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added corrected models for the concentrations plot

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Luis Aleixo 2021-09-16 12:12:52 +02:00
parent 42ed330ad4
commit 0c992e459f
4 changed files with 105 additions and 71 deletions
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1
cara/apps/calculator/model_generator.py
View file

@ -247,7 +247,6 @@ class FormData:
infected=self.infected_population(),
),
exposed=self.exposed_population(),
fraction_deposited=self.
)
def build_model(self, sample_size=_DEFAULT_MC_SAMPLE_SIZE) -> models.ExposureModel:

129
cara/model_scenarios_paper.py
View file

@ -489,97 +489,118 @@ def shouting_heavy_exercise_exposure():
return exposure_mc
########## Concentration curves ###########
def classroom_no_mask_windows_closed_exposure():
exposure_mc = mc.ExposureModel(
def office_model_no_mask_windows_closed():
office_model_no_vent = 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,
),
room=models.Room(volume=160, humidity=0.3),
ventilation=models.MultipleVentilation(
(models.AirChange(active=models.PeriodicInterval(period=120, duration=120), air_exch=0.0),
models.AirChange(active=models.PeriodicInterval(period=120, duration=120), air_exch=0.25))),
infected=mc.InfectedPopulation(
number=1,
presence=models.SpecificInterval(present_times = ((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
virus=mc.SARSCoV2(
viral_load_in_sputum=symptomatic_vl_frequencies,
infectious_dose=infectious_dose_distribution,
viable_to_RNA=infectious_virus_distribution,
),
presence=models.SpecificInterval(((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
mask=models.Mask.types["No mask"],
activity=activity_distributions['Light activity'],
expiration=models.Expiration.types['Talking'],
),
activity=activity_distributions['Seated'],
expiration=models.MultipleExpiration(
expirations = (models.Expiration.types['Talking'],
models.Expiration.types['Breathing']),
weights=(1, 2)
)
)
),
exposed=mc.Population(
number=14,
presence=models.SpecificInterval(((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
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"],
),
mask=models.Mask.types['No mask']
)
)
return exposure_mc
return office_model_no_vent
def classrom_no_mask_windows_open_breaks():
exposure_mc = mc.ExposureModel(
def office_model_no_mask_windows_open_breaks():
office_model_no_vent = mc.ExposureModel(
concentration_model=mc.ConcentrationModel(
room=models.Room(volume=100, humidity=0.5),
ventilation=models.SlidingWindow(
active=models.SpecificInterval(((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,
room=models.Room(volume=160, humidity=0.3),
ventilation = models.MultipleVentilation(
ventilations=(
models.SlidingWindow(
active=models.SpecificInterval(present_times=((1.5, 2), (3.5, 4.5), (6, 6.5))),
inside_temp=models.PiecewiseConstant((0, 24), (295,)),
outside_temp=models.PiecewiseConstant((0, 24), (291,)),
window_height=1.6,
opening_length=0.6,
),
models.AirChange(active=models.PeriodicInterval(period=120, duration=120), air_exch=0.25),
)
),
infected=mc.InfectedPopulation(
number=1,
presence=models.SpecificInterval(present_times=((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
virus=mc.SARSCoV2(
viral_load_in_sputum=symptomatic_vl_frequencies,
infectious_dose=infectious_dose_distribution,
viable_to_RNA=infectious_virus_distribution,
),
presence=models.SpecificInterval(((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
mask=models.Mask.types["No mask"],
activity=activity_distributions['Light activity'],
expiration=models.Expiration.types['Talking'],
),
activity=activity_distributions['Seated'],
expiration=models.MultipleExpiration(
expirations = (models.Expiration.types['Talking'],
models.Expiration.types['Breathing']),
weights=(1, 2)
)
)
),
exposed=mc.Population(
number=14,
presence=models.SpecificInterval(((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
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"],
),
mask=models.Mask.types['No mask']
)
)
return exposure_mc
return office_model_no_vent
def classrom_no_mask_windows_open_alltimes():
exposure_mc = mc.ExposureModel(
def office_model_no_mask_windows_open_alltimes():
office_model_no_vent = mc.ExposureModel(
concentration_model=mc.ConcentrationModel(
room=models.Room(volume=100, humidity=0.5),
ventilation=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,
room=models.Room(volume=160, humidity=0.3),
ventilation=models.MultipleVentilation(
ventilations=(
models.SlidingWindow(
active=models.PeriodicInterval(period=120, duration=120),
inside_temp=models.PiecewiseConstant((0, 24), (295,)),
outside_temp=models.PiecewiseConstant((0, 24), (291,)),
window_height=1.6, opening_length=0.6,
),
models.AirChange(active=models.PeriodicInterval(period=120, duration=120), air_exch=0.25),
)
),
infected=mc.InfectedPopulation(
number=1,
presence=models.SpecificInterval(present_times=((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
virus=mc.SARSCoV2(
viral_load_in_sputum=symptomatic_vl_frequencies,
infectious_dose=infectious_dose_distribution,
viable_to_RNA=infectious_virus_distribution,
),
presence=models.SpecificInterval(((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
mask=models.Mask.types["No mask"],
activity=activity_distributions['Light activity'],
expiration=models.Expiration.types['Talking'],
),
activity=activity_distributions['Seated'],
expiration=models.MultipleExpiration(
expirations = (models.Expiration.types['Talking'],
models.Expiration.types['Breathing']),
weights=(1, 2)
)
)
),
exposed=mc.Population(
number=14,
presence=models.SpecificInterval(((0, 1.5), (2, 3.5), (4.5, 6), (6.5, 8))),
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"],
),
mask=models.Mask.types['No mask']
)
)
return exposure_mc
return office_model_no_vent

