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This commit is contained in:
Luis Aleixo 2022-02-23 10:39:20 +01:00
commit 98aba25bd0
3 changed files with 128 additions and 77 deletions
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67
cara/short_range_plots/model_scenarios.py
View file

@ -62,11 +62,11 @@ def exposure_module_without_short_range(activity: str, expiration: str, mask: st
room=models.Room(volume=100, humidity=0.5),
ventilation=models.AirChange(
active=models.SpecificInterval(((0, 24),)),
air_exch=0.25,
air_exch=500/100,
),
infected=mc.InfectedPopulation(
number=1,
virus=virus_distributions['SARS_CoV_2'],
virus=virus_distributions['SARS_CoV_2_OMICRON'],
presence=mc.SpecificInterval(((8.5, 12.5),(13.5, 17.5),)),
mask=exposure_mask,
activity=activity_distributions[activity],
@ -89,11 +89,7 @@ def exposure_module_without_short_range(activity: str, expiration: str, mask: st
)
return exposure_mc
def exposure_module_with_short_range(activity: str,
expiration: str,
mask: str,
sr_presence: list,
sr_activities: list):
def exposure_module_with_short_range(activity: str, expiration: str, mask: str, sr_presence: list, sr_activities: list):
if mask == 'No mask':
exposure_mask = models.Mask.types['No mask']
else:
@ -104,11 +100,11 @@ def exposure_module_with_short_range(activity: str,
room=models.Room(volume=100, humidity=0.5),
ventilation=models.AirChange(
active=models.SpecificInterval(((0, 24),)),
air_exch=0.25,
air_exch=500/100,
),
infected=mc.InfectedPopulation(
number=1,
virus=virus_distributions['SARS_CoV_2'],
virus=virus_distributions['SARS_CoV_2_OMICRON'],
presence=mc.SpecificInterval(((8.5, 12.5),(13.5, 17.5),)),
mask=exposure_mask,
activity=activity_distributions[activity],
@ -132,12 +128,7 @@ def exposure_module_with_short_range(activity: str,
)
return exposure_mc
def baseline_model(activity: str,
expiration: str,
mask: str,
sr_presence: list,
sr_activities: list):
def exposure_module_with_short_range_outdoors(activity: str, expiration: str, mask: str, sr_presence: list, sr_activities: list):
if mask == 'No mask':
exposure_mask = models.Mask.types['No mask']
else:
@ -145,7 +136,47 @@ def baseline_model(activity: str,
exposure_mc = mc.ExposureModel(
concentration_model=mc.ConcentrationModel(
room=models.Room(volume=10, humidity=0.5),
room=models.Room(volume=100000, humidity=0.5),
ventilation=models.AirChange(
active=models.SpecificInterval(((0, 24),)),
air_exch=1000000,
),
infected=mc.InfectedPopulation(
number=1,
virus=virus_distributions['SARS_CoV_2_OMICRON'],
presence=mc.SpecificInterval(((8.5, 12.5),)),
mask=exposure_mask,
activity=activity_distributions[activity],
expiration=build_expiration(expiration),
host_immunity=0.,
),
),
short_range=mc.ShortRangeModel(
presence=[models.SpecificInterval(interval) for interval in sr_presence],
expirations=[short_range_expiration_distributions[activity] for activity in sr_activities],
dilutions=dilution_factor(activities=sr_activities,
distance=np.random.uniform(0.5, 1.5, 250000)),
),
exposed=mc.Population(
number=14,
presence=mc.SpecificInterval(((8.5, 12.5),)),
activity=activity_distributions[activity],
mask=exposure_mask,
host_immunity=0.,
),
)
return exposure_mc
def baseline_model(activity: str, expiration: str, mask: str, sr_presence: list, sr_activities: list):
if mask == 'No mask':
exposure_mask = models.Mask.types['No mask']
else:
exposure_mask = mask_distributions[mask]
exposure_mc = mc.ExposureModel(
concentration_model=mc.ConcentrationModel(
room=models.Room(volume=100, humidity=0.5),
ventilation=models.AirChange(
active=models.PeriodicInterval(period=120, duration=120),
air_exch=0.25,
@ -153,7 +184,7 @@ def baseline_model(activity: str,
infected=mc.InfectedPopulation(
number=1,
virus=virus_distributions['SARS_CoV_2_OMICRON'],
presence=models.SpecificInterval(((8.5, 12.5),)),
presence=models.SpecificInterval(((8.5, 9.5),)),
mask=exposure_mask,
activity=activity_distributions[activity],
expiration=build_expiration(expiration),
@ -168,7 +199,7 @@ def baseline_model(activity: str,
),
exposed=mc.Population(
number=3,
presence=models.SpecificInterval(((8.5, 12.5),)),
presence=models.SpecificInterval(((8.5, 9.5),)),
activity=activity_distributions[activity],
mask=exposure_mask,
host_immunity=0.,

