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updated schema to reflect v2.0.0

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This commit is contained in:
Luis Aleixo 2024-03-07 16:44:31 +01:00
parent 74849cdee0
commit c02737cf92
6 changed files with 236 additions and 262 deletions
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2
caimira/apps/calculator/co2_model_generator.py
View file

@ -159,7 +159,7 @@ class CO2FormData(FormData):
return tuple((self.CO2_data['times'][0], self.CO2_data['times'][-1]))
def build_model(self, size=None) -> models.CO2DataModel: # type: ignore
size = size or self.data_registry.monte_carlo_sample_size
size = size or self.data_registry.monte_carlo['sample_size']
# Build a simple infected and exposed population for the case when presence
# intervals and number of people are dynamic. Activity type is not needed.
infected_presence = self.infected_present_interval()

10
caimira/apps/calculator/model_generator.py
View file

@ -200,10 +200,10 @@ class VirusFormData(FormData):
if self.arve_sensors_option == False:
if self.room_heating_option:
humidity = self.data_registry.room['defaults']['humidity_with_heating']
humidity = self.data_registry.room['humidity_with_heating']
else:
humidity = self.data_registry.room['defaults']['humidity_without_heating']
inside_temp = self.data_registry.room['defaults']['inside_temp']
humidity = self.data_registry.room['humidity_without_heating']
inside_temp = self.data_registry.room['inside_temp']
else:
humidity = float(self.humidity)
inside_temp = self.inside_temp
@ -245,11 +245,11 @@ class VirusFormData(FormData):
)
def build_model(self, sample_size=None) -> models.ExposureModel:
sample_size = sample_size or self.data_registry.monte_carlo_sample_size
sample_size = sample_size or self.data_registry.monte_carlo['sample_size']
return self.build_mc_model().build_model(size=sample_size)
def build_CO2_model(self, sample_size=None) -> models.CO2ConcentrationModel:
sample_size = sample_size or self.data_registry.monte_carlo_sample_size
sample_size = sample_size or self.data_registry.monte_carlo['sample_size']
infected_population: models.InfectedPopulation = self.infected_population().build_model(sample_size)
exposed_population: models.Population = self.exposed_population().build_model(sample_size)

10
caimira/apps/calculator/report_generator.py
View file

@ -233,8 +233,8 @@ def conditional_prob_inf_given_vl_dist(
for vl_log in viral_loads:
specific_prob = infection_probability[np.where((vl_log-step/2-specific_vl)*(vl_log+step/2-specific_vl)<0)[0]] #type: ignore
pi_means.append(specific_prob.mean())
lower_percentiles.append(np.quantile(specific_prob, data_registry.conditional_prob_inf_given_viral_load['lower_percentile']))
upper_percentiles.append(np.quantile(specific_prob, data_registry.conditional_prob_inf_given_viral_load['upper_percentile']))
lower_percentiles.append(np.quantile(specific_prob, 0.05))
upper_percentiles.append(np.quantile(specific_prob, 0.95))
return pi_means, lower_percentiles, upper_percentiles
@ -245,8 +245,8 @@ def manufacture_conditional_probability_data(
):
data_registry: DataRegistry = exposure_model.data_registry
min_vl = data_registry.conditional_prob_inf_given_viral_load['min_vl']
max_vl = data_registry.conditional_prob_inf_given_viral_load['max_vl']
min_vl = 2
max_vl = 10
step = (max_vl - min_vl)/100
viral_loads = np.arange(min_vl, max_vl, step)
specific_vl = np.log10(exposure_model.concentration_model.virus.viral_load_in_sputum)
@ -442,7 +442,7 @@ def scenario_statistics(
sample_times: typing.List[float],
compute_prob_exposure: bool
):
model = mc_model.build_model(size=mc_model.data_registry.monte_carlo_sample_size)
model = mc_model.build_model(size=mc_model.data_registry.monte_carlo['sample_size'])
if (compute_prob_exposure):
# It means we have data to calculate the total_probability_rule
prob_probabilistic_exposure = model.total_probability_rule()