19
cara/models.py
View file

@ -924,6 +924,25 @@ class ExposureModel:
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)))))
fraction_deposited: _VectorisedFloat = DF
def _normed_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat:
"""The number of virions per meter^3 between any two times, normalized
by the emission rate of the infected population"""
for start, stop in self.exposed.presence.boundaries():
if start > time2:
normed_exposure = 0.
break
elif time2 <= stop:
normed_exposure = self.concentration_model.normed_integrated_concentration(time1, time2)
break
else:
normed_exposure = self.concentration_model.normed_integrated_concentration(time1, time2)
return normed_exposure
def exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat:
"""The number of virions per meter^3 between any two times."""
return (self._normed_exposure_between_bounds(time1, time2) *
self.concentration_model.infected.emission_rate_when_present())
def _normed_exposure(self) -> _VectorisedFloat:
"""
The number of virus per meter^3, normalized by the emission rate

27
cara/results_paper.py
View file

@ -15,7 +15,7 @@ import math
######### Plot material #########
SAMPLE_SIZE = 50000
SAMPLE_SIZE = 250000
viral_loads = np.linspace(2, 12, 600)
############# Markers (for legend) #############
@ -528,9 +528,9 @@ def calculate_deposition_factor():
############ Compare concentration curves ############
def compare_concentration_curves():
exp_models=[classroom_no_mask_windows_closed_exposure().build_model(size=SAMPLE_SIZE),
classrom_no_mask_windows_open_breaks().build_model(size=SAMPLE_SIZE),
classrom_no_mask_windows_open_alltimes().build_model(size=SAMPLE_SIZE)]
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']
@ -550,25 +550,20 @@ def compare_concentration_curves():
ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1] * 1.2)
ax.spines["right"].set_visible(False)
# When merged, use the integrated_concentration function.
factors = [0.6 * model.exposed.activity.inhalation_rate * (1 - model.exposed.mask.η_inhale) for model in exp_models]
present_indexes = np.array([exp_models[0].exposed.person_present(t) for t in times])
modified_concentrations = [np.array(c) for c in concentrations]
for mc in modified_concentrations:
mc[~present_indexes] = 0
cumulative_doses = [[np.trapz(c[:i + 1], times[:i + 1]) * factor for i in range(len(times))]
for c, factor in zip(modified_concentrations, factors)]
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 viral concentration\n(virion m$^{-3}$)", 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, qd, label='qD - ' + label, color=color, linestyle='dotted')
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\n(virion)', fontsize=14)
ax1.set_ylabel('Mean cumulative dose (virions)', fontsize=14)
ax1.set_ylim(ax1.get_ylim()[0], ax1.get_ylim()[1] * 1.2)
plt.show()