Removed script files
This commit is contained in:
Luis Aleixo
parent
cbae4a01a3
commit
25fe14c1aa
4 changed files with 0 additions and 1617 deletions
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@ -1,687 +0,0 @@
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from cara import models
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from cara.monte_carlo.data import activity_distributions, symptomatic_vl_frequencies, infectious_virus_distribution, infectious_dose_distribution
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import cara.monte_carlo as mc
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import numpy as np
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######### Scatter points (data taken: copies per hour) #########
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############# Coleman #############
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############# Coleman - Breathing #############
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coleman_etal_vl_breathing = [np.log10(821065925.4), np.log10(1382131207), np.log10(81801735.96), np.log10(
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487760677.4), np.log10(2326593535), np.log10(1488879159), np.log10(884480386.5)]
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coleman_etal_er_breathing = [127, 455.2, 281.8, 884.2, 448.4, 1100.6, 621]
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############# Coleman - Talking #############
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coleman_etal_vl_talking = [np.log10(70492378.55), np.log10(7565486.029), np.log10(7101877592), np.log10(1382131207),
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np.log10(821065925.4), np.log10(1382131207), np.log10(
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81801735.96), np.log10(487760677.4),
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np.log10(2326593535), np.log10(1488879159), np.log10(884480386.5)]
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coleman_etal_er_talking = [1668, 938, 319.6, 3632.8, 1243.6,
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17344, 2932, 5426, 5493.2, 1911.6, 9714.8]
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############# Milton et al #############
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milton_vl = [np.log10(8.30E+04), np.log10(4.20E+05), np.log10(1.80E+06)]
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milton_er = [22, 220, 1120]
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############# Milton et al #############
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yann_vl = [np.log10(7.86E+07), np.log10(2.23E+09), np.log10(1.51E+10)]
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yann_er = [8396.78166, 45324.55964, 400054.0827]
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######### Standard exposure models ###########
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######### Breathing model ###########
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def breathing_exposure(activity: str, mask: str):
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution
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),
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presence=mc.SpecificInterval(((0, 2),)),
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mask=models.Mask.types[mask],
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activity=activity_distributions[activity],
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expiration=models.Expiration.types['Breathing'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types[activity],
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mask=models.Mask.types[mask],
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),
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)
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return exposure_mc
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######### Speaking model ###########
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def speaking_exposure(activity: str, mask: str):
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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presence=mc.SpecificInterval(((0, 2),)),
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mask=models.Mask.types[mask],
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activity=activity_distributions[activity],
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expiration=models.Expiration.types['Talking'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types[activity],
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mask=models.Mask.types[mask],
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),
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)
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return exposure_mc
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######### Shouting model ###########
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def shouting_exposure(activity: str, mask: str):
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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presence=mc.SpecificInterval(((0, 2),)),
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mask=models.Mask.types[mask],
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activity=activity_distributions[activity],
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expiration=models.Expiration.types['Shouting'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types[activity],
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mask=models.Mask.types[mask],
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),
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)
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return exposure_mc
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######### Breathing model for specific viral load ###########
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def breathing_exposure_vl(vl):
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exposure_mc = mc.ExposureModel(
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concentration_model=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=models.Virus(
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viral_load_in_sputum=10**vl,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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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.Expiration.types['Breathing'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Seated'],
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mask=models.Mask.types["No mask"],
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),
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)
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return exposure_mc
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######### Talking model for specific viral load ###########
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def talking_exposure_vl(vl):
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exposure_mc = mc.ExposureModel(
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concentration_model=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=models.Virus(
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viral_load_in_sputum=10**vl,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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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.Expiration.types['Talking'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Seated'],
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mask=models.Mask.types["No mask"],
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),
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)
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return exposure_mc
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######### Shouting model for specific viral load ###########