12
caimira/models.py
View file

@ -879,7 +879,7 @@ class _PopulationWithVirus(Population):
The fraction of infectious virus.
"""
return self.data_registry.population_with_virus['fraction_of_infectious_virus'] # type: ignore
return 1
def aerosols(self):
"""
@ -1052,7 +1052,7 @@ class _ConcentrationModelBase:
(in the same unit as the concentration). Its the value towards which
the concentration will decay to.
"""
return self.data_registry.concentration_model['min_background_concentration'] # type: ignore
return self.data_registry.concentration_model['virus_concentration_model']['min_background_concentration'] # type: ignore
def normalization_factor(self) -> _VectorisedFloat:
"""
@ -1242,7 +1242,7 @@ class ConcentrationModel(_ConcentrationModelBase):
def __post_init__(self):
if self.evaporation_factor is None:
self.evaporation_factor = self.data_registry.particle['evaporation_factor']
self.evaporation_factor = self.data_registry.expiration_particle['particle']['evaporation_factor']
@property
def population(self) -> InfectedPopulation:
@ -1335,7 +1335,7 @@ class ShortRangeModel:
'''
The dilution factor for the respective expiratory activity type.
'''
_dilution_factor = self.data_registry.short_range_model['dilution_factor']
_dilution_factor = self.data_registry.short_range_model['dilution_factor']
# Average mouth opening diameter (m)
mouth_diameter: float = _dilution_factor['mouth_diameter'] # type: ignore
@ -1359,7 +1359,7 @@ class ShortRangeModel:
tstar: float = _dilution_factor['tstar'] # type: ignore
# Streamwise and radial penetration coefficients
_df_pc = _dilution_factor['penetration_coefficients']
_df_pc = _dilution_factor['penetration_coefficients'] # type: ignore
𝛽r1: float = _df_pc['𝛽r1'] # type: ignore
𝛽r2: float = _df_pc['𝛽r2'] # type: ignore
𝛽x1: float = _df_pc['𝛽x1'] # type: ignore
@ -1585,7 +1585,7 @@ class ExposureModel:
#: The number of times the exposure event is repeated (default 1).
@property
def repeats(self) -> int:
return self.data_registry.exposure_model['repeats'] # type: ignore
return 1
def __post_init__(self):
"""

