diff --git a/cara/model_scenarios_paper.py b/cara/model_scenarios_paper.py new file mode 100644 index 00000000..89d65fe2 --- /dev/null +++ b/cara/model_scenarios_paper.py @@ -0,0 +1,748 @@ +from cara import models +from cara.monte_carlo.data import activity_distributions, symptomatic_vl_frequencies, viable_to_RNA_ratio_distribution, infectious_dose_distribution +import cara.monte_carlo as mc +import numpy as np + +######### Scatter points (data taken: copies per hour) ######### + +############# Coleman ############# +############# Coleman - Breathing ############# +coleman_etal_vl_breathing = [np.log10(821065925.4), np.log10(1382131207), np.log10(81801735.96), np.log10( + 487760677.4), np.log10(2326593535), np.log10(1488879159), np.log10(884480386.5)] +coleman_etal_er_breathing = [127, 455.2, 281.8, 884.2, 448.4, 1100.6, 621] +############# Coleman - Talking ############# +coleman_etal_vl_talking = [np.log10(70492378.55), np.log10(7565486.029), np.log10(7101877592), np.log10(1382131207), + np.log10(821065925.4), np.log10(1382131207), np.log10( + 81801735.96), np.log10(487760677.4), + np.log10(2326593535), np.log10(1488879159), np.log10(884480386.5)] +coleman_etal_er_talking = [1668, 938, 319.6, 3632.8, 1243.6, + 17344, 2932, 5426, 5493.2, 1911.6, 9714.8] + +############# Milton et al ############# +milton_vl = [np.log10(8.30E+04), np.log10(4.20E+05), np.log10(1.80E+06)] +milton_er = [22, 220, 1120] +############# Milton et al ############# + +yann_vl = [np.log10(7.86E+07), np.log10(2.23E+09), np.log10(1.51E+10)] +yann_er = [8396.78166, 45324.55964, 400054.0827] + +######### 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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types[mask], + activity=activity_distributions[activity], + expiration=models.Expiration.types['Breathing'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types[activity], + mask=models.Mask.types[mask], + host_immunity=0., + ), + ) + 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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types[mask], + activity=activity_distributions[activity], + expiration=models.Expiration.types['Talking'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types[activity], + mask=models.Mask.types[mask], + host_immunity=0., + ), + ) + return exposure_mc + +######### Shouting model ########### +def shouting_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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types[mask], + activity=activity_distributions[activity], + expiration=models.Expiration.types['Shouting'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types[activity], + mask=models.Mask.types[mask], + host_immunity=0., + ), + ) + return exposure_mc + +######### Breathing model for specific viral load ########### +def breathing_exposure_vl(vl): + 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=models.Virus( + viral_load_in_sputum=10**vl, + infectious_dose=infectious_dose_distribution, + viable_to_RNA_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types["No mask"], + activity=activity_distributions['Seated'], + expiration=models.Expiration.types['Breathing'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Seated'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + return exposure_mc + +######### Talking model for specific viral load ########### +def talking_exposure_vl(vl): + 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=models.Virus( + viral_load_in_sputum=10**vl, + infectious_dose=infectious_dose_distribution, + viable_to_RNA_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types["No mask"], + activity=activity_distributions['Seated'], + expiration=models.Expiration.types['Talking'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Seated'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + return exposure_mc + +######### Shouting model for specific viral load ########### +def shouting_exposure_vl(vl): + 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=10**vl, + infectious_dose=infectious_dose_distribution, + viable_to_RNA_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types['No mask'], + activity=activity_distributions['Light activity'], + expiration=models.Expiration.types['Shouting'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Light activity'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + return exposure_mc + +######### Used for CDF Models ########### +######### Breathing Models ######### +def breathing_seated_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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types["No mask"], + activity=activity_distributions['Seated'], + expiration=models.Expiration.types['Breathing'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Seated'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + return exposure_mc + +def breathing_light_activity_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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types["No mask"], + activity=activity_distributions['Light activity'], + expiration=models.Expiration.types['Breathing'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Light activity'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + return exposure_mc + +def breathing_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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types["No mask"], + activity=activity_distributions['Heavy exercise'], + expiration=models.Expiration.types['Breathing'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Heavy exercise'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + return exposure_mc + +######### Speaking Models ######### +def speaking_seated_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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types["No mask"], + activity=activity_distributions['Seated'], + expiration=models.Expiration.types['Talking'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Seated'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + return exposure_mc + +def speaking_light_activity_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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types["No mask"], + activity=activity_distributions['Light activity'], + expiration=models.Expiration.types['Talking'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Light activity'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + return exposure_mc + +def speaking_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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types["No mask"], + activity=activity_distributions['Heavy exercise'], + expiration=models.Expiration.types['Talking'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Heavy exercise'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + return exposure_mc + +######### Shouting Models ######### +def shouting_seated_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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types["No mask"], + activity=activity_distributions['Seated'], + expiration=models.Expiration.types['Shouting'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Seated'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + return exposure_mc + +def shouting_light_activity_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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types["No mask"], + activity=activity_distributions['Light activity'], + expiration=models.Expiration.types['Shouting'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Light activity'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + 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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types["No mask"], + activity=activity_distributions['Heavy exercise'], + expiration=models.Expiration.types['Shouting'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types['Heavy exercise'], + mask=models.Mask.types["No mask"], + host_immunity=0., + ), + ) + 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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + 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) + ), + host_immunity=0., + ) + ), + 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'], + host_immunity=0., + ) + ) + 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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + 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) + ), + host_immunity=0., + ) + ), + 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'], + host_immunity=0., + ) + ) + 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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + 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) + ), + host_immunity=0., + ) + ), + 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'], + host_immunity=0., + ) + ) + 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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types[mask], + activity=activity_distributions[activity], + expiration=models.Expiration.types['Breathing'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types[activity], + mask=models.Mask.types[mask], + host_immunity=0., + ), + ) + 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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types[mask], + activity=activity_distributions[activity], + expiration=models.Expiration.types['Talking'], + host_immunity=0., + ), + ), + exposed=mc.Population( + number=14, + presence=mc.SpecificInterval(((0, 2),)), + activity=models.Activity.types[activity], + mask=models.Mask.types[mask], + host_immunity=0., + ), + ) + 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_ratio=viable_to_RNA_ratio_distribution, + transmissibility_factor=1., + ), + presence=mc.SpecificInterval(((0, 2),)), + mask=models.Mask.types[mask], + activity=activity_distributions[activity], + expiration=models.Expiration.types[expiratory_activity]) + + return infected diff --git a/cara/plot_output.py b/cara/plot_output.py new file mode 100644 index 00000000..c63f27da --- /dev/null +++ b/cara/plot_output.py @@ -0,0 +1,54 @@ +""" Title: COVID Airborne Risk Assessment +Author: +Date: +Code version: +Availability: """ + +from cara.models import ExposureModel, InfectedPopulation +from cara import model_scenarios_paper +from cara.results_paper 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 ER-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() diff --git a/cara/results_paper.py b/cara/results_paper.py new file mode 100644 index 00000000..54c74bb3 --- /dev/null +++ b/cara/results_paper.py @@ -0,0 +1,804 @@ +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() / 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() + 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()/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() + 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() + 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() + 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() + 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() + 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()) 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 = 250000 + 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, 0.5), (0, 0, 0.5), (0.5, 0, 0)] + colors_violin=['lightsteelblue', 'wheat', 'darkseagreen'] + colors_violin_lines = ['royalblue', 'orange', 'forestgreen'] + + # 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()) for pop in infected_populations] + + fig, ax = plt.subplots() + ax.set_xlim((0, 11)) + + 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_violin_lines, + alpha=0.8) + + + for pc, color, bc in zip(parts['bodies'], colors_violin, border_colors): + pc.set_facecolor(color) + pc.set_edgecolor(bc) + pc.set_alpha(0.5) + parts['cquantiles'].set_color([c for c in colors_violin_lines[:3] for _ in range(2)]) + parts['cquantiles'].set_alpha(1) + + positions=np.linspace(4.5, 11.5, 20) + + ######### SARS-CoV-2 ######### + lower_bound = [418, 216, 216, 518, 648, 878, 893, 1670, 1872, 1915, 2002, 2002, 2189, 3341, 9835, 13968, 60667] + higher_bound = [4176, 2160, 2160, 5184, 6480, 8784, 8928, 16704, 18720, 19152, 20016, 20016, 21888, 33408, 98352, 139680, 606672] + + 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 = [259, 8640, 39816, 124416] + higher_bound = [2592, 86400, 398160, 1244160] + + 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+5]], medianprops=dict(color=colors[1]+ (0.5,)), whiskerprops=dict(color=colors[1]+ (0.5,)), boxprops=dict(color=colors[1]+ (0.5,))) + + ######### Influenza ######### + lower_bound = [2, 114, 1138] + higher_bound = [16, 1145, 11376] + + 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+12]], medianprops=dict(color=colors[2]+ (0.5,)), whiskerprops=dict(color=colors[2]+ (0.5,)), boxprops=dict(color=colors[2]+ (0.5,))) + + ######### Rhinovirus ######### + lower_bound = [45] + higher_bound = [446] + + 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+8]], 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(edgecolor=c, facecolor='none', label=l) for c, l in zip([p + (0.5,) for p in [(0., 0.5, 0.5), (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') + + ax.annotate("Bus ride", xy=(6, np.log10(5500)), color='k', fontsize=8, + xycoords='data', + xytext=(-50, 50), textcoords='offset points', + arrowprops=dict(arrowstyle="->", + connectionstyle="arc3,rad=-0.2", color='lightgrey')) + + ax.annotate("S V Chorale", xy=(10, np.log10(110000)), color='k', fontsize=8, + xycoords='data', + xytext=(-50, 40), textcoords='offset points', + arrowprops=dict(arrowstyle="->", + connectionstyle="arc3,rad=-0.2", color='lightgrey')) + + handles = [patches.Patch(color=c, label=l) for c, l in zip([p for p in colors_violin], ('Breathing', 'Speaking', 'Shouting'))] + plt.legend(handles=handles, loc='lower left', bbox_to_anchor=(0.12, 0.)) + plt.gca().add_artist(boxplot_legend) + + ax.hlines(y=[-2, 0, 2, 4, 6], xmin=ax.get_xlim()[0], xmax=ax.get_xlim()[1], colors=(0.8, 0.8, 0.8), linestyles='--', alpha=0.3) + ax.vlines(x=4, 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, 7]) + ax.set_xticklabels(['SARS-CoV-2\n(model)', 'Literature Data\n(recorded outbreaks) ']) + 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 + + +