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def shouting_exposure_vl(vl):
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=10**vl,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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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['Light activity'],
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expiration=models.Expiration.types['Shouting'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Light activity'],
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mask=models.Mask.types["No mask"],
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),
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)
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return exposure_mc
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######### Used for CDF Models ###########
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######### Breathing Models #########
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def breathing_seated_exposure():
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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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.Expiration.types['Breathing'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Seated'],
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mask=models.Mask.types["No mask"],
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),
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)
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return exposure_mc
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def breathing_light_activity_exposure():
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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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['Light activity'],
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expiration=models.Expiration.types['Breathing'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Light activity'],
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mask=models.Mask.types["No mask"],
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),
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)
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return exposure_mc
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def breathing_heavy_exercise_exposure():
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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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['Heavy exercise'],
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expiration=models.Expiration.types['Breathing'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Heavy exercise'],
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mask=models.Mask.types["No mask"],
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),
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)
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return exposure_mc
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######### Speaking Models #########
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def speaking_seated_exposure():
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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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.Expiration.types['Talking'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Seated'],
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mask=models.Mask.types["No mask"],
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),
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)
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return exposure_mc
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def speaking_light_activity_exposure():
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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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['Light activity'],
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expiration=models.Expiration.types['Talking'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Light activity'],
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mask=models.Mask.types["No mask"],
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),
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)
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return exposure_mc
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def speaking_heavy_exercise_exposure():
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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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['Heavy exercise'],
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expiration=models.Expiration.types['Talking'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Heavy exercise'],
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mask=models.Mask.types["No mask"],
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),
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)
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return exposure_mc
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######### Shouting Models #########
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def shouting_seated_exposure():
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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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.Expiration.types['Shouting'],
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),
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),
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exposed=mc.Population(
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number=14,
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Seated'],
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mask=models.Mask.types["No mask"],
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),
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)
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return exposure_mc
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def shouting_light_activity_exposure():
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exposure_mc = mc.ExposureModel(
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concentration_model=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=mc.Virus(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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infectious_dose=infectious_dose_distribution,
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viable_to_RNA=infectious_virus_distribution,
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),
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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['Light activity'],
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expiration=models.Expiration.types['Shouting'],
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),
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),
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exposed=mc.Population(
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number=14,
|
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presence=mc.SpecificInterval(((0, 2),)),
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activity=models.Activity.types['Light activity'],
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mask=models.Mask.types["No mask"],
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),
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)
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||||
return exposure_mc
|
||||
|
||||
def shouting_heavy_exercise_exposure():
|
||||
exposure_mc = mc.ExposureModel(
|
||||
concentration_model=mc.ConcentrationModel(
|
||||
room=models.Room(volume=100, humidity=0.5),
|
||||
ventilation=models.AirChange(
|
||||
active=models.SpecificInterval(((0, 24),)),
|
||||
air_exch=0.25,
|
||||
),
|
||||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
