diff --git a/caimira/enums.py b/caimira/enums.py
index 6a776e2c..8b66443b 100644
--- a/caimira/enums.py
+++ b/caimira/enums.py
@@ -3,11 +3,3 @@ from enum import Enum
class ViralLoads(Enum):
COVID_OVERALL = "Ref: Viral load - covid_overal_vl_data"
SYMPTOMATIC_FREQUENCIES = "Ref: Viral load - symptomatic_vl_frequencies"
-
-
-class InfectiousDoses(Enum):
- DISTRIBUTION = "Ref: Infectious dose - infectious_dose_distribution"
-
-
-class ViableToRNARatios(Enum):
- DISTRIBUTION = "Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution"
diff --git a/caimira/models.py b/caimira/models.py
index 441b70ff..e671194b 100644
--- a/caimira/models.py
+++ b/caimira/models.py
@@ -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
@@ -1355,11 +1355,14 @@ class ShortRangeModel:
# Initial velocity of the exhalation airflow (m/s)
u0 = np.array(Q_exh/Am)
- # Duration of the expiration period(s), assuming a 4s breath-cycle
- tstar: float = _dilution_factor['tstar'] # type: ignore
+ # Duration of one breathing cycle
+ breathing_cicle: float = _dilution_factor['breathing_cycle'] # type: ignore
+
+ # Duration of the expiration period(s)
+ tstar: float = breathing_cicle / 2
# 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 +1588,7 @@ 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):
"""
diff --git a/caimira/monte_carlo/data.py b/caimira/monte_carlo/data.py
index d82970aa..d35ab37d 100644
--- a/caimira/monte_carlo/data.py
+++ b/caimira/monte_carlo/data.py
@@ -7,84 +7,72 @@ import numpy as np
from scipy import special as sp
from scipy.stats import weibull_min
-from caimira.enums import ViralLoads, InfectiousDoses, ViableToRNARatios
+from caimira.enums import ViralLoads
import caimira.monte_carlo.models as mc
from caimira.monte_carlo.sampleable import LogCustom, LogNormal, Normal, LogCustomKernel, CustomKernel, Uniform, Custom
from caimira.store.data_registry import DataRegistry
-def evaluate_vl(value, data_registry: DataRegistry):
- if value == ViralLoads.COVID_OVERALL.value:
+def evaluate_vl(root: typing.Dict, value: str, data_registry: DataRegistry):
+ if root[value] == ViralLoads.COVID_OVERALL.value:
return covid_overal_vl_data(data_registry)
- elif value == ViralLoads.SYMPTOMATIC_FREQUENCIES.value:
+ elif root[value] == ViralLoads.SYMPTOMATIC_FREQUENCIES.value:
return symptomatic_vl_frequencies
+ elif root[value] == 'Custom':
+ return param_evaluation(root, 'Viral load custom')
else:
raise ValueError(f"Invalid ViralLoads value {value}")
-def evaluate_infectd(value, data_registry: DataRegistry):
- if value == InfectiousDoses.DISTRIBUTION.value:
- return infectious_dose_distribution(data_registry)
- else:
- raise ValueError(f"Invalid InfectiousDoses value {value}")
-
-
-def evaluate_vtrr(value, data_registry: DataRegistry):
- if value == ViableToRNARatios.DISTRIBUTION.value:
- return viable_to_RNA_ratio_distribution(data_registry)
- else:
- raise ValueError(f"Invalid ViableToRNARatios value {value}")
-
-
sqrt2pi = np.sqrt(2.*np.pi)
sqrt2 = np.sqrt(2.)
-def custom_distribution_lookup(dict: dict, key_part: str) -> typing.Any:
+def custom_value_type_lookup(dict: dict, key_part: str) -> typing.Any:
"""
- Look up a custom distribution based on a partial key.
+ Look up a custom value type based on a partial key.
Args:
dict (dict): The root to search.
- key_part (str): The distribution key to match.
+ key_part (str): The value type key to match.
Returns:
- str: The associated distribution.
+ str: The associated value.
"""
try:
for key, value in dict.items():
if (key_part in key):
- return value['associated_distribution']
+ return value['associated_value']
except KeyError:
return f"Key '{key_part}' not found."
-def evaluate_custom_distribution(dist: str, params: typing.Dict) -> typing.Any:
+def evaluate_custom_value_type(value_type: str, params: typing.Dict) -> typing.Any:
"""
- Evaluate a custom distribution.
+ Evaluate a custom value type.
Args:
- dist (str): The type of distribution.
- params (Dict): The parameters for the distribution.