58
caimira/monte_carlo/data.py
View file

@ -148,21 +148,21 @@ class BLOmodel:
# total concentration of aerosols for each mode.
@property
def cn(self) -> typing.Tuple[float, float, float]:
_cn = self.data_registry.BLOmodel['cn']
_cn = self.data_registry.expiration_particle['BLOmodel']['cn'] # type: ignore
return (_cn['B'],_cn['L'],_cn['O'])
# Mean of the underlying normal distributions (represents the log of a
# diameter in microns), for resp. the B, L and O modes.
@property
def mu(self) -> typing.Tuple[float, float, float]:
_mu = self.data_registry.BLOmodel['mu']
_mu = self.data_registry.expiration_particle['BLOmodel']['mu'] # type: ignore
return (_mu['B'], _mu['L'], _mu['O'])
# Std deviation of the underlying normal distribution, for resp.
# the B, L and O modes.
@property
def sigma(self) -> typing.Tuple[float, float, float]:
_sigma = self.data_registry.BLOmodel['sigma']
_sigma = self.data_registry.expiration_particle['BLOmodel']['sigma'] # type: ignore
return (_sigma['B'],_sigma['L'],_sigma['O'])
def distribution(self, d):
@ -250,51 +250,51 @@ symptomatic_vl_frequencies = LogCustomKernel(
def viral_load(data_registry):
return np.linspace(
weibull_min.ppf(
data_registry.covid_overal_vl_data['start'],
c=data_registry.covid_overal_vl_data['shape_factor'],
scale=data_registry.covid_overal_vl_data['scale_factor']
data_registry.virological_data['covid_overal_vl_data']['start'],
c=data_registry.virological_data['covid_overal_vl_data']['shape_factor'],
scale=data_registry.virological_data['covid_overal_vl_data']['scale_factor']
),
weibull_min.ppf(
data_registry.covid_overal_vl_data['stop'],
c=data_registry.covid_overal_vl_data['shape_factor'],
scale=data_registry.covid_overal_vl_data['scale_factor']
data_registry.virological_data['covid_overal_vl_data']['stop'],
c=data_registry.virological_data['covid_overal_vl_data']['shape_factor'],
scale=data_registry.virological_data['covid_overal_vl_data']['scale_factor']
),
int(data_registry.covid_overal_vl_data['num'])
int(data_registry.virological_data['covid_overal_vl_data']['num'])
)
def frequencies_pdf(data_registry):
return weibull_min.pdf(
viral_load(data_registry),
c=data_registry.covid_overal_vl_data['shape_factor'],
scale=data_registry.covid_overal_vl_data['scale_factor']
c=data_registry.virological_data['covid_overal_vl_data']['shape_factor'],
scale=data_registry.virological_data['covid_overal_vl_data']['scale_factor']
)
def covid_overal_vl_data(data_registry):
return LogCustom(
bounds=(data_registry.covid_overal_vl_data['min_bound'], data_registry.covid_overal_vl_data['max_bound']),
bounds=(data_registry.virological_data['covid_overal_vl_data']['min_bound'], data_registry.virological_data['covid_overal_vl_data']['max_bound']),
function=lambda d: np.interp(
d,
viral_load(data_registry),
frequencies_pdf(data_registry),
data_registry.covid_overal_vl_data['interpolation_fp_left'],
data_registry.covid_overal_vl_data['interpolation_fp_right']
data_registry.virological_data['covid_overal_vl_data']['interpolation_fp_left'],
data_registry.virological_data['covid_overal_vl_data']['interpolation_fp_right']
),
max_function=data_registry.covid_overal_vl_data['max_function']
max_function=data_registry.virological_data['covid_overal_vl_data']['max_function']
)
# Derived from data in doi.org/10.1016/j.ijid.2020.09.025 and
# https://iosh.com/media/8432/aerosol-infection-risk-hospital-patient-care-full-report.pdf (page 60)
def viable_to_RNA_ratio_distribution(data_registry):
return Uniform(data_registry.viable_to_RNA_ratio_distribution['low'], data_registry.viable_to_RNA_ratio_distribution['high'])
return Uniform(data_registry.virological_data['viable_to_RNA_ratio_distribution']['low'], data_registry.virological_data['viable_to_RNA_ratio_distribution']['high'])
# From discussion with virologists
def infectious_dose_distribution(data_registry):
return Uniform(data_registry.infectious_dose_distribution['low'], data_registry.infectious_dose_distribution['high'])
return Uniform(data_registry.virological_data['infectious_dose_distribution']['low'], data_registry.virological_data['infectious_dose_distribution']['high'])
# From https://doi.org/10.1101/2021.10.14.21264988 and references therein
def virus_distributions(data_registry):
vd = data_registry.virus_distributions
vd = data_registry.virological_data['virus_distributions']
return {
'SARS_CoV_2': mc.SARSCoV2(
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2']['viral_load_in_sputum'], data_registry),
@ -392,10 +392,10 @@ def expiration_distribution(
def expiration_BLO_factors(data_registry):
breathing = data_registry.expiration_BLO_factors['Breathing']
speaking = data_registry.expiration_BLO_factors['Speaking']
singing = data_registry.expiration_BLO_factors['Singing']
shouting = data_registry.expiration_BLO_factors['Shouting']
breathing = data_registry.expiration_particle['expiration_BLO_factors']['Breathing']
speaking = data_registry.expiration_particle['expiration_BLO_factors']['Speaking']
singing = data_registry.expiration_particle['expiration_BLO_factors']['Singing']
shouting = data_registry.expiration_particle['expiration_BLO_factors']['Shouting']
return {
'Breathing': (
param_evaluation(breathing, 'B'),
@ -425,8 +425,8 @@ def expiration_distributions(data_registry):
exp_type: expiration_distribution(
data_registry=data_registry,
BLO_factors=BLO_factors,
d_min=param_evaluation(data_registry.long_range_expiration_distributions, 'minimum_diameter'),
d_max=param_evaluation(data_registry.long_range_expiration_distributions, 'maximum_diameter')
d_min=param_evaluation(data_registry.expiration_particle['long_range_expiration_distributions'], 'minimum_diameter'),
d_max=param_evaluation(data_registry.expiration_particle['long_range_expiration_distributions'], 'maximum_diameter')
)
for exp_type, BLO_factors in expiration_BLO_factors(data_registry).items()
}
@ -437,8 +437,8 @@ def short_range_expiration_distributions(data_registry):
exp_type: expiration_distribution(
data_registry=data_registry,
BLO_factors=BLO_factors,
d_min=param_evaluation(data_registry.short_range_expiration_distributions, 'minimum_diameter'),
d_max=param_evaluation(data_registry.short_range_expiration_distributions, 'maximum_diameter')
d_min=param_evaluation(data_registry.expiration_particle['short_range_expiration_distributions'], 'minimum_diameter'),
d_max=param_evaluation(data_registry.expiration_particle['short_range_expiration_distributions'], 'maximum_diameter')
)
for exp_type, BLO_factors in expiration_BLO_factors(data_registry).items()
}
@ -452,8 +452,8 @@ frequencies = np.array((0.0598036, 0.0946154, 0.1299152, 0.1064905, 0.1099066, 0
def short_range_distances(data_registry):
return Custom(
bounds=(
param_evaluation(data_registry.short_range_distances, 'minimum_distance'),
param_evaluation(data_registry.short_range_distances, 'maximum_distance')
param_evaluation(data_registry.short_range_model['conversational_distance'], 'minimum_distance'),
param_evaluation(data_registry.short_range_model['conversational_distance'], 'maximum_distance')
),
function=lambda x: np.interp(x, distances, frequencies, left=0., right=0.),
max_function=0.13

