added methods to different scenarios
This commit is contained in:
Luis Aleixo
parent
4d95dd2ced
commit
cbc5ffb203
2 changed files with 271 additions and 188 deletions
267
cara/model_scenarios.py
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267
cara/model_scenarios.py
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from cara import models
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from cara.monte_carlo.data import activity_distributions
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from tqdm import tqdm
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import cara.monte_carlo as mc
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.spatial import ConvexHull
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import pandas as pd
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import matplotlib.lines as mlines
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from matplotlib.patches import Rectangle
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######### Plot material #########
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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SAMPLE_SIZE = 50000
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er_means = []
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er_medians = []
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lower_percentiles = []
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upper_percentiles = []
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######### Scatter points #########
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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), np.log10(821065925.4),
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np.log10(1382131207), np.log10(81801735.96), np.log10(
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487760677.4), np.log10(2326593535), np.log10(1488879159),
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np.log10(884480386.5)]
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coleman_etal_er_talking = [417, 234.5, 79.9, 908.2, 310.9,
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4336, 733, 1356.5, 1373.3, 477.9, 2428.7]
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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] # removed first and last due to its dimensions
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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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def exposure_model_from_vl_talking(viral_loads):
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for vl in tqdm(viral_loads):
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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=50.,
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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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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present()
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er_means.append(np.mean(emission_rate))
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er_medians.append(np.median(emission_rate))
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lower_percentiles.append(np.quantile(emission_rate, 0.01))
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upper_percentiles.append(np.quantile(emission_rate, 0.99))
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build_plot(viral_loads, er_means,
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lower_percentiles, upper_percentiles, coleman_etal_vl_talking, coleman_etal_er_talking, milton_vl, milton_er, yann_vl, yann_er)
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############ Plot ############
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plt.title('Exhaled virions while talking for 1h',
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fontsize=16, fontweight="bold")
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plt.ylabel(
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'Aerosol viral load, $\mathrm{vl_{out}}$\n(RNA copies)', fontsize=14)
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plt.xticks(ticks=[i for i in range(2, 13)], labels=[
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'$10^{' + str(i) + '}$' for i in range(2, 13)])
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plt.xlabel('NP viral load, $\mathrm{vl_{in}}$\n(RNA copies)', fontsize=14)
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plt.show()
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return er_means
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def exposure_model_from_vl_breathing(viral_loads):
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for vl in tqdm(viral_loads):
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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=50.,
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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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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present()
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er_means.append(np.mean(emission_rate))
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er_medians.append(np.median(emission_rate))
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lower_percentiles.append(np.quantile(emission_rate, 0.01))
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upper_percentiles.append(np.quantile(emission_rate, 0.99))
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build_plot(viral_loads, er_means,
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lower_percentiles, upper_percentiles, coleman_etal_vl_breathing, coleman_etal_er_breathing, milton_vl, milton_er, yann_vl, yann_er)
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############ Plot ############
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plt.title('Exhaled virions while breathing for 1h',
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fontsize=16, fontweight="bold")
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plt.ylabel(
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'Aerosol viral load, $\mathrm{vl_{out}}$\n(RNA copies)', fontsize=14)
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plt.xticks(ticks=[i for i in range(2, 13)], labels=[
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'$10^{' + str(i) + '}$' for i in range(2, 13)])
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plt.xlabel('NP viral load, $\mathrm{vl_{in}}$\n(RNA copies)', fontsize=14)
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plt.show()
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return er_means
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######### Auxiliar functions #########
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def get_enclosure_points(x_coordinates, y_coordinates):
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df = pd.DataFrame({'x': x_coordinates, 'y': y_coordinates})
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points = df[['x', 'y']].values
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# get convex hull
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hull = ConvexHull(points)
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# get x and y coordinates
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# repeat last point to close the polygon
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x_hull = np.append(points[hull.vertices, 0],
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points[hull.vertices, 0][0])
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y_hull = np.append(points[hull.vertices, 1],
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points[hull.vertices, 1][0])
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return x_hull, y_hull
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def build_legend(fig):
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result_from_model = mlines.Line2D(
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[], [], color='blue', marker='_', linestyle='None')
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coleman = mlines.Line2D([], [], color='orange',
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marker='x', linestyle='None')
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milton_mean = mlines.Line2D(
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[], [], color='red', marker='d', linestyle='None') # mean
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milton_25 = mlines.Line2D(
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[], [], color='red', marker=5, linestyle='None') # 25
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milton_75 = mlines.Line2D(
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[], [], color='red', marker=4, linestyle='None') # 75
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yann_mean = mlines.Line2D([], [], color='green',
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marker='d', linestyle='None') # mean
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yann_25 = mlines.Line2D([], [], color='green',
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marker=5, linestyle='None') # 25
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yann_75 = mlines.Line2D([], [], color='green',
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marker=4, linestyle='None') # 75
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title_proxy = Rectangle((0, 0), 0, 0, color='w')
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titles = ["$\\bf{CARA \, \\it{(SARS-CoV-2)}:}$", "$\\bf{Coleman \, et \, al. \, \\it{(SARS-CoV-2)}:}$",
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"$\\bf{Milton \, et \, al. \,\\it{(Influenza)}:}$", "$\\bf{Yann \, et \, al. \,\\it{(Influenza)}:}$"]
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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],
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[titles[0], "Result from model", titles[1], "Dataset", titles[2], "Mean", "25th per.", "75th per.", titles[3], "Mean", "25th per.", "75th per."])
