Adding end-to-end integration tests of the full Monte-Carlo approach (comparison with CERN report cases and a few more)
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Nicolas Mounet
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cara/tests/test_monte_carlo_full_models.py
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cara/tests/test_monte_carlo_full_models.py
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import numpy as np
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import numpy.testing as npt
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import pytest
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import cara.monte_carlo as mc
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from cara import models,data
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from cara.monte_carlo.data import activity_distributions, virus_distributions
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# TODO: seed better the random number generators
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np.random.seed(2000)
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SAMPLE_SIZE = 20000
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TOLERANCE = 0.05
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# references values for infection_probability and expected new cases
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# in the following tests, were obtained from the feature/mc branch
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def test_shared_office():
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# corresponds to the 1st line of Table 5 in CERN-OPEN-2021-04, but
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# speaking 30% of the time (instead of 1/3)
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=50, humidity=0.3),
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ventilation=models.MultipleVentilation(
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(
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models.SlidingWindow(
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active=models.PeriodicInterval(period=120, duration=10),
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inside_temp=models.PiecewiseConstant((0, 24), (293,)),
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outside_temp=models.PiecewiseConstant((0, 24), (283,)),
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window_height=1.6, opening_length=0.6,
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),
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models.AirChange(
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active=models.SpecificInterval(((0,24),)),
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air_exch=0.25,
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),
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),
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),
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infected=mc.InfectedPopulation(
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number=1,
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virus=virus_distributions['SARS_CoV_2_B117'],
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presence=mc.SpecificInterval(((0, 2), (2.1, 4), (5, 7), (7.1, 9))),
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mask=models.Mask(η_inhale=0.3),
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activity=activity_distributions['Seated'],
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expiration=models.MultipleExpiration(
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expirations=(models.Expiration.types['Talking'],
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models.Expiration.types['Breathing']),
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weights=(0.3, 0.7)),
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),
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)
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exposure_mc = mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=3,
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presence=concentration_mc.infected.presence,
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activity=models.Activity.types['Seated'],
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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10.7, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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0.32, rtol=TOLERANCE)
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def test_classroom():
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# corresponds to the 2nd line of Table 5 in CERN-OPEN-2021-04
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=160, humidity=0.3),
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ventilation=models.MultipleVentilation(
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(
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models.SlidingWindow(
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active=models.PeriodicInterval(period=120, duration=10),
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inside_temp=models.PiecewiseConstant((0, 24), (293,)),
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outside_temp=models.PiecewiseConstant((0, 24), (283,)),
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window_height=1.6, opening_length=0.6,
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),
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models.AirChange(
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active=models.SpecificInterval(((0,24),)),
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air_exch=0.25,
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),
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),
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),
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infected=mc.InfectedPopulation(
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number=1,
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virus=virus_distributions['SARS_CoV_2_B117'],
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presence=mc.SpecificInterval(((0, 2), (2.5, 4), (5, 7), (7.5, 9))),
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mask=models.Mask.types['No mask'],
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activity=activity_distributions['Light activity'],
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expiration=models.Expiration.types['Talking'],
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),
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)
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exposure_mc = mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=19,
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presence=concentration_mc.infected.presence,
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activity=models.Activity.types['Seated'],
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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36.1, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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6.87, rtol=TOLERANCE)
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def test_skicabin():
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# corresponds to the 3rd line of Table 5 in CERN-OPEN-2021-04
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=10, humidity=0.5),
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ventilation=models.AirChange(
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active=models.SpecificInterval(((0,24),)),
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air_exch=0,
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),
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infected=mc.InfectedPopulation(
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number=1,
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virus=virus_distributions['SARS_CoV_2_B117'],
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presence=mc.SpecificInterval(((0, 1/3),)),
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mask=models.Mask(η_inhale=0.3),
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activity=activity_distributions['Moderate activity'],
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expiration=models.Expiration.types['Talking'],
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),
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)
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exposure_mc = mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=3,
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presence=concentration_mc.infected.presence,
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activity=models.Activity.types['Moderate activity'],
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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16.2, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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0.49, rtol=TOLERANCE)
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def test_gym():
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# corresponds to the 4th line of Table 5 in CERN-OPEN-2021-04,
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# but there the expected number of cases is wrongly reported as 0.56
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# while it should be 0.63.