96
cara/short_range_plots/results.py
View file

@ -13,48 +13,70 @@ from dataclasses import dataclass
from cara.monte_carlo.data import symptomatic_vl_frequencies
# print('\n<<<<<<<<<<< Peak viral concentration without short range for baseline scenarios >>>>>>>>>>>')
# concentration_curve(models = [exposure_module_without_short_range(activity='Seated', expiration='Breathing', mask='No mask')],
# labels = ['Baseline'],
# concentration_curve(models = [exposure_module_without_short_range(activity='Light activity', expiration={"Speaking": 1, "Breathing": 2}, mask='No mask')],
# labels = ['Background (long-range) concentration'],
# labelsDose = ['Dose (long-range)'],
# colors = ['royalblue'],
# )
# linestyles = ['--'],
# thickness = [2])
# print('\n<<<<<<<<<<< Peak viral concentration with short range interactions for baseline scenarios >>>>>>>>>>>')
# concentration_curve(models=[exposure_module_with_short_range(
# activity='Seated',
# expiration={"Speaking": 1, "Breathing": 2},
# activity='Light activity',
# expiration={"Speaking": 1, "Breathing": 2},
# mask='No mask',
# sr_presence=[(10.5, 11.0), (15.5, 16.0)],
# sr_activities=['Breathing', 'Shouting']),
# sr_presence=[(10.5, 11.0)],
# sr_activities=['Breathing']),
# exposure_module_without_short_range(
# activity='Seated',
# expiration={"Speaking": 1, "Breathing": 2},
# mask='No mask',
# )],
# labels = ['Mean concentration with short range interactions', 'Mean concentration without short range interactions'],
# colors = ['royalblue', 'darkviolet'],
# linestyles = ['-', '-'],
# thickness = [2, 2.5])
# activity='Light activity',
# expiration={"Speaking": 1, "Breathing": 2},
# mask='No mask',)
# ],
# labels = ['Concentration with short range interactions', 'Background (long-range) concentration'],
# labelsDose = ['Dose (full)', 'Dose (long-range)'],
# colors = ['salmon', 'royalblue'],
# linestyles = ['-', '--'],
# thickness = [2, 2])
# print('\n<<<<<<<<<<< Probability of infections vs exposure time >>>>>>>>>>>')
# Always assume 1h for the short range interactions.
# Always assume that in each model there is only ONE short range interaction.
# plot_pi_vs_exposure_time(exp_models = [
# baseline_model(
# activity='Seated',
# expiration={"Speaking": 2, "Breathing": 1},
# mask='No mask',
# sr_presence=[(10.0, 11.0)],
# sr_activities=['Breathing']),
# baseline_model(
# activity='Seated',
# expiration={"Shouting": 2, "Breathing": 1},
# mask='No mask',
# sr_presence=[(10.0, 11.0)],
# sr_activities=['Shouting'])],
# labels = ['Baseline model breathing', 'Baseline model shouting'],
# colors=['royalblue', 'darkviolet'],
# linestyles=['solid', 'solid'],
# points=20,
# time_in_minutes=True,
# normalize_y_axis=True)
# print('\n<<<<<<<<<<< Peak viral concentration with short range interactions for baseline scenarios >>>>>>>>>>>')
# concentration_curve(models=[exposure_module_with_short_range(
# activity='Light activity',
# expiration={"Speaking": 1, "Breathing": 2},
# mask='No mask',
# sr_presence=[(10.5, 11.0), (15.0, 16.0)],
# sr_activities=['Breathing', 'Speaking']),
# exposure_module_without_short_range(
# activity='Light activity',
# expiration={"Speaking": 1, "Breathing": 2},
# mask='No mask',)
# ],
# labels = ['Concentration with short range interactions', 'Background (long-range) concentration'],
# labelsDose = ['Dose (full)', 'Dose (long-range)'],
# colors = ['salmon', 'royalblue'],
# linestyles = ['-', '--'],
# thickness = [2, 2])
print('\n<<<<<<<<<<< Dose vs SR exposure time >>>>>>>>>>>')
#Always assume 1h for the short range interactions.
#Always assume that in each model there is only ONE short range interaction.
plot_vD_vs_exposure_time(exp_models = [
baseline_model(
activity='Light activity',
expiration={"Speaking": 2, "Breathing": 1},
mask='No mask',
sr_presence=[(8.5, 9.5)],
sr_activities=['Breathing']),
baseline_model(
activity='Light activity',
expiration={"Speaking": 2, "Breathing": 1},
mask='No mask',
sr_presence=[(8.5, 9.5)],
sr_activities=['Speaking'])],
labels = ['Breathing', 'Speaking'],
colors=['royalblue', 'darkviolet'],
linestyles=['solid', 'solid'],
points=20,
time_in_minutes=True,
normalize_y_axis=True)