virus=mc.Virus(
|
||||
viral_load_in_sputum=symptomatic_vl_frequencies,
|
||||
infectious_dose=infectious_dose_distribution,
|
||||
viable_to_RNA=infectious_virus_distribution,
|
||||
),
|
||||
presence=mc.SpecificInterval(((0, 2),)),
|
||||
mask=models.Mask.types["No mask"],
|
||||
activity=activity_distributions['Heavy exercise'],
|
||||
expiration=models.Expiration.types['Shouting'],
|
||||
),
|
||||
),
|
||||
exposed=mc.Population(
|
||||
number=14,
|
||||
presence=mc.SpecificInterval(((0, 2),)),
|
||||
activity=models.Activity.types['Heavy exercise'],
|
||||
mask=models.Mask.types["No mask"],
|
||||
),
|
||||
)
|
||||
return exposure_mc
|
||||
|
||||
########## Concentration curves ###########
|
||||
def office_model_no_mask_windows_closed():
|
||||
office_model_no_vent = mc.ExposureModel(
|
||||
concentration_model=mc.ConcentrationModel(
|
||||
room=models.Room(volume=160, humidity=0.3),
|
||||
ventilation=models.MultipleVentilation(
|
||||
(models.AirChange(active=models.PeriodicInterval(period=120, duration=120), air_exch=0.0),
|
||||
models.AirChange(active=models.PeriodicInterval(period=120, duration=120), air_exch=0.25))),
|
||||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
presence=models.SpecificInterval(present_times = ((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
|
||||
virus=mc.SARSCoV2(
|
||||
viral_load_in_sputum=symptomatic_vl_frequencies,
|
||||
infectious_dose=infectious_dose_distribution,
|
||||
viable_to_RNA=infectious_virus_distribution,
|
||||
),
|
||||
mask=models.Mask.types["No mask"],
|
||||
activity=activity_distributions['Seated'],
|
||||
expiration=models.MultipleExpiration(
|
||||
expirations = (models.Expiration.types['Talking'],
|
||||
models.Expiration.types['Breathing']),
|
||||
weights=(1, 2)
|
||||
)
|
||||
)
|
||||
),
|
||||
exposed=models.Population(
|
||||
number=18,
|
||||
presence=models.SpecificInterval(present_times = ((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
|
||||
activity=models.Activity.types['Seated'],
|
||||
mask=models.Mask.types['No mask']
|
||||
)
|
||||
)
|
||||
return office_model_no_vent
|
||||
|
||||
def office_model_no_mask_windows_open_breaks():
|
||||
office_model_no_vent = mc.ExposureModel(
|
||||
concentration_model=mc.ConcentrationModel(
|
||||
room=models.Room(volume=160, humidity=0.3),
|
||||
ventilation = models.MultipleVentilation(
|
||||
ventilations=(
|
||||
models.SlidingWindow(
|
||||
active=models.SpecificInterval(present_times=((1.5, 2), (3.5, 4.5), (6, 6.5))),
|
||||
inside_temp=models.PiecewiseConstant((0, 24), (295,)),
|
||||
outside_temp=models.PiecewiseConstant((0, 24), (291,)),
|
||||
window_height=1.6,
|
||||
opening_length=0.6,
|
||||
),
|
||||
models.AirChange(active=models.PeriodicInterval(period=120, duration=120), air_exch=0.25),
|
||||
)
|
||||
),
|
||||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
presence=models.SpecificInterval(present_times=((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
|
||||
virus=mc.SARSCoV2(
|
||||
viral_load_in_sputum=symptomatic_vl_frequencies,
|
||||
infectious_dose=infectious_dose_distribution,
|
||||
viable_to_RNA=infectious_virus_distribution,
|
||||
),
|
||||
mask=models.Mask.types["No mask"],
|
||||
activity=activity_distributions['Seated'],
|
||||
expiration=models.MultipleExpiration(
|
||||
expirations = (models.Expiration.types['Talking'],
|
||||
models.Expiration.types['Breathing']),
|
||||
weights=(1, 2)
|
||||
)
|
||||
)
|
||||
),
|
||||
exposed=models.Population(
|
||||
number=18,
|
||||
presence=models.SpecificInterval(present_times=((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
|
||||
activity=models.Activity.types['Seated'],
|
||||
mask=models.Mask.types['No mask']
|
||||
)
|
||||
)
|
||||
return office_model_no_vent
|
||||
|
||||
def office_model_no_mask_windows_open_alltimes():
|
||||
office_model_no_vent = mc.ExposureModel(
|
||||
concentration_model=mc.ConcentrationModel(
|
||||
room=models.Room(volume=160, humidity=0.3),
|
||||
ventilation=models.MultipleVentilation(
|
||||
ventilations=(
|
||||
models.SlidingWindow(
|
||||
active=models.PeriodicInterval(period=120, duration=120),
|
||||
inside_temp=models.PiecewiseConstant((0, 24), (295,)),
|
||||
outside_temp=models.PiecewiseConstant((0, 24), (291,)),
|
||||
window_height=1.6, opening_length=0.6,
|
||||
),
|
||||
models.AirChange(active=models.PeriodicInterval(period=120, duration=120), air_exch=0.25),
|
||||
)
|
||||
),
|
||||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
presence=models.SpecificInterval(present_times=((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
|
||||
virus=mc.SARSCoV2(
|
||||
viral_load_in_sputum=symptomatic_vl_frequencies,
|
||||
infectious_dose=infectious_dose_distribution,
|
||||
viable_to_RNA=infectious_virus_distribution,
|
||||
),
|
||||
mask=models.Mask.types["No mask"],
|
||||
activity=activity_distributions['Seated'],
|
||||
expiration=models.MultipleExpiration(
|
||||
expirations = (models.Expiration.types['Talking'],
|
||||
models.Expiration.types['Breathing']),
|
||||
weights=(1, 2)
|
||||
)
|
||||
)
|
||||
),
|
||||
exposed=models.Population(
|
||||
number=18,
|
||||
presence=models.SpecificInterval(present_times=((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
|
||||
activity=models.Activity.types['Seated'],
|
||||
mask=models.Mask.types['No mask']
|
||||
)
|
||||
)
|
||||
return office_model_no_vent
|
||||
|
||||
|
||||
######### Standard exposure models ###########
|
||||
|
||||
######### Breathing model ###########
|
||||
def breathing_exposure(activity: str, mask: str):
|
||||
exposure_mc = mc.ExposureModel(
|
||||
concentration_model=mc.ConcentrationModel(
|
||||
room=models.Room(volume=100, humidity=0.5),
|
||||
ventilation=models.AirChange(
|
||||
active=models.SpecificInterval(((0, 24),)),
|
||||
air_exch=0.25,
|
||||
),
|
||||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
virus=mc.Virus(
|
||||
viral_load_in_sputum=symptomatic_vl_frequencies,
|
||||
infectious_dose=infectious_dose_distribution,
|
||||
viable_to_RNA=infectious_virus_distribution
|
||||
),
|
||||
presence=mc.SpecificInterval(((0, 2),)),
|
||||
mask=models.Mask.types[mask],
|
||||
activity=activity_distributions[activity],
|
||||
expiration=models.Expiration.types['Breathing'],
|
||||
),
|
||||
),
|
||||
exposed=mc.Population(
|
||||
number=14,
|
||||
presence=mc.SpecificInterval(((0, 2),)),
|
||||
activity=models.Activity.types[activity],
|
||||
mask=models.Mask.types[mask],
|
||||
),
|
||||
)
|
||||
return exposure_mc
|
||||
|
||||
######### Speaking model ###########
|
||||
def speaking_exposure(activity: str, mask: str):
|
||||
exposure_mc = mc.ExposureModel(
|
||||
concentration_model=mc.ConcentrationModel(
|
||||
room=models.Room(volume=100, humidity=0.5),
|
||||
ventilation=models.AirChange(
|
||||
active=models.SpecificInterval(((0, 24),)),
|
||||
air_exch=0.25,
|
||||
),
|
||||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
virus=mc.Virus(
|
||||
viral_load_in_sputum=symptomatic_vl_frequencies,
|
||||
infectious_dose=infectious_dose_distribution,
|
||||
viable_to_RNA=infectious_virus_distribution,
|
||||
),
|
||||
presence=mc.SpecificInterval(((0, 2),)),
|
||||
mask=models.Mask.types[mask],
|
||||
activity=activity_distributions[activity],
|
||||
expiration=models.Expiration.types['Talking'],
|
||||
),
|
||||
),
|
||||
exposed=mc.Population(
|
||||
number=14,
|
||||
presence=mc.SpecificInterval(((0, 2),)),
|
||||
activity=models.Activity.types[activity],
|
||||
mask=models.Mask.types[mask],
|
||||
),
|
||||
)
|
||||
return exposure_mc
|
||||
|
||||
######### Infected Population model ###########
|
||||
def infected_model(mask: str, activity: str, expiratory_activity: str):
|
||||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
virus=mc.Virus(
|
||||
viral_load_in_sputum=symptomatic_vl_frequencies,
|
||||
infectious_dose=infectious_dose_distribution,
|
||||
viable_to_RNA=infectious_virus_distribution,
|
||||
),
|
||||