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=300, humidity=0.5),
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ventilation=models.AirChange(
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active=models.SpecificInterval(((0,24),)),
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air_exch=6,
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),
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infected=mc.InfectedPopulation(
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number=2,
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virus=virus_distributions['SARS_CoV_2_B117'],
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presence=mc.SpecificInterval(((0, 1),)),
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mask=models.Mask.types["No mask"],
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activity=activity_distributions['Heavy exercise'],
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expiration=models.Expiration.types['Breathing'],
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),
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)
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exposure_mc = mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=28,
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presence=concentration_mc.infected.presence,
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activity=models.Activity.types['Heavy exercise'],
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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2.2, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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0.63, rtol=TOLERANCE)
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def test_waiting_room():
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# corresponds to the 5th line of Table 5 in CERN-OPEN-2021-04, but
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# speaking 30% of the time (instead of 20%)
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=100, humidity=0.5),
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ventilation=models.AirChange(
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active=models.SpecificInterval(((0,24),)),
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air_exch=0.25,
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),
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infected=mc.InfectedPopulation(
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number=1,
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virus=virus_distributions['SARS_CoV_2_B117'],
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presence=mc.SpecificInterval(((0, 2),)),
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mask=models.Mask.types["No mask"],
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activity=activity_distributions['Seated'],
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expiration=models.MultipleExpiration(
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expirations=(models.Expiration.types['Talking'],
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models.Expiration.types['Breathing']),
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weights=(0.3, 0.7)),
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),
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)
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exposure_mc = mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=14,
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presence=concentration_mc.infected.presence,
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activity=models.Activity.types['Seated'],
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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9.7, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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1.36, rtol=TOLERANCE)
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def test_skagit_chorale():
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# corresponds to the 6th line of Table 5 in CERN-OPEN-2021-04, but
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# infection probability should be 29.8% instead of 32%, and number of
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# new cases 17.9 instead of 21.
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=810, humidity=0.5),
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ventilation=models.AirChange(
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active=models.SpecificInterval(((0,24),)),
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air_exch=0.7,
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),
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infected=mc.InfectedPopulation(
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number=1,
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virus=virus_distributions['SARS_CoV_2'],
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presence=mc.SpecificInterval(((0, 2.5),)),
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mask=models.Mask.types["No mask"],
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activity=activity_distributions['Light activity'],
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expiration=models.Expiration((5., 5., 5.)),
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),
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)
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exposure_mc = mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=60,
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presence=concentration_mc.infected.presence,
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activity=models.Activity.types['Moderate activity'],
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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29.8, rtol=TOLERANCE)
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npt.assert_allclose(exposure_model.expected_new_cases().mean(),
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17.9, rtol=TOLERANCE)
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@pytest.mark.parametrize(
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"mask_type, month, expected_probability",
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[
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["No mask", "Jul", 30],
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["Type I", "Jul", 10.2],
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["FFP2", "Jul", 4],
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["Type I", "Feb", 4.3],
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],
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)
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def test_small_shared_office_Geneva(mask_type,month,expected_probability):
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concentration_mc = mc.ConcentrationModel(
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room=models.Room(volume=33, humidity=0.5),
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ventilation=models.MultipleVentilation(
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(
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models.SlidingWindow(
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active=models.SpecificInterval(((0,24),)),
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inside_temp=models.PiecewiseConstant((0, 24), (293,)),
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outside_temp=data.GenevaTemperatures[month],
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window_height=1.5, opening_length=0.2,
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),
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models.AirChange(
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active=models.SpecificInterval(((0,24),)),
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air_exch=0.25,
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),
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),
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),
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infected=mc.InfectedPopulation(
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number=1,
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virus=virus_distributions['SARS_CoV_2_B117'],
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presence=mc.SpecificInterval(((9, 10+2/3), (10+5/6, 12.5), (13.5, 15+2/3), (15+5/6, 18))),
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mask=models.Mask.types[mask_type],
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activity=activity_distributions['Seated'],
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expiration=models.MultipleExpiration(
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expirations=(models.Expiration.types['Talking'],
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models.Expiration.types['Breathing']),
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weights=(0.33, 0.67)),
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),
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)
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exposure_mc = mc.ExposureModel(
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concentration_model=concentration_mc,
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exposed=mc.Population(
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number=1,
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presence=concentration_mc.infected.presence,
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activity=activity_distributions['Seated'],
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mask=concentration_mc.infected.mask,
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),
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)
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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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npt.assert_allclose(exposure_model.infection_probability().mean(),
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expected_probability, rtol=TOLERANCE)
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