406
caimira/store/data_registry.py
View file

@ -6,23 +6,31 @@ class DataRegistry:
version = None
BLOmodel = {
"cn": {
"B": 0.06,
"L": 0.2,
"O": 0.0010008,
expiration_particle = {
"long_range_expiration_distributions": {
"minimum_diameter": 0.1,
"maximum_diameter": 30,
},
"mu": {
"B": 0.989541,
"L": 1.38629,
"O": 4.97673,
"short_range_expiration_distributions": {
"minimum_diameter": 0.1,
"maximum_diameter": 100,
},
"sigma": {
"B": 0.262364,
"L": 0.506818,
"O": 0.585005,
"BLOmodel": {
"cn": {"B": 0.06, "L": 0.2, "O": 0.0010008},
"mu": {"B": 0.989541, "L": 1.38629, "O": 4.97673},
"sigma": {"B": 0.262364, "L": 0.506818, "O": 0.585005},
},
"expiration_BLO_factors": {
"Breathing": {"B": 1., "L": 0., "O": 0., },
"Speaking": {"B": 1., "L": 1., "O": 1., },
"Singing": {"B": 1., "L": 5., "O": 5., },
"Shouting": {"B": 1., "L": 5., "O": 5., },
},
"particle": {
"evaporation_factor": 0.3,
}
}
activity_distributions = {
"Seated": {
"inhalation_rate": {
@ -105,160 +113,164 @@ class DataRegistry:
},
},
}
symptomatic_vl_frequencies = {
"log_variable": [
2.46032,
2.67431,
2.85434,
3.06155,
3.25856,
3.47256,
3.66957,
3.85979,
4.09927,
4.27081,
4.47631,
4.66653,
4.87204,
5.10302,
5.27456,
5.46478,
5.6533,
5.88428,
6.07281,
6.30549,
6.48552,
6.64856,
6.85407,
7.10373,
7.30075,
7.47229,
7.66081,
7.85782,
8.05653,
8.27053,
8.48453,
8.65607,
8.90573,
9.06878,
9.27429,
9.473,
9.66152,
9.87552,
],
"frequencies": [
0.001206885,
0.007851618,
0.008078144,
0.01502491,
0.013258014,
0.018528495,
0.020053765,
0.021896167,
0.022047184,
0.018604005,
0.01547796,
0.018075445,
0.021503523,
0.022349217,
0.025097721,
0.032875078,
0.030594727,
0.032573045,
0.034717482,
0.034792991,
0.033267721,
0.042887485,
0.036846816,
0.03876473,
0.045016819,
0.040063473,
0.04883754,
0.043944602,
0.048142864,
0.041588741,
0.048762031,
0.027921732,
0.033871788,
0.022122693,
0.016927718,
0.008833228,
0.00478598,
0.002807662,
],
"kernel_bandwidth": 0.1,
}
covid_overal_vl_data = {
"shape_factor": 3.47,
"scale_factor": 7.01,
"start": 0.01,
"stop": 0.99,
"num": 30.0,
"min_bound": 2,
"max_bound": 10,
"interpolation_fp_left": 0,
"interpolation_fp_right": 0,
"max_function": 0.2,
}
viable_to_RNA_ratio_distribution = {
"low": 0.01,
"high": 0.6,
}
infectious_dose_distribution = {
"low": 10,
"high": 100,
}
virus_distributions = {
"SARS_CoV_2": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 1,
"infectiousness_days": 14,
virological_data = {
"symptomatic_vl_frequencies": {
"log_variable": [
2.46032,
2.67431,
2.85434,
3.06155,
3.25856,
3.47256,
3.66957,
3.85979,
4.09927,
4.27081,
4.47631,
4.66653,
4.87204,
5.10302,
5.27456,
5.46478,
5.6533,
5.88428,
6.07281,
6.30549,
6.48552,
6.64856,
6.85407,
7.10373,
7.30075,
7.47229,
7.66081,
7.85782,
8.05653,
8.27053,
8.48453,
8.65607,
8.90573,
9.06878,
9.27429,
9.473,
9.66152,
9.87552,
],
"frequencies": [
0.001206885,
0.007851618,
0.008078144,
0.01502491,
0.013258014,
0.018528495,
0.020053765,
0.021896167,
0.022047184,
0.018604005,
0.01547796,
0.018075445,
0.021503523,
0.022349217,
0.025097721,
0.032875078,
0.030594727,
0.032573045,
0.034717482,
0.034792991,
0.033267721,
0.042887485,
0.036846816,
0.03876473,
0.045016819,
0.040063473,
0.04883754,
0.043944602,
0.048142864,
0.041588741,
0.048762031,
0.027921732,
0.033871788,
0.022122693,
0.016927718,
0.008833228,
0.00478598,
0.002807662,
],
"kernel_bandwidth": 0.1,
},
"SARS_CoV_2_ALPHA": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.78,
"infectiousness_days": 14,
'covid_overal_vl_data': {
"shape_factor": 3.47,
"scale_factor": 7.01,