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# Move titles to the left
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for item, label in zip(leg.legendHandles, leg.texts):
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if label._text in titles:
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width = item.get_window_extent(fig.canvas.get_renderer()).width
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label.set_ha('left')
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label.set_position((-3*width, 0))
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def build_plot(viral_loads, er_means, lower_percentiles, upper_percentiles, coleman_etal_vl, coleman_etal_er, milton_vl, milton_er, yann_vl, yann_er, ):
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ax.plot(viral_loads, er_means)
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ax.fill_between(viral_loads, lower_percentiles,
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upper_percentiles, alpha=0.2)
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ax.set_yscale('log')
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############# Coleman #############
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plt.scatter(coleman_etal_vl, coleman_etal_er, marker='x')
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x_hull, y_hull = get_enclosure_points(
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coleman_etal_vl, coleman_etal_er)
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# plot shape
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plt.fill(x_hull, y_hull, '--', c='orange', alpha=0.2)
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############# Markers #############
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markers = [5, 'd', 4]
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############# Milton et al #############
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for index, m in enumerate(markers):
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plt.scatter(milton_vl[index], milton_er[index],
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marker=m, color='red')
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x_hull, y_hull = get_enclosure_points(milton_vl, milton_er)
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# plot shape
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plt.fill(x_hull, y_hull, '--', c='red', alpha=0.2)
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############# Yan et al #############
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plt.scatter(yann_vl[0], yann_er[0], marker=markers[0], color='green')
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plt.scatter(yann_vl[1], yann_er[1], marker=markers[1], color='green', s=50)
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plt.scatter(yann_vl[2], yann_er[2], marker=markers[2], color='green')
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x_hull, y_hull = get_enclosure_points(yann_vl, yann_er)
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# plot shape
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plt.fill(x_hull, y_hull, '--', c='green', alpha=0.2)
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############ Legend ############
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build_legend(fig)
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return plt
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# # Milton
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# boxes = [
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# {
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# 'label': "Milton data",
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# 'whislo': 0, # Bottom whisker position
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# 'q1': 22, # First quartile (25th percentile)
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# 'med': 220, # Median (50th percentile)
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# 'q3': 1120, # Third quartile (75th percentile)
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# 'whishi': 260000, # Top whisker position
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# 'fliers': [] # Outliers
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# }
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# ]
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# # `box plot aligned with the viral load value of 5.62325
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# ax.bxp(boxes, showfliers=False, positions=[5.62324929])
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# # Yan
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# boxes = [
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# {
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# 'whislo': 1424.81, # Bottom whisker position
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# 'q1': 8396.78, # First quartile (25th percentile)
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# 'med': 45324.6, # Median (50th percentile)
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# 'q3': 400054, # Third quartile (75th percentile)
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# 'whishi': 88616200, # Top whisker position
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# 'fliers': [] # Outliers
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# }
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# ]
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# ax.bxp(boxes, showfliers=False, positions=[9.34786])
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# box plot aligned with the viral load value of 9.34786
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@ -1,80 +1,11 @@
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from dataclasses import field
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from cara.model_scenarios import exposure_model_from_vl_breathing, exposure_model_from_vl_talking
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.lines as mlines
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from matplotlib.patches import Rectangle
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import pandas as pd
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import csv
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import cara.monte_carlo as mc
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from cara import models
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from cara.monte_carlo.data import activity_distributions
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from tqdm import tqdm
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from scipy.spatial import ConvexHull
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viral_loads = np.linspace(2, 12, 600)
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def get_enclosure_points(x_coordinates, y_coordinates):
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df = pd.DataFrame({'x': x_coordinates, 'y': y_coordinates})
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points = df[['x', 'y']].values
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# get convex hull
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hull = ConvexHull(points)
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# get x and y coordinates
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# repeat last point to close the polygon