presence=mc.SpecificInterval(((0, 2),)),
|
||||
mask=models.Mask.types[mask],
|
||||
activity=activity_distributions[activity],
|
||||
expiration=models.Expiration.types[expiratory_activity])
|
||||
|
||||
return infected
|
||||
|
|
@ -1,55 +0,0 @@
|
|||
""" Title: COVID Airborne Risk Assessment
|
||||
Author: <author(s) names>
|
||||
Date: <date>
|
||||
Code version: <code version>
|
||||
Availability: <where it's located> """
|
||||
|
||||
from cara.models import ExposureModel, InfectedPopulation
|
||||
from cara import model_scenarios_paper
|
||||
from cara.results_paper import *
|
||||
from cara.test_plots import *
|
||||
from cara.monte_carlo.data import symptomatic_vl_frequencies
|
||||
from itertools import product
|
||||
from dataclasses import dataclass
|
||||
|
||||
# Exhaled virions while talking, seated #
|
||||
#print('\n<<<<<<<<<<< Vlout for Talking, seated >>>>>>>>>>>')
|
||||
#exposure_model_from_vl_talking()
|
||||
|
||||
# Exhaled virions while breathing, seated #
|
||||
#print('\n<<<<<<<<<<< Vlout for Breathing, seated >>>>>>>>>>>')
|
||||
#exposure_model_from_vl_breathing()
|
||||
|
||||
# Exhaled virions while breathing, light activity #
|
||||
#print('\n<<<<<<<<<<< Vlout for Shouting, light activity >>>>>>>>>>>')
|
||||
#exposure_model_from_vl_shouting()
|
||||
|
||||
# Exhaled virions while talking according to BLO model, seated #
|
||||
#print('\n<<<<<<<<<<< Vlout for Talking, seated with chosen Cn,L >>>>>>>>>>>')
|
||||
#exposure_model_from_vl_talking_cn()
|
||||
|
||||
# Exhaled virions while breathing according to BLO model, seated #
|
||||
#print('\n<<<<<<<<<<< Vlout for Breathing, seated with chosen Cn,B >>>>>>>>>>>')
|
||||
#exposure_model_from_vl_breathing_cn()
|
||||
#print('\n')
|
||||
|
||||
############ Plots with viral loads and emission rates + statistical data ############
|
||||
#present_vl_er_histograms(activity='Seated', mask='No mask')
|
||||
#present_vl_er_histograms(activity='Light activity', mask='No mask')
|
||||
#present_vl_er_histograms(activity='Heavy exercise', mask='No mask')
|
||||
|
||||
############ CDFs for comparing the QR-Values in different scenarios ############
|
||||
#generate_cdf_curves()
|
||||
|
||||
############ Deposition Fraction Graph ############
|
||||
#print('\n<<<<<<<<<<< Deposition Fraction for Breathing, seated >>>>>>>>>>>')
|
||||
#calculate_deposition_factor()
|
||||
|
||||
############ Comparison between concentration curves ############
|
||||
#compare_concentration_curves()
|
||||
|
||||
############ Emission Rate Violin plot ############
|
||||
compare_viruses_vr()
|
||||
|
||||
############ Used for testing ############
|
||||
#exposure_model_from_vl_talking_new_points()
|
||||
|
|
@ -1,789 +0,0 @@
|
|||
from numpy.core.function_base import linspace
|
||||
from cara import models
|
||||
from cara.monte_carlo.data import activity_distributions
|
||||
from tqdm import tqdm
|
||||
from matplotlib.patches import Rectangle, Patch
|
||||
from scipy.spatial import ConvexHull
|
||||
from model_scenarios_paper import *
|
||||
import cara.monte_carlo as mc
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import matplotlib.lines as mlines
|
||||
import matplotlib.patches as patches
|
||||
import matplotlib as mpl
|
||||
from random import randrange
|
||||
|
||||
|
||||
######### Plot material #########
|
||||
SAMPLE_SIZE = 250000
|
||||
viral_loads = np.linspace(2, 12, 600)
|
||||
|
||||
############# Markers (for legend) #############
|
||||
markers = [5, 'd', 4]
|
||||
|
||||
""" Exhaled virions from exposure models """
|
||||
|
||||
######### Breathing #########
|
||||
|
||||
|
||||
def exposure_model_from_vl_breathing():
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
|
||||
er_means = []
|
||||
er_means_1h = []
|
||||
er_medians = []
|
||||
lower_percentiles = []
|
||||
upper_percentiles = []
|
||||
|
||||
for vl in tqdm(viral_loads):
|
||||
exposure_mc = breathing_exposure_vl(vl)
|
||||
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
|
||||
# divide by 2 to have in 30min (half an hour)
|
||||
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(cn_B=0.06, cn_L=0.2) / 2
|
||||
er_means.append(np.mean(emission_rate))
|
||||
er_medians.append(np.median(emission_rate))
|
||||
lower_percentiles.append(np.quantile(emission_rate, 0.01))
|
||||
upper_percentiles.append(np.quantile(emission_rate, 0.99))
|
||||
emission_rate_1h = exposure_model.concentration_model.infected.emission_rate_when_present(cn_B=0.06, cn_L=0.2)
|
||||
er_means_1h.append(np.mean(emission_rate_1h))
|
||||
|
||||
# divide by 2 to have in 30min (half an hour)
|
||||
coleman_etal_er_breathing_2 = [x/2 for x in coleman_etal_er_breathing]
|
||||
milton_er_2 = [x/2 for x in milton_er]
|
||||
yann_er_2 = [x/2 for x in yann_er]
|
||||
|
||||
ax.plot(viral_loads, er_means)
|
||||
ax.fill_between(viral_loads, lower_percentiles,
|
||||
upper_percentiles, alpha=0.2)
|
||||
ax.set_yscale('log')
|
||||
|
||||
ratio = np.mean(10**viral_loads / er_means)
|
||||
ratio_1h = np.mean(10**viral_loads / er_means_1h)
|
||||
print('Mean swab-to-aersol vl ratio in 30min:')
|
||||
print(format(ratio, "5.1e"))
|
||||
print('Mean swab-to-aersol vl ratio emission rate per hour:')
|
||||
print(format(ratio_1h, "5.1e"))
|
||||
|
||||
############# Coleman #############
|
||||
scatter_coleman_data(coleman_etal_vl_breathing,
|
||||
coleman_etal_er_breathing_2)
|
||||
|
||||
############# Milton et al #############
|
||||
scatter_milton_data(milton_vl, milton_er_2)
|
||||
|
||||
############# Yan et al #############
|
||||
scatter_yann_data(yann_vl, yann_er_2)
|
||||
|
||||
############ Legend ############
|
||||
build_breathing_legend(fig)
|
||||
|
||||
############ Plot ############
|
||||
plt.title('',
|
||||
fontsize=16, fontweight="bold")
|
||||
plt.ylabel(
|
||||
'Aerosol viral load, $\mathrm{vl_{out}}$\n(RNA copies)', fontsize=14)
|
||||
plt.xticks(ticks=[i for i in range(2, 13)], labels=[
|
||||
'$10^{' + str(i) + '}$' for i in range(2, 13)])
|
||||
plt.xlabel('NP viral load, $\mathrm{vl_{in}}$\n(RNA copies)', fontsize=14)
|
||||
plt.show()
|
||||
|
||||
|
||||
""" Variation according to the BLO model """
|
||||
############ Breathing ############
|
||||
|
||||
|
||||
def exposure_model_from_vl_breathing_cn():
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
|
||||
n_lines = 30
|
||||
cns = np.linspace(0.01, 0.5, n_lines)
|
||||
|
||||
cmap = define_colormap(cns)
|
||||
|
||||
for cn in tqdm(cns):
|
||||
er_means = np.array([])
|
||||
for vl in viral_loads:
|
||||
exposure_mc = breathing_exposure_vl(vl)
|
||||
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
|
||||
# divide by 2 to have in 30min (half an hour)
|
||||
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
|
||||
cn_B=cn, cn_L=0.2) / 2
|
||||
er_means = np.append(er_means, np.mean(emission_rate))
|
||||
|
||||
# divide by 2 to have in 30min (half an hour)
|
||||
coleman_etal_er_breathing_2 = [x/2 for x in coleman_etal_er_breathing]
|
||||
milton_er_2 = [x/2 for x in milton_er]
|
||||
yann_er_2 = [x/2 for x in yann_er]
|
||||
ax.plot(viral_loads, er_means, color=cmap.to_rgba(
|
||||
cn, alpha=0.75), linewidth=0.5)
|
||||
|
||||
# The dashed line for the chosen Cn,B
|
||||
er_means = np.array([])
|
||||
for vl in viral_loads:
|
||||
exposure_mc = breathing_exposure_vl(vl)
|
||||
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
|
||||
# divide by 2 to have in 30min (half an hour)
|
||||
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
|
||||
cn_B=0.06, cn_L=0.2) / 2
|
||||
er_means = np.append(er_means, np.mean(emission_rate))
|
||||
ax.plot(viral_loads, er_means, color=cmap.to_rgba(
|
||||
cn, alpha=0.75), linewidth=1, ls='--')
|
||||
plt.text(viral_loads[int(len(viral_loads)*0.9)], 10**4.2,
|
||||
r"$\mathbf{c_{n,B}=0.06}$", color=cmap.to_rgba(cn), fontsize=12)
|
||||
|
||||
cmap = fig.colorbar(cmap, ticks=[0.01, 0.1, 0.25, 0.5])
|
||||
cmap.set_label(label='Particle emission concentration, ${c_{n,B}}$', fontsize=12)