"start": 0.01,
"stop": 0.99,
"num": 30.0,
"min_bound": 2,
"max_bound": 10,
"interpolation_fp_left": 0,
"interpolation_fp_right": 0,
"max_function": 0.2,
},
"SARS_CoV_2_BETA": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.8,
"infectiousness_days": 14,
"viable_to_RNA_ratio_distribution": {
"low": 0.01,
"high": 0.6,
},
"SARS_CoV_2_GAMMA": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.72,
"infectiousness_days": 14,
"infectious_dose_distribution": {
"low": 10,
"high": 100,
},
"SARS_CoV_2_DELTA": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.51,
"infectiousness_days": 14,
},
"SARS_CoV_2_OMICRON": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.2,
"infectiousness_days": 14,
},
"SARS_CoV_2_Other": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.1,
"infectiousness_days": 14,
"virus_distributions": {
"SARS_CoV_2": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 1,
"infectiousness_days": 14,
},
"SARS_CoV_2_ALPHA": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.78,
"infectiousness_days": 14,
},
"SARS_CoV_2_BETA": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.8,
"infectiousness_days": 14,
},
"SARS_CoV_2_GAMMA": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.72,
"infectiousness_days": 14,
},
"SARS_CoV_2_DELTA": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.51,
"infectiousness_days": 14,
},
"SARS_CoV_2_OMICRON": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.2,
"infectiousness_days": 14,
},
"SARS_CoV_2_Other": {
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
"transmissibility_factor": 0.1,
"infectiousness_days": 14,
},
},
}
mask_distributions = {
"Type I": {
"η_inhale": {
@ -301,50 +313,15 @@ class DataRegistry:
"factor_exhale": 1,
},
}
expiration_BLO_factors = {
"Breathing": {
"B": 1.0,
"L": 0.0,
"O": 0.0,
},
"Speaking": {
"B": 1.0,
"L": 1.0,
"O": 1.0,
},
"Singing": {
"B": 1.0,
"L": 5.0,
"O": 5.0,
},
"Shouting": {
"B": 1.0,
"L": 5.0,
"O": 5.0,
},
}
long_range_expiration_distributions = {
"minimum_diameter": 0.1,
"maximum_diameter": 30,
}
short_range_expiration_distributions = {
"minimum_diameter": 0.1,
"maximum_diameter": 100,
}
short_range_distances = {
"minimum_distance": 0.5,
"maximum_distance": 2.0,
}
####################################
room = {
"defaults": {
"inside_temp": 293,
"humidity_with_heating": 0.3,
"humidity_without_heating": 0.5,
},
"inside_temp": 293,
"humidity_with_heating": 0.3,
"humidity_without_heating": 0.5,
}
ventilation = {
"natural": {
"discharge_factor": {
@ -353,19 +330,17 @@ class DataRegistry:
},
"infiltration_ventilation": 0.25,
}
particle = {
"evaporation_factor": 0.3,
}
population_with_virus = {
"fraction_of_infectious_virus": 1,
}
concentration_model = {
"min_background_concentration": 0.0,
"virus_concentration_model": {
"min_background_concentration": 0.0,
},
"CO2_concentration_model": {
"CO2_atmosphere_concentration": 440.44,
"CO2_fraction_exhaled": 0.042,
},
}
short_range_model = {
"dilution_factor": {
"mouth_diameter": 0.02,
@ -377,17 +352,16 @@ class DataRegistry:
"𝛽x1": 2.4,
},
},
"conversational_distance": {
"minimum_distance": 0.5,
"maximum_distance": 2.0,
},
}
exposure_model = {
"repeats": 1,
monte_carlo = {
"sample_size": 250000,
}
conditional_prob_inf_given_viral_load = {
"lower_percentile": 0.05,
"upper_percentile": 0.95,
"min_vl": 2,
"max_vl": 10,
}
monte_carlo_sample_size = 250000
population_scenario_activity = {
"office": {"placeholder": "Office", "activity": "Seated", "expiration": {"Speaking": 1, "Breathing": 2}},
"smallmeeting": {