
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"time", [10.75, 14.75, 16.]
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)
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def test_concentration_with_shortrange_and_distributions(
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expo_sr_model_distr,simple_expo_sr_model_distr,time):
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npt.assert_allclose(
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expo_sr_model_distr.concentration(time).mean(),
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simple_expo_sr_model_distr.total_concentration(time).mean(),
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rtol=TOLERANCE
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)
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def test_exposure_with_shortrange_and_distributions(expo_sr_model_distr,
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simple_expo_sr_model_distr):
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npt.assert_allclose(
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expo_sr_model_distr.deposited_exposure().mean(),
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simple_expo_sr_model_distr.dose().mean(), rtol=0.05
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)
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npt.assert_allclose(
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expo_sr_model_distr.infection_probability().mean(),
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simple_expo_sr_model_distr.probability_infection().mean(),
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rtol=0.03
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)
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