|
||||
ax.set_yscale('log')
|
||||
|
||||
############# Coleman #############
|
||||
scatter_coleman_data(coleman_etal_vl_breathing,
|
||||
coleman_etal_er_breathing_2)
|
||||
|
||||
############# Milton et al #############
|
||||
scatter_milton_data(milton_vl, milton_er_2)
|
||||
|
||||
############# Yan et al #############
|
||||
scatter_yann_data(yann_vl, yann_er_2)
|
||||
|
||||
############ Legend ############
|
||||
build_breathing_legend(fig)
|
||||
|
||||
############ Plot ############
|
||||
plt.title('',
|
||||
fontsize=16, fontweight="bold")
|
||||
plt.ylabel(
|
||||
'Aerosol viral load, $\mathrm{vl_{out}}$\n(RNA copies)', fontsize=14)
|
||||
plt.xticks(ticks=[i for i in range(2, 13)], labels=[
|
||||
'$10^{' + str(i) + '}$' for i in range(2, 13)])
|
||||
plt.xlabel('NP viral load, $\mathrm{vl_{in}}$\n(RNA copies)', fontsize=14)
|
||||
plt.show()
|
||||
|
||||
############ Talking ############
|
||||
|
||||
|
||||
def exposure_model_from_vl_talking():
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
|
||||
er_means = []
|
||||
er_means_1h = []
|
||||
er_medians = []
|
||||
lower_percentiles = []
|
||||
upper_percentiles = []
|
||||
|
||||
for vl in tqdm(viral_loads):
|
||||
exposure_mc = talking_exposure_vl(vl)
|
||||
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
|
||||
# divide by 4 to have in 15min (quarter of an hour)
|
||||
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(cn_B=0.06, cn_L=0.2)/4
|
||||
er_means.append(np.mean(emission_rate))
|
||||
er_medians.append(np.median(emission_rate))
|
||||
lower_percentiles.append(np.quantile(emission_rate, 0.01))
|
||||
upper_percentiles.append(np.quantile(emission_rate, 0.99))
|
||||
emission_rate_1h = exposure_model.concentration_model.infected.emission_rate_when_present(cn_B=0.06, cn_L=0.2)
|
||||
er_means_1h.append(np.mean(emission_rate_1h))
|
||||
|
||||
# divide by 4 to have in 15min (quarter of an hour)
|
||||
coleman_etal_er_talking_2 = [x/4 for x in coleman_etal_er_talking]
|
||||
|
||||
ax.plot(viral_loads, er_means)
|
||||
ax.fill_between(viral_loads, lower_percentiles,
|
||||
upper_percentiles, alpha=0.2)
|
||||
ax.set_yscale('log')
|
||||
|
||||
ratio = np.mean(10**viral_loads / er_means)
|
||||
ratio_1h = np.mean(10**viral_loads / er_means_1h)
|
||||
print('Mean swab-to-aersol vl ratio in 30min:')
|
||||
print(format(ratio, "5.1e"))
|
||||
print('Mean swab-to-aersol vl ratio emission rate per hour:')
|
||||
print(format(ratio_1h, "5.1e"))
|
||||
|
||||
############# Coleman #############
|
||||
scatter_coleman_data(coleman_etal_vl_talking, coleman_etal_er_talking_2)
|
||||
|
||||
############ Legend ############
|
||||
build_talking_legend(fig)
|
||||
|
||||
############ Plot ############
|
||||
plt.title('',
|
||||
fontsize=16, fontweight="bold")
|
||||
plt.ylabel(
|
||||
'Aerosol viral load, $\mathrm{vl_{out}}$\n(RNA copies)', fontsize=14)
|
||||
plt.xticks(ticks=[i for i in range(2, 13)], labels=[
|
||||
'$10^{' + str(i) + '}$' for i in range(2, 13)])
|
||||
plt.xlabel('NP viral load, $\mathrm{vl_{in}}$\n(RNA copies)', fontsize=14)
|
||||
|
||||
plt.show()
|
||||
|
||||
def exposure_model_from_vl_talking_cn():
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
|
||||
n_lines = 30
|
||||
cns = np.linspace(0.01, 2, n_lines)
|
||||
cmap = define_colormap(cns)
|
||||
|
||||
for cn in tqdm(cns):
|
||||
er_means = np.array([])
|
||||
for vl in viral_loads:
|
||||
exposure_mc = talking_exposure_vl(vl)
|
||||
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
|
||||
# divide by 4 to have in 15min (quarter of an hour)
|
||||
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
|
||||
cn_B=0.1, cn_L=cn) / 4
|
||||
er_means = np.append(er_means, np.mean(emission_rate))
|
||||
|
||||
# divide by 4 to have in 15min (quarter of an hour)
|
||||
coleman_etal_er_talking_2 = [x/4 for x in coleman_etal_er_talking]
|
||||
ax.plot(viral_loads, er_means, color=cmap.to_rgba(
|
||||
cn, alpha=0.75), linewidth=0.5)
|
||||
|
||||
# The dashed line for the chosen Cn,L
|
||||
er_means = np.array([])
|
||||
for vl in viral_loads:
|
||||
exposure_mc = talking_exposure_vl(vl)
|
||||
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
|
||||
# divide by 4 to have in 15min
|
||||
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
|
||||
cn_B=0.06, cn_L=0.2) / 4
|
||||
er_means = np.append(er_means, np.mean(emission_rate))
|
||||
ax.plot(viral_loads, er_means, color=cmap.to_rgba(
|
||||
cn, alpha=0.75), linewidth=1, ls='--')
|
||||
plt.text(viral_loads[int(len(viral_loads)*0.93)], 10**5.5,
|
||||
r"$\mathbf{c_{n,L}=0.2}$", color=cmap.to_rgba(cn), fontsize=12)
|
||||
|
||||
cmap = fig.colorbar(cmap, ticks=[0.01, 0.5, 1.0, 2.0])
|
||||
cmap.set_label(label='Particle emission concentration, ${c_{n,L}}$', fontsize=12)
|
||||
ax.set_yscale('log')
|
||||
|
||||
############# Coleman #############
|
||||
scatter_coleman_data(coleman_etal_vl_talking, coleman_etal_er_talking_2)
|
||||
|
||||
############ Legend ############
|
||||
build_talking_legend(fig)
|
||||
|
||||
############ Plot ############
|
||||
plt.title('',
|
||||
fontsize=16, fontweight="bold")
|
||||
plt.ylabel(
|
||||
'Aerosol viral load, $\mathrm{vl_{out}}$\n(RNA copies)', fontsize=14)
|
||||
plt.xticks(ticks=[i for i in range(2, 13)], labels=[
|
||||
'$10^{' + str(i) + '}$' for i in range(2, 13)])
|
||||
plt.xlabel('NP viral load, $\mathrm{vl_{in}}$\n(RNA copies)', fontsize=14)
|
||||
plt.show()
|
||||
|
||||
####### Shouting ########
|
||||
|
||||
|
||||
def exposure_model_from_vl_shouting():
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
|
||||
er_means_1h = []
|
||||
|
||||
for vl in tqdm(viral_loads):
|
||||
exposure_mc = shouting_exposure_vl(vl)
|
||||
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
|
||||
emission_rate_1h = exposure_model.concentration_model.infected.emission_rate_when_present(cn_B=0.06, cn_L=0.2)
|
||||
er_means_1h.append(np.mean(emission_rate_1h))
|
||||
|
||||
ax.plot(viral_loads, er_means_1h)
|
||||
ax.set_yscale('log')
|
||||
|
||||
ratio_1h = np.mean(10**viral_loads / er_means_1h)
|
||||
print('Mean swab-to-aersol vl ratio emission rate per hour:')
|
||||
print(format(ratio_1h, "5.1e"))
|
||||
|
||||
############ Plot ############
|
||||
plt.title('',
|
||||
fontsize=16, fontweight="bold")
|
||||
plt.ylabel(
|
||||
'Aerosol viral load, $\mathrm{vl_{out}}$\n(RNA copies)', fontsize=14)
|
||||
plt.xticks(ticks=[i for i in range(2, 13)], labels=[
|
||||
'$10^{' + str(i) + '}$' for i in range(2, 13)])
|
||||
plt.xlabel('NP viral load, $\mathrm{vl_{in}}$\n(RNA copies)', fontsize=14)
|
||||
plt.show()
|
||||
|
||||
|
||||
############ Plots with viral loads and emission rates ############
|
||||
############ Statistical Data ############
|
||||
|
||||
|
||||
def get_statistical_data(activity: str, mask: str):
|
||||
############ Breathing model ############
|
||||
exposure_mc = breathing_exposure(activity, mask)
|
||||
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
|
||||
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
|
||||
cn_B=0.06, cn_L=0.2)
|
||||
breathing_er = [np.log10(er) for er in emission_rate]
|
||||
print('\n<<<<<<<<<<< Breathing model statistics >>>>>>>>>>>')
|
||||
print_er_info(emission_rate, breathing_er)
|
||||
|
||||
############ Speaking model ############
|
||||
exposure_mc = speaking_exposure(activity, mask)
|
||||
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
|
||||
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
|
||||
cn_B=0.06, cn_L=0.2)
|
||||
speaking_er = [np.log10(er) for er in emission_rate]
|
||||
print('\n<<<<<<<<<<< Speaking model statistics >>>>>>>>>>>')
|
||||
print_er_info(emission_rate, speaking_er)
|
||||
|
||||
############ Shouting model ############
|
||||
exposure_mc = shouting_exposure(activity, mask)
|
||||
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
|
||||
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
|
||||
cn_B=0.06, cn_L=0.2)
|
||||
shouting_er = [np.log10(er) for er in emission_rate]