42
cara/short_range_plots/scripts.py
View file

@ -23,7 +23,7 @@ from mpl_toolkits.axes_grid1.inset_locator import mark_inset
np.random.seed(2000)
SAMPLE_SIZE = 250000
TIMESTEP = 0.01
viral_loads = np.linspace(2, 12, 600)
#viral_loads = np.linspace(2, 12, 600)
_VectorisedFloat = typing.Union[float, np.ndarray]
@ -59,7 +59,7 @@ def previous_deposited_exposure_between_bounds(model: ExposureModel, time1: floa
(1 - model.exposed.mask.inhale_efficiency()))
def concentration_curve(models, labels, colors, linestyles, thickness):
def concentration_curve(models, labels, labelsDose, colors, linestyles, thickness):
exp_models = [model.build_model(size=SAMPLE_SIZE) for model in models]
@ -73,12 +73,13 @@ def concentration_curve(models, labels, colors, linestyles, thickness):
concentrations = [[np.mean(model.concentration(
t)) for t in times] for model in tqdm(exp_models)]
fig, ax = plt.subplots()
fig, ax = plt.subplots(figsize=(8,6))
for c, color, linestyle, width in zip(concentrations, colors, linestyles, thickness):
ax.plot(times, c, color=color, ls=linestyle, lw=width)
ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1] * 1.2)
#ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1] * 1.2)
ax.set_ylim(ax.get_ylim()[0], 90)
ax.spines["right"].set_visible(False)
cumulative_doses = [np.cumsum([
@ -129,10 +130,11 @@ def concentration_curve(models, labels, colors, linestyles, thickness):
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] * 1.3)
ax1.set_ylabel('Mean cumulative dose\n(infectious virus)', fontsize=14)
#ax1.set_ylim(ax1.get_ylim()[0], ax1.get_ylim()[1] * 1.3)
ax1.set_ylim(ax1.get_ylim()[0], 40)
complete_labels = labels + ['vD - ' + label for label in labels]
complete_labels = labels + [label for label in labelsDose]
complete_colors = colors + [color for color in colors]
complete_linestyles = linestyles + ['dotted' for linestyle in linestyles]
@ -146,21 +148,17 @@ def concentration_curve(models, labels, colors, linestyles, thickness):
f"5th per = {quantile_05[i][-1]}\n"
f"95th per = {quantile_95[i][-1]}\n")
plt.legend(handles=labels_legend, loc='upper right', bbox_to_anchor=(1, 1))
plt.legend(handles=labels_legend, loc='upper left')
plt.show()
def plot_pi_vs_exposure_time(exp_models: typing.List[mc.ExposureModel],
labels,
colors,
linestyles,
points: int = 20, time_in_minutes: bool = False, normalize_y_axis: bool = False) -> None:
def plot_vD_vs_exposure_time(exp_models: typing.List[mc.ExposureModel], labels, colors, linestyles, points: int = 20, time_in_minutes: bool = False, normalize_y_axis: bool = False) -> None:
TIMESTEP = 0.001
concentration_models = [model.concentration_model for model in exp_models]
exposed_models = [model.exposed for model in exp_models]
pis: typing.List[typing.List[float]] = [[] for _ in exp_models]
vDs: typing.List[typing.List[float]] = [[] for _ in exp_models]
presence_intervals = [model.short_range.presence[0].boundaries() for model in exp_models]
start, final = presence_intervals[0]
@ -177,20 +175,20 @@ def plot_pi_vs_exposure_time(exp_models: typing.List[mc.ExposureModel],
) for cm, exposed, em in zip(concentration_models, exposed_models, exp_models)]
for i, m in enumerate(current_models):
pis[i].append(np.mean(m.build_model(SAMPLE_SIZE).infection_probability() / 100))
vDs[i].append(np.mean(m.build_model(SAMPLE_SIZE).deposited_exposure()))
times = np.linspace(0, 60, points)
for i, pi in enumerate(pis):
plt.plot(times, pi, color=colors[i], label=labels[i])
for i, vD in enumerate(vDs):
plt.plot(times, vD, color=colors[i], label=labels[i])
# plt.xlim((0, 60))
if normalize_y_axis:
plt.ylim((0, 1))
#if normalize_y_axis:
# plt.ylim((0, 1))
for m in exp_models:
print(np.mean(m.build_model(SAMPLE_SIZE).infection_probability() / 100))
print(np.mean(m.build_model(SAMPLE_SIZE).deposited_exposure()))
plt.xlabel(f'Exposure time (m)', fontsize=12)
plt.ylabel('Probability of infection\n$P(\,\mathtt{I}\,)$', fontsize=12)
plt.xlabel(f'Duration of close-proximity encounter (min)', fontsize=12)
plt.ylabel('Mean cumulative dose\n(infectious virus)', fontsize=12)
plt.legend()
plt.show()