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x_hull = np.append(points[hull.vertices, 0],
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points[hull.vertices, 0][0])
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y_hull = np.append(points[hull.vertices, 1],
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points[hull.vertices, 1][0])
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return x_hull, y_hull
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SAMPLE_SIZE = 50000
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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points = 600
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viral_loads = np.linspace(2, 12, points)
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er_means = []
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er_medians = []
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lower_percentiles = []
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upper_percentiles = []
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for vl in tqdm(viral_loads):
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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=50.,
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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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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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exposure_model = exposure_mc.build_model(size=SAMPLE_SIZE)
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emission_rate = exposure_model.concentration_model.infected.emission_rate_when_present()
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er_means.append(np.mean(emission_rate))
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er_medians.append(np.median(emission_rate))
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lower_percentiles.append(np.quantile(emission_rate, 0.01))
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upper_percentiles.append(np.quantile(emission_rate, 0.99))
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#er_means = exposure_model_from_vl_talking(viral_loads)
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er_means = exposure_model_from_vl_breathing(viral_loads)
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with open('data.csv', 'w', newline='') as csvfile:
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fieldnames = ['viral load', 'emission rate']
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@ -83,118 +14,3 @@ with open('data.csv', 'w', newline='') as csvfile:
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for i, vl in enumerate(viral_loads):
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thewriter.writerow(
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{'viral load': 10**vl, 'emission rate': er_means[i]})
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ax.plot(viral_loads, er_means)
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ax.fill_between(viral_loads, lower_percentiles,
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upper_percentiles, alpha=0.2)
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ax.set_yscale('log')
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############# Coleman #############
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#Breathing
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coleman_etal_vl = [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 = [127, 455.2, 281.8, 884.2, 448.4, 1100.6, 621]
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#Talking
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coleman_etal_vl = [np.log10(70492378.55), np.log10(7565486.029), np.log10(7101877592), np.log10(1382131207), np.log10(821065925.4),
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np.log10(1382131207), np.log10(81801735.96), np.log10(487760677.4), np.log10(2326593535), np.log10(1488879159),
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np.log10(884480386.5)]
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coleman_etal_er = [417, 234.5, 79.9, 908.2, 310.9, 4336, 733, 1356.5, 1373.3, 477.9, 2428.7]
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plt.scatter(coleman_etal_vl, coleman_etal_er, marker='x')
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x_hull, y_hull = get_enclosure_points(coleman_etal_vl, coleman_etal_er)
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# plot shape
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plt.fill(x_hull, y_hull, '--', c='orange', alpha=0.2)
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############# Markers #############
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markers = [5, 'd', 4]
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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] # removed first and last due to its dimensions
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plt.scatter(milton_vl[0], milton_er[0], marker=markers[0], color='red')
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plt.scatter(milton_vl[1], milton_er[1], marker=markers[1], color='red', s=50)
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||||
plt.scatter(milton_vl[2], milton_er[2], marker=markers[2], 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)
|
||||
|
||||
############# Yan et al #############
|
||||
# removed first and last due to its dimensions
|
||||
yan_vl = [np.log10(7.86E+07), np.log10(2.23E+09), np.log10(1.51E+10)]
|
||||
yan_er = [8396.78166, 45324.55964, 400054.0827]
|
||||
plt.scatter(yan_vl[0], yan_er[0], marker=markers[0], color='green')
|
||||
plt.scatter(yan_vl[1], yan_er[1], marker=markers[1], color='green', s=50)
|
||||
plt.scatter(yan_vl[2], yan_er[2], marker=markers[2], color='green')
|
||||
|
||||
x_hull, y_hull = get_enclosure_points(yan_vl, yan_er)
|
||||
# plot shape
|
||||
plt.fill(x_hull, y_hull, '--', c='green', alpha=0.2)
|
||||
|
||||
# # Milton
|
||||
# boxes = [
|
||||
# {
|
||||
# 'label': "Milton data",
|
||||
# 'whislo': 0, # Bottom whisker position
|
||||
# 'q1': 22, # First quartile (25th percentile)
|
||||
# 'med': 220, # Median (50th percentile)
|
||||
# 'q3': 1120, # Third quartile (75th percentile)
|
||||
# 'whishi': 260000, # Top whisker position
|
||||
# 'fliers': [] # Outliers
|
||||
# }
|
||||
# ]
|
||||
# # `box plot aligned with the viral load value of 5.62325
|
||||
# ax.bxp(boxes, showfliers=False, positions=[5.62324929])
|
||||
|
||||
# # Yan
|
||||
|
||||
# boxes = [
|
||||
# {
|
||||
# 'whislo': 1424.81, # Bottom whisker position
|
||||
# 'q1': 8396.78, # First quartile (25th percentile)
|
||||
# 'med': 45324.6, # Median (50th percentile)
|
||||
# 'q3': 400054, # Third quartile (75th percentile)
|
||||
# 'whishi': 88616200, # Top whisker position
|
||||
# 'fliers': [] # Outliers
|
||||
# }
|
||||
# ]
|
||||
# ax.bxp(boxes, showfliers=False, positions=[9.34786])
|
||||
# box plot aligned with the viral load value of 9.34786
|
||||
|
||||
############ Legend ############
|
||||
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], "Result 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))
|
||||
|
||||
|
||||
############ Plot ############
|
||||
plt.title('Exhaled virions while breathing for 1h', 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()
|
||||
|
|
|
|||
Loading…
Reference in a new issue