|
||||
print('\n<<<<<<<<<<< Shouting model statistics >>>>>>>>>>>')
|
||||
print_er_info(emission_rate, shouting_er)
|
||||
|
||||
viral_load_in_sputum = exposure_model.concentration_model.infected.virus.viral_load_in_sputum
|
||||
|
||||
return viral_load_in_sputum, breathing_er, speaking_er, shouting_er
|
||||
|
||||
|
||||
def present_vl_er_histograms(activity: str, mask: str):
|
||||
viral_load_in_sputum, breathing_er, speaking_er, shouting_er = get_statistical_data(activity, mask)
|
||||
|
||||
fig, axs = plt.subplots(1, 2, sharex=False, sharey=False)
|
||||
fig.set_figheight(5)
|
||||
fig.set_figwidth(10)
|
||||
plt.tight_layout()
|
||||
plt.subplots_adjust(wspace=0.2)
|
||||
plt.subplots_adjust(top=0.9)
|
||||
plt.subplots_adjust(bottom=0.15)
|
||||
|
||||
viral_loads = [np.log10(vl) for vl in viral_load_in_sputum]
|
||||
|
||||
axs[0].hist(viral_loads, bins = 300, color='lightgrey')
|
||||
axs[0].set_xlabel('vl$_{\mathrm{in}}$ (log$_{10}$ RNA copies mL$^{-1}$)')
|
||||
|
||||
mean = np.mean(viral_loads)
|
||||
axs[0].vlines(x=(mean), ymin=0, ymax=axs[0].get_ylim()[1], colors=('grey'), linestyles=('dashed'))
|
||||
|
||||
breathing_mean_er = np.mean(breathing_er)
|
||||
speaking_mean_er = np.mean(speaking_er)
|
||||
shouting_mean_er = np.mean(shouting_er)
|
||||
|
||||
axs[1].hist(breathing_er, bins = 300, color='lightsteelblue')
|
||||
axs[1].hist(speaking_er, bins = 300, color='wheat')
|
||||
axs[1].hist(shouting_er, bins = 300, color='darkseagreen')
|
||||
axs[1].set_xlabel('vR (log$_{10}$ virions h$^{-1}$)')
|
||||
|
||||
axs[1].vlines(x=(breathing_mean_er, speaking_mean_er, shouting_mean_er), ymin=0, ymax=axs[1].get_ylim()[1], colors=('cornflowerblue', 'goldenrod', 'olivedrab'), alpha=(0.75, 0.75, 0.75), linestyles=('dashed', 'dashed', 'dashed'))
|
||||
|
||||
labels = [Patch([], [], color=color, label=label)
|
||||
for color, label in zip(['lightgrey', 'lightsteelblue', 'wheat', 'darkseagreen'],
|
||||
['Viral Load', 'Breathing', 'Speaking', 'Shouting'])]
|
||||
labels.append(mlines.Line2D([], [], color='black',
|
||||
marker='', linestyle='dashed', label='Mean'))
|
||||
|
||||
|
||||
for x in (0, 1):
|
||||
axs[x].set_yticklabels([])
|
||||
axs[x].set_yticks([])
|
||||
|
||||
plt.legend(handles=labels, loc='upper left', bbox_to_anchor=(1, 1))
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
############ CDFs for comparing the QR-Values in different scenarios ############
|
||||
|
||||
|
||||
def generate_cdf_curves():
|
||||
fig, axs = plt.subplots(3, 1, sharex='all')
|
||||
|
||||
############ Breathing models ############
|
||||
br_seated = breathing_seated_exposure()
|
||||
br_seated_model = br_seated.build_model(size=SAMPLE_SIZE)
|
||||
|
||||
br_light_activity = breathing_light_activity_exposure()
|
||||
br_light_activity_model = br_light_activity.build_model(size=SAMPLE_SIZE)
|
||||
|
||||
br_heavy_exercise = breathing_heavy_exercise_exposure()
|
||||
br_heavy_exercise_model = br_heavy_exercise.build_model(size=SAMPLE_SIZE)
|
||||
|
||||
############ Speaking models ############
|
||||
sp_seated = speaking_seated_exposure()
|
||||
sp_seated_model = sp_seated.build_model(size=SAMPLE_SIZE)
|
||||
|
||||
sp_light_activity = speaking_light_activity_exposure()
|
||||
sp_light_activity_model = sp_light_activity.build_model(size=SAMPLE_SIZE)
|
||||
|
||||
sp_heavy_exercise = speaking_heavy_exercise_exposure()
|
||||
sp_heavy_exercise_model = sp_heavy_exercise.build_model(size=SAMPLE_SIZE)
|
||||
|
||||
############ Shouting models ############
|
||||
sh_seated = shouting_seated_exposure()
|
||||
sh_seated_model = sh_seated.build_model(size=SAMPLE_SIZE)
|
||||
|
||||
sh_light_activity = shouting_light_activity_exposure()
|
||||
sh_light_activity_model = sh_light_activity.build_model(size=SAMPLE_SIZE)
|
||||
|
||||
sh_heavy_exercise = shouting_heavy_exercise_exposure()
|
||||
sh_heavy_exercise_model = sh_heavy_exercise.build_model(size=SAMPLE_SIZE)
|
||||
|
||||
er_values = [np.log10(scenario.concentration_model.infected.emission_rate_when_present(cn_B=0.06, cn_L=0.2)) for scenario in (br_seated_model, br_light_activity_model,
|
||||
br_heavy_exercise_model, sp_seated_model, sp_light_activity_model, sp_heavy_exercise_model, sh_seated_model, sh_light_activity_model, sh_heavy_exercise_model)]
|
||||
left = min(np.min(ers) for ers in er_values)
|
||||
right = max(np.max(ers) for ers in er_values)
|
||||
|
||||
# Colors can be changed here
|
||||
colors_breathing = ['lightsteelblue', 'cornflowerblue', 'royalblue']
|
||||
colors_speaking = ['wheat', 'tan', 'orange']
|
||||
colors_shouting = ['palegreen', 'darkseagreen', 'forestgreen']
|
||||
colors = [colors_breathing, colors_speaking, colors_shouting]
|
||||
|
||||
breathing_rates = ['Seated', 'Light activity', 'Heavy activity']
|
||||
activities = ['Breathing', 'Speaking', 'Shouting']
|
||||
lines_breathing = [mlines.Line2D([], [], color=color, markersize=15, label=label)
|
||||
for color, label in zip(colors_breathing, breathing_rates)]
|
||||
lines_speaking = [mlines.Line2D([], [], color=color, markersize=15, label=label)
|
||||
for color, label in zip(colors_speaking, breathing_rates)]
|
||||
lines_shouting = [mlines.Line2D([], [], color=color, markersize=15, label=label)
|
||||
for color, label in zip(colors_shouting, breathing_rates)]
|
||||
lines = [lines_breathing, lines_speaking, lines_shouting]
|
||||
|
||||
samples: int = 50000
|
||||
for i in range(3):
|
||||
axs[i].hist(er_values[3 * i:3 * (i + 1)], bins=2000,
|
||||
histtype='step', cumulative=True, range=(-7, 6), color=colors[i])
|
||||
axs[i].set_xlim(-6, 6)
|
||||
axs[i].set_yticks([0, samples / 2, samples])
|
||||
axs[i].set_yticklabels(['0.0', '0.5', '1.0'])
|
||||
axs[i].yaxis.set_label_position("right")
|
||||
axs[i].set_ylabel(activities[i], fontsize=14)
|
||||
axs[i].grid(linestyle='--')
|
||||
axs[i].legend(handles=lines[i], loc='upper left')
|
||||
|
||||
plt.xlabel('$\mathrm{vR}$', fontsize=16)
|
||||
tick_positions = np.arange(-6, 6, 2)
|
||||
plt.xticks(ticks=tick_positions, labels=[
|
||||
'$\;10^{' + str(i) + '}$' for i in tick_positions])
|
||||
|
||||
fig.text(0.02, 0.5, 'Cumulative Distribution Function',
|
||||
va='center', rotation='vertical', fontsize=14)
|
||||
fig.set_figheight(8)
|
||||
fig.set_figwidth(5)
|
||||
plt.show()
|
||||
|
||||
############ Deposition Fraction Graph #############
|
||||
def calculate_deposition_factor():
|
||||
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
|
||||
diameters = np.linspace(0.001, 0.01, 1000) #particle diameter (μm)
|
||||
diameters = np.append(diameters, np.linspace(0.01, 0.1, 1000))
|
||||
diameters = np.append(diameters, np.linspace(0.1, 1., 1000))
|
||||
diameters = np.append(diameters, np.linspace(1., 10., 1000))
|
||||
diameters = np.append(diameters, np.linspace(10, 100, 1000))
|
||||
|
||||
fractions_et = []
|
||||
fractions_tb = []
|
||||
fractions_al = []
|
||||
fractions_df = []
|
||||
for d in diameters:
|
||||
|
||||
IF = 1 - 0.5 * (1 - (1 / (1 + (0.00076*(d**2.8)))))
|
||||
DF_et = IF * (
|
||||
(1 / ( 1 + np.exp(6.84 + 1.183 * np.log(d)))) + (1 / (1 + np.exp(0.924 - 1.885 * np.log(d))))
|
||||
)
|
||||
fractions_et.append(DF_et)
|
||||
|
||||
DF_tb = (0.00352/d) * (np.exp(-0.234*((np.log(d) + 3.40)**2)) + (63.9 * np.exp(-0.819*((np.log(d) - 1.61)**2))))
|
||||
fractions_tb.append(DF_tb)
|
||||
|
||||
DF_al = (0.0155/d) * (np.exp(-0.416*((np.log(d) + 2.84)**2)) + (19.11*np.exp(-0.482 * ((np.log(d) - 1.362)**2))))
|
||||
fractions_al.append(DF_al)
|
||||
|
||||
DF = IF * (0.0587 + (0.911/(1 + np.exp(4.77 + 1.485 * np.log(d)))) + (0.943/(1 + np.exp(0.508 - 2.58 * np.log(d)))))
|
||||
fractions_df.append(DF)
|
||||
|
||||
ax.plot(diameters, fractions_df, label = 'Total Deposition', color='k')
|
||||
ax.plot(diameters, fractions_et, label = 'Extrathoracic', ls='-.', lw=0.9, color='grey')
|
||||
ax.plot(diameters, fractions_tb, label = 'Tracheobronchial', ls='--', lw=0.9, color='darkgray')
|
||||
