Revert "variables renaming (removal of quantum)"
This reverts commit ae4db5a4b0.
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ae4db5a4b0
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4 changed files with 26 additions and 25 deletions
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@ -420,8 +420,8 @@ class Virus:
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#: RNA copies / mL
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viral_load_in_sputum: _VectorisedFloat
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#: RNA-copies
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infectious_dose: _VectorisedFloat
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#: RNA-copies per quantum
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quantum_infectious_dose: _VectorisedFloat
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#: Pre-populated examples of Viruses.
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types: typing.ClassVar[typing.Dict[str, "Virus"]]
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@ -458,20 +458,20 @@ Virus.types = {
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# It is somewhere between 1000 or 10 SARS-CoV viruses,
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# as per https://www.dhs.gov/publication/st-master-question-list-covid-19
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# 50 comes from Buonanno et al.
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infectious_dose=50.,
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quantum_infectious_dose=50.,
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),
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'SARS_CoV_2_B117': SARSCoV2(
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# also called VOC-202012/01
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viral_load_in_sputum=1e9,
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infectious_dose=30.,
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quantum_infectious_dose=30.,
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),
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'SARS_CoV_2_P1': SARSCoV2(
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viral_load_in_sputum=1e9,
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infectious_dose=1/0.045,
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quantum_infectious_dose=1/0.045,
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),
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'SARS_CoV_2_B16172': SARSCoV2(
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viral_load_in_sputum=1e9,
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infectious_dose=30/1.6,
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quantum_infectious_dose=30/1.6,
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),
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}
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@ -677,7 +677,7 @@ class InfectedPopulation(Population):
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Note that the rate is not currently time-dependent.
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"""
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# Emission Rate (infectious dose / h)
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# Emission Rate (infectious quantum / h)
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# Note on units: exhalation rate is in m^3/h, aerosols in mL/cm^3
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# and viral load in virus/mL -> 1e6 conversion factor
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aerosols = self.expiration.aerosols(self.mask)
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@ -685,14 +685,15 @@ class InfectedPopulation(Population):
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ER = (self.virus.viral_load_in_sputum *
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self.activity.exhalation_rate *
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10 ** 6 *
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aerosols)
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aerosols /
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self.virus.quantum_infectious_dose)
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# For superspreading event, where ejection_factor is infinite we fix the ER
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# based on Miller et al. (2020).
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if isinstance(aerosols, np.ndarray):
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ER[np.isinf(aerosols)] = 970 * self.virus.infectious_dose
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ER[np.isinf(aerosols)] = 970
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elif np.isinf(aerosols):
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ER = 970 * self.virus.infectious_dose
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ER = 970
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return ER
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@ -803,7 +804,7 @@ class ConcentrationModel:
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def concentration(self, time: float) -> _VectorisedFloat:
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"""
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Virus concentration, as a function of time.
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Virus quanta concentration, as a function of time.
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The formulas used here assume that all parameters (ventilation,
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emission rate) are constant between two state changes - only
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the value of these parameters at the next state change, are used.
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@ -866,8 +867,8 @@ class ExposureModel:
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#: The fraction of viruses actually deposited in the respiratory tract
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fraction_deposited: _VectorisedFloat = 0.6
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def exposure(self) -> _VectorisedFloat:
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"""The number of virus per meter^3."""
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def quanta_exposure(self) -> _VectorisedFloat:
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"""The number of virus quanta per meter^3."""
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exposure = 0.0
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for start, stop in self.exposed.presence.boundaries():
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@ -876,7 +877,7 @@ class ExposureModel:
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return exposure * self.repeats
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def infection_probability(self) -> _VectorisedFloat:
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exposure = self.exposure()
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exposure = self.quanta_exposure()
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inf_aero = (
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self.exposed.activity.inhalation_rate *
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@ -885,7 +886,7 @@ class ExposureModel:
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)
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# Probability of infection.
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return (1 - np.exp(-inf_aero / self.concentration_model.virus.infectious_dose)) * 100
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return (1 - np.exp(-inf_aero)) * 100
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def expected_new_cases(self) -> _VectorisedFloat:
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prob = self.infection_probability()
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@ -43,18 +43,18 @@ symptomatic_vl_frequencies = LogCustomKernel(
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virus_distributions = {
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'SARS_CoV_2': mc.SARSCoV2(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=100,
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quantum_infectious_dose=100,
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),
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'SARS_CoV_2_B117': mc.SARSCoV2(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=60,
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quantum_infectious_dose=60,
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),
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'SARS_CoV_2_P1': mc.SARSCoV2(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=100/2.25,
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quantum_infectious_dose=100/2.25,
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),
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'SARS_CoV_2_B16172': mc.SARSCoV2(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=60/1.6,
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quantum_infectious_dose=60/1.6,
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),
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}
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@ -13,7 +13,7 @@ from cara import models
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{'humidity': np.array([0.5, 0.4])},
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{'air_change': np.array([100, 120])},
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{'viral_load_in_sputum': np.array([5e8, 1e9])},
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{'infectious_dose': np.array([50, 20])},
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{'quantum_infectious_dose': np.array([50, 20])},
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]
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)
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def test_concentration_model_vectorisation(override_params):
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@ -22,7 +22,7 @@ def test_concentration_model_vectorisation(override_params):
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'humidity': 0.5,
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'air_change': 100,
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'viral_load_in_sputum': 1e9,
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'infectious_dose': 50,
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'quantum_infectious_dose': 50,
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}
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defaults.update(override_params)
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@ -43,7 +43,7 @@ def test_concentration_model_vectorisation(override_params):
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),
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virus=models.SARSCoV2(
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viral_load_in_sputum=defaults['viral_load_in_sputum'],
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infectious_dose=defaults['infectious_dose'],
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quantum_infectious_dose=defaults['quantum_infectious_dose'],
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),
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expiration=models.Expiration((1., 0., 0.)),
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)
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@ -7,14 +7,14 @@ import cara.models
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@pytest.mark.parametrize(
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"override_params", [
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{'viral_load_in_sputum': np.array([5e8, 1e9])},
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{'infectious_dose': np.array([50, 20])},
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{'quantum_infectious_dose': np.array([50, 20])},
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{'exhalation_rate': np.array([0.75, 0.81])},
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]
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)
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def test_infected_population_vectorisation(override_params):
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defaults = {
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'viral_load_in_sputum': 1e9,
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'infectious_dose': 50,
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'quantum_infectious_dose': 50,
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'exhalation_rate': 0.75,
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}
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defaults.update(override_params)
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@ -33,7 +33,7 @@ def test_infected_population_vectorisation(override_params):
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),
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virus=cara.models.Virus(
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viral_load_in_sputum=defaults['viral_load_in_sputum'],
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infectious_dose=defaults['infectious_dose'],
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quantum_infectious_dose=defaults['quantum_infectious_dose'],
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),
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expiration=cara.models.Expiration((1., 0., 0.)),
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)
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