ax.plot(diameters, fractions_al, label = 'Alveolar', ls=(0,(1,1)), lw=0.9, color='darkgray')
|
||||
|
||||
ax.grid(linestyle='--')
|
||||
ax.set_xscale('log')
|
||||
ax.margins(x=0, y=0)
|
||||
plt.legend(bbox_to_anchor=(1.04,1), loc="upper left")
|
||||
|
||||
y_ticks = [0., 0.2, 0.4, 0.6, 0.8, 1]
|
||||
x_ticks = [0.001, 0.01, 0.1, 1, 10, 100]
|
||||
plt.yticks(ticks=y_ticks, labels=[
|
||||
str(i) for i in y_ticks])
|
||||
plt.xticks(ticks=x_ticks, labels=[
|
||||
str(i) for i in x_ticks])
|
||||
plt.xlabel('Particle diameter (μm)', fontsize=14)
|
||||
plt.ylabel('Deposition fraction\nf$_{dep}$', fontsize=14)
|
||||
|
||||
fig.set_figwidth(10)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
############ Compare concentration curves ############
|
||||
def compare_concentration_curves():
|
||||
|
||||
exp_models=[office_model_no_mask_windows_closed().build_model(size=SAMPLE_SIZE),
|
||||
office_model_no_mask_windows_open_breaks().build_model(size=SAMPLE_SIZE),
|
||||
office_model_no_mask_windows_open_alltimes().build_model(size=SAMPLE_SIZE)]
|
||||
|
||||
labels=['Windows closed', 'Window open during breaks', 'Window open at all times']
|
||||
colors=['tomato', 'lightskyblue', 'limegreen', '#1f77b4', 'seagreen', 'lightskyblue', 'deepskyblue']
|
||||
|
||||
start=min(min(model.concentration_model.infected.presence.transition_times()) for model in exp_models)
|
||||
stop=max(max(model.concentration_model.infected.presence.transition_times()) for model in exp_models)
|
||||
|
||||
TIMESTEP = 0.01
|
||||
times=np.arange(start, stop, TIMESTEP)
|
||||
|
||||
concentrations = [[np.mean(model.concentration_model.concentration(t)) for t in times] for model in exp_models]
|
||||
fig, ax = plt.subplots()
|
||||
for c, label, color in zip(concentrations, labels, colors):
|
||||
ax.plot(times, c, label=label, color=color)
|
||||
|
||||
ax.legend(loc='upper left')
|
||||
ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1] * 1.2)
|
||||
ax.spines["right"].set_visible(False)
|
||||
|
||||
cumulative_doses = [np.cumsum([
|
||||
np.array(model.exposure_between_bounds(float(time1), float(time2))).mean()
|
||||
for time1, time2 in zip(times[:-1], times[1:])
|
||||
]) for model in exp_models]
|
||||
|
||||
plt.xlabel("Exposure time ($h$)", fontsize=14)
|
||||
plt.ylabel("Mean concentration (virions m$^{-3}$)", fontsize=14)
|
||||
|
||||
ax1 = ax.twinx()
|
||||
for qd, label, color in zip(cumulative_doses, labels, colors):
|
||||
ax1.plot(times[:-1], qd, label='qD - ' + label, color=color, linestyle='dotted')
|
||||
ax1.spines["right"].set_linestyle("--")
|
||||
ax1.spines["right"].set_linestyle((0,(1,5)))
|
||||
ax1.set_ylabel('Mean cumulative dose (virions)', fontsize=14)
|
||||
ax1.set_ylim(ax1.get_ylim()[0], ax1.get_ylim()[1] * 1.2)
|
||||
|
||||
plt.show()
|
||||
|
||||
def compare_viruses_vr():
|
||||
|
||||
# Represented as tuples of three numbers on the interval [0, 1] (e.g. (1, 0, 0)) (R, G, B)
|
||||
colors = [(0, 0.5, 0), (0, 0, 0.5), (0.5, 0, 0)]
|
||||
|
||||
# The colors of the borders surrounding the violin plots
|
||||
border_colors = [(0, 0, 0), (0, 0, 0), (0, 0, 0)]
|
||||
|
||||
whisker_width = 0.8
|
||||
positions = [1, 2, 3, 12]
|
||||
|
||||
infected_populations = [infected_model('No mask', 'Light activity', activity).build_model(size=SAMPLE_SIZE) for activity in ('Breathing', 'Talking', 'Shouting')]
|
||||
|
||||
vrs = [np.log10(pop.emission_rate_when_present(cn_B=0.06, cn_L=0.2)) for pop in infected_populations]
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
ax.set_xlim((0, 13))
|
||||
|
||||
parts = ax.violinplot(vrs, quantiles=[(0.05, 0.95) for _ in vrs], showextrema=False)
|
||||
means = [np.log10(np.mean(10 ** vr)) for vr in vrs]
|
||||
ax.hlines(y=means,
|
||||
xmin=[pos - whisker_width / 2 for pos in positions[:3]],
|
||||
xmax=[pos + whisker_width / 2 for pos in positions[:3]],
|
||||
colors=colors)
|
||||
|
||||
|
||||
for pc, color, bc in zip(parts['bodies'], colors, border_colors):
|
||||
pc.set_facecolor(color)
|
||||
pc.set_edgecolor(bc)
|
||||
parts['cquantiles'].set_color([c for c in colors[:3] for _ in range(2)])
|
||||
|
||||
positions=np.linspace(5.5, 12.5, 25)
|
||||
|
||||
######### SARS-CoV-2 #########
|
||||
lower_bound = [290, 150, 150, 360,450, 610, 620, 1160, 1300, 1330, 1390, 1390, 1520, 2320, 6830, 9700, 42130]
|
||||
higher_bound = [2900, 1500, 1500, 3600, 4500, 6100, 6200, 11600, 13000, 13300, 13900, 13900, 15200, 23200, 68300, 97000, 421300]
|
||||
|
||||
for i in range(len(lower_bound)):
|
||||
data = np.random.uniform(lower_bound[i], higher_bound[i], size=200000)
|
||||
ax.boxplot(np.log10(data), positions=[positions[i]], medianprops=dict(color=colors[0]+ (0.5,)), whiskerprops=dict(color=colors[0]+ (0.5,)), boxprops=dict(color=colors[0]+ (0.5,)))
|
||||
|
||||
######### Measles #########
|
||||
lower_bound = [180, 6000, 27650, 86400]
|
||||
higher_bound = [1800, 60000, 276500, 864000]
|
||||
|
||||
for i in range(len(lower_bound)):
|
||||
data = np.random.uniform(lower_bound[i], higher_bound[i], size=200000)
|
||||
ax.boxplot(np.log10(data), positions=[positions[i+17]], medianprops=dict(color=colors[1]+ (0.5,)), whiskerprops=dict(color=colors[1]+ (0.5,)), boxprops=dict(color=colors[1]+ (0.5,)))
|
||||
|
||||
######### Influenza #########
|
||||
lower_bound = [1.1, 79.5, 790]
|
||||
higher_bound = [11, 795, 7900]
|
||||
|
||||
for i in range(len(lower_bound)):
|
||||
data = np.random.uniform(lower_bound[i], higher_bound[i], size=200000)
|
||||
ax.boxplot(np.log10(data), positions=[positions[i+21]], medianprops=dict(color=colors[2]+ (0.5,)), whiskerprops=dict(color=colors[2]+ (0.5,)), boxprops=dict(color=colors[2]+ (0.5,)))
|
||||
|
||||
######### Rhinovirus #########
|
||||
lower_bound = [31]
|
||||
higher_bound = [310]
|
||||
|
||||
for i in range(len(lower_bound)):
|
||||
data = np.random.uniform(lower_bound[i], higher_bound[i], size=200000)
|
||||
ax.boxplot(np.log10(data), positions=[positions[i+24]], medianprops=dict(color=(0.5, 0.5, 0.5, 0.5, )), whiskerprops=dict(color=(0.5, 0.5, 0.5, 0.5,)), boxprops=dict(color=(0.5, 0.5, 0.5, 0.5,)))
|
||||
|
||||
handles = [patches.Patch(color=c, label=l) for c, l in zip([p + (0.5,) for p in [(0, 0.5, 0), (0, 0, 0.5), (0.5, 0, 0), (0.5, 0.5, 0.5)]], ('SARS-CoV-2', 'Measles', 'Influenza', 'Rhinovirus'))]
|
||||
boxplot_legend = plt.legend(handles=handles, loc='lower right')
|
||||
|
||||
handles = [patches.Patch(color=c, label=l) for c, l in zip([p + (0.3,) for p in colors], ('Breathing', 'Speaking', 'Shouting'))]
|
||||
plt.legend(handles=handles, loc='lower left', bbox_to_anchor=(0.15, 0.))
|
||||
plt.gca().add_artist(boxplot_legend)
|
||||
|
||||
ax.vlines(x=5, ymin=ax.get_ylim()[0], ymax=ax.get_ylim()[1], colors=(0.8, 0.8, 0.8))
|
||||
ax.set_yticks([i for i in range(-4, 7, 2)])
|
||||
ax.set_yticklabels(['$10^{' + str(i) + '}$' for i in range(-4, 7, 2)])
|
||||
ax.set_xticks([2.5, 9])
|
||||
ax.set_xticklabels(['SARS-CoV-2', 'Experimental Results'])
|
||||
ax.set_ylabel('Emission rate (virions h$^{-1}$)', fontsize=12)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
######### Auxiliar functions #########
|
||||
|
||||
def get_enclosure_points(x_coordinates, y_coordinates):
|
||||
df = pd.DataFrame({'x': x_coordinates, 'y': y_coordinates})
|
||||
|
||||
points = df[['x', 'y']].values
|
||||
# get convex hull
|
||||
hull = ConvexHull(points)
|
||||
# get x and y coordinates
|
||||
# repeat last point to close the polygon
|
||||
x_hull = np.append(points[hull.vertices, 0],
|
||||
points[hull.vertices, 0][0])
|
||||
y_hull = np.append(points[hull.vertices, 1],
|
||||
points[hull.vertices, 1][0])
|
||||
return x_hull, y_hull
|
||||
|
||||
|
||||
def define_colormap(cns):
|
||||
min_val, max_val = 0.25, 0.85
|
||||
n = 10
|
||||
orig_cmap = plt.cm.Blues
|
||||
colors = orig_cmap(np.linspace(min_val, max_val, n))
|
||||
|
||||
norm = mpl.colors.Normalize(vmin=cns.min(), vmax=cns.max())
|
||||
cmap = mpl.cm.ScalarMappable(
|
||||
norm=norm, cmap=mpl.colors.LinearSegmentedColormap.from_list("mycmap", colors))
|
||||
cmap.set_array([])
|
||||
|
||||
return cmap
|
||||
|
||||
|
||||
def scatter_coleman_data(coleman_etal_vl_breathing, coleman_etal_er_breathing):
|
||||
plt.scatter(coleman_etal_vl_breathing,
|
||||
coleman_etal_er_breathing, marker='x', c='orange')
|
||||
x_hull, y_hull = get_enclosure_points(
|
||||
coleman_etal_vl_breathing, coleman_etal_er_breathing)
|
||||
# plot shape
|
||||
plt.fill(x_hull, y_hull, '--', c='orange', alpha=0.2)
|
||||
|
||||
|
||||
def scatter_milton_data(milton_vl, milton_er):
|
||||
try:
|
||||
for index, m in enumerate(markers):
|
||||
plt.scatter(milton_vl[index], milton_er[index],
|
||||
marker=m, color='red')
|
||||
x_hull, y_hull = get_enclosure_points(milton_vl, milton_er)
|
||||
# plot shape
|
||||
plt.fill(x_hull, y_hull, '--', c='red', alpha=0.2)
|
||||
except:
|
||||
print("No data for Milton et al")
|
||||
|
||||
|
||||
def scatter_yann_data(yann_vl, yann_er):
|
||||
try:
|
||||
plt.scatter(yann_vl[0], yann_er[0], marker=markers[0], color='green')
|
||||
plt.scatter(yann_vl[1], yann_er[1],
|
||||
marker=markers[1], color='green', s=50)
|
||||
plt.scatter(yann_vl[2], yann_er[2], marker=markers[2], color='green')
|
||||
|
||||
x_hull, y_hull = get_enclosure_points(yann_vl, yann_er)
|
||||
# plot shape
|
||||
plt.fill(x_hull, y_hull, '--', c='green', alpha=0.2)
|
||||
except:
|
||||
print("No data for Yan et al")
|
||||
|
||||
|
||||
def build_talking_legend(fig):
|
||||
result_from_model = mlines.Line2D(
|
||||
[], [], color='blue', marker='_', linestyle='None')
|
||||
coleman = mlines.Line2D([], [], color='orange',
|
||||
marker='x', linestyle='None')
|
||||
|
||||
title_proxy = Rectangle((0, 0), 0, 0, color='w')
|
||||
titles = ["$\\bf{CARA \, \\it{(SARS-CoV-2)}:}$",
|
||||
"$\\bf{Coleman \, et \, al. \, \\it{(SARS-CoV-2)}:}$"]
|
||||
leg = plt.legend([title_proxy, result_from_model, title_proxy, coleman],
|
||||
[titles[0], "Results from model", titles[1], "Dataset"])
|
||||
|
||||
# Move titles to the left
|
||||
for item, label in zip(leg.legendHandles, leg.texts):
|
||||
if label._text in titles:
|
||||
width = item.get_window_extent(fig.canvas.get_renderer()).width
|
||||
label.set_ha('left')
|
||||
label.set_position((-3*width, 0))
|
||||
|
||||
|
||||
def build_breathing_legend(fig):
|
||||
result_from_model = mlines.Line2D(
|
||||
[], [], color='blue', marker='_', linestyle='None')
|
||||
coleman = mlines.Line2D([], [], color='orange',
|
||||
marker='x', linestyle='None')
|
||||
milton_mean = mlines.Line2D(
|
||||
[], [], color='red', marker='d', linestyle='None') # mean
|
||||
milton_25 = mlines.Line2D(
|
||||
[], [], color='red', marker=5, linestyle='None') # 25
|
||||
milton_75 = mlines.Line2D(
|
||||
[], [], color='red', marker=4, linestyle='None') # 75
|
||||
yann_mean = mlines.Line2D([], [], color='green',
|
||||
marker='d', linestyle='None') # mean
|
||||
yann_25 = mlines.Line2D([], [], color='green',
|
||||
marker=5, linestyle='None') # 25
|
||||
yann_75 = mlines.Line2D([], [], color='green',
|
||||
marker=4, linestyle='None') # 75
|
||||
|
||||
title_proxy = Rectangle((0, 0), 0, 0, color='w')
|
||||
titles = ["$\\bf{CARA \, \\it{(SARS-CoV-2)}:}$", "$\\bf{Coleman \, et \, al. \, \\it{(SARS-CoV-2)}:}$",
|
||||
"$\\bf{Milton \, et \, al. \,\\it{(Influenza)}:}$", "$\\bf{Yann \, et \, al. \,\\it{(Influenza)}:}$"]
|
||||
leg = plt.legend([title_proxy, result_from_model, title_proxy, coleman, title_proxy, milton_mean, milton_25, milton_75, title_proxy, yann_mean, yann_25, yann_75],
|
||||
[titles[0], "Results from model", titles[1], "Dataset", titles[2], "Mean", "25th per.", "75th per.", titles[3], "Mean", "25th per.", "75th per."])
|
||||
|
||||
# Move titles to the left
|
||||
for item, label in zip(leg.legendHandles, leg.texts):
|
||||
if label._text in titles:
|
||||
width = item.get_window_extent(fig.canvas.get_renderer()).width
|
||||
label.set_ha('left')
|
||||
label.set_position((-3*width, 0))
|
||||
|
||||
|
||||
def print_er_info(er: np.array, log_er: np.array):
|
||||
"""
|
||||
Prints statistical parameters of a given distribution of ER-values
|
||||
"""
|
||||
print(f"MEAN of ER = {np.mean(er)}\n"
|
||||
f"MEAN of log ER = {np.mean(log_er)}\n"
|
||||
f"SD of ER = {np.std(er)}\n"
|
||||
f"SD of log ER = {np.std(log_er)}\n"
|
||||
f"Median of ER = {np.quantile(er, 0.5)}\n")
|
||||
|
||||
print(f"Percentiles of ER:")
|
||||
for quantile in (0.01, 0.05, 0.25, 0.50, 0.55, 0.65, 0.75, 0.95, 0.99):
|
||||
print(f"ER_{quantile} = {np.quantile(er, quantile)}")
|
||||
|
||||
return
|
||||
|
||||
|
||||
|
||||
|
|
@ -1,86 +0,0 @@
|
|||
from numpy.core.function_base import linspace
|
||||
from cara import models
|
||||
from cara.monte_carlo.data import activity_distributions
|
||||
from tqdm import tqdm
|
||||
import cara.monte_carlo as mc
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from scipy.spatial import ConvexHull
|
||||
import pandas as pd
|
||||
import matplotlib.lines as mlines
|
||||
from matplotlib.patches import Rectangle
|
||||
import matplotlib as mpl
|
||||
from model_scenarios_paper import *
|
||||
|
||||
# Used for testing
|
||||
|
||||
######### Plot material #########
|
||||
|
||||
SAMPLE_SIZE = 50000
|
||||
viral_loads = np.linspace(2, 12, 600)
|
||||
|
||||
er_means = []
|
||||
er_medians = []
|
||||
lower_percentiles = []
|
||||
upper_percentiles = []
|
||||
def exposure_model_from_vl_talking_new_points():
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(1, 1, 1)
|
||||
|
||||
for vl in tqdm(viral_loads):
|
||||
exposure_mc = talking_exposure_vl(vl)
|
||||
exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
|
||||
# divide by 4 to have in 15min (quarter of an hour)
|
||||
emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present(
|
||||
1.0)/4
|
||||
er_means.append((10**vl) / np.mean(emission_rate))
|
||||
er_medians.append(np.median(emission_rate))
|
||||
lower_percentiles.append(np.quantile(emission_rate, 0.01))
|
||||
upper_percentiles.append(np.quantile(emission_rate, 0.99))
|
||||
|
||||
# divide by 4 to have in 15min (quarter of an hour)
|
||||
coleman_etal_er_talking_2 = [x/4 for x in coleman_etal_er_talking]
|
||||
|
||||
ax.plot(viral_loads, er_means)
|
||||
ax.set_yscale('log')
|
||||
|
||||
new_datapoints = [
|
||||
10**(a) / b for a, b in zip(coleman_etal_vl_talking, coleman_etal_er_talking_2)]
|
||||
print(new_datapoints)
|
||||
|
||||
############# Coleman #############
|
||||
plt.scatter(coleman_etal_vl_talking, new_datapoints, marker='x')
|
||||
|
||||
############# Markers #############
|
||||
markers = [5, 'd', 4]
|
||||
|
||||
############ Legend ############
|
||||
result_from_model = mlines.Line2D(
|
||||
[], [], color='blue', marker='_', linestyle='None')
|
||||
coleman = mlines.Line2D([], [], color='orange',
|
||||
marker='x', linestyle='None')
|
||||
|
||||
title_proxy = Rectangle((0, 0), 0, 0, color='w')
|
||||
titles = ["$\\bf{CARA \, \\it{(SARS-CoV-2)}:}$",
|
||||
"$\\bf{Coleman \, et \, al. \, \\it{(SARS-CoV-2)}:}$"]
|
||||
leg = plt.legend([title_proxy, result_from_model, title_proxy, coleman],
|
||||
[titles[0], "Result from model", titles[1], "Dataset"])
|
||||
|
||||
# Move titles to the left
|
||||
for item, label in zip(leg.legendHandles, leg.texts):
|
||||
if label._text in titles:
|
||||
width = item.get_window_extent(fig.canvas.get_renderer()).width
|
||||
label.set_ha('left')
|
||||
label.set_position((-3*width, 0))
|
||||
|
||||
############ Plot ############
|
||||
plt.title('Exhaled virions while talking for 15min',
|
||||
fontsize=16, fontweight="bold")
|
||||
plt.ylabel(
|
||||
'Aerosol viral load, $\mathrm{vl_{out}}$\n(RNA copies)', fontsize=14)
|
||||
plt.xticks(ticks=[i for i in range(2, 13)], labels=[
|
||||
'$10^{' + str(i) + '}$' for i in range(2, 13)])
|
||||
plt.xlabel('NP viral load, $\mathrm{vl_{in}}$\n(RNA copies)', fontsize=14)
|
||||
plt.ylim([10**0, 10**10])
|
||||
plt.show()
|
||||
|
||||
Loading…
Reference in a new issue