+ dist (str): The type of value.
+ params (Dict): The parameters for the value type.
Returns:
- Any: The generated distribution.
+ Any: The generated value.
Raises:
- ValueError: If the distribution type is not recognized.
+ ValueError: If the value type is not recognized.
"""
- if dist == 'Linear Space':
- return np.linspace(params['start'], params['stop'], params['num'])
- elif dist == 'Normal':
+ if value_type == 'Constant value':
+ return params
+ elif value_type == 'Normal distribution':
return Normal(params['normal_mean_gaussian'], params['normal_standard_deviation_gaussian'])
- elif dist == 'Log-normal':
+ elif value_type == 'Log-normal distribution':
return LogNormal(params['lognormal_mean_gaussian'], params['lognormal_standard_deviation_gaussian'])
- elif dist == 'Uniform':
+ elif value_type == 'Uniform distribution':
return Uniform(params['low'], params['high'])
else:
- raise ValueError('Bad request - distribution not found.')
+ raise ValueError('Bad request - value type not found.')
def param_evaluation(root: typing.Dict, param: typing.Union[str, typing.Any]) -> typing.Any:
@@ -104,17 +92,10 @@ def param_evaluation(root: typing.Dict, param: typing.Union[str, typing.Any]) ->
"""
value = root.get(param)
- if isinstance(value, str):
- if value == 'Custom':
- custom_distribution: typing.Dict = custom_distribution_lookup(
- root, 'custom distribution')
- for d, p in custom_distribution.items():
- return evaluate_custom_distribution(d, p)
-
- elif isinstance(value, dict):
- dist: str = root[param]['associated_distribution']
+ if isinstance(value, dict):
+ value_type: str = root[param]['associated_value']
params: typing.Dict = root[param]['parameters']
- return evaluate_custom_distribution(dist, params)
+ return evaluate_custom_value_type(value_type, params)
elif isinstance(value, float) or isinstance(value, int):
return value
@@ -148,21 +129,21 @@ 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):
@@ -229,19 +210,12 @@ def activity_distributions(data_registry):
# From https://doi.org/10.1101/2021.10.14.21264988 and references therein
-symptomatic_vl_frequencies = LogCustomKernel(
- np.array((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)),
- np.array((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
-)
+def symptomatic_vl_frequencies(data_registry):
+ return LogCustomKernel(
+ np.array(data_registry.virological_data['symptomatic_vl_frequencies']['log_variable']),
+ np.array(data_registry.virological_data['symptomatic_vl_frequencies']['frequencies']),
+ kernel_bandwidth=data_registry.virological_data['symptomatic_vl_frequencies']['kernel_bandwidth']
+ )
# Weibull distribution with a shape factor of 3.47 and a scale factor of 7.01.
@@ -250,86 +224,86 @@ 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'])
+def viable_to_RNA_ratio_distribution():
+ return Uniform(0.01, 0.6)
# From discussion with virologists
-def infectious_dose_distribution(data_registry):
- return Uniform(data_registry.infectious_dose_distribution['low'], data_registry.infectious_dose_distribution['high'])
+def infectious_dose_distribution():
+ return Uniform(10., 100.)
# 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),
- infectious_dose=evaluate_infectd(vd['SARS_CoV_2']['infectious_dose'], data_registry),
- viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2']['viable_to_RNA_ratio'], data_registry),
+ viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2'], 'viral_load_in_sputum', data_registry),
+ infectious_dose=param_evaluation(vd['SARS_CoV_2'], 'infectious_dose'),
+ viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2'], 'viable_to_RNA_ratio'),
transmissibility_factor=vd['SARS_CoV_2']['transmissibility_factor'],
),
'SARS_CoV_2_ALPHA': mc.SARSCoV2(
- viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_ALPHA']['viral_load_in_sputum'], data_registry),
- infectious_dose=evaluate_infectd(vd['SARS_CoV_2_ALPHA']['infectious_dose'], data_registry),
- viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_ALPHA']['viable_to_RNA_ratio'], data_registry),
+ viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_ALPHA'], 'viral_load_in_sputum', data_registry),
+ infectious_dose=param_evaluation(vd['SARS_CoV_2_ALPHA'], 'infectious_dose'),
+ viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2_ALPHA'], 'viable_to_RNA_ratio'),
transmissibility_factor=vd['SARS_CoV_2_ALPHA']['transmissibility_factor'],
),
'SARS_CoV_2_BETA': mc.SARSCoV2(
- viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_BETA']['viral_load_in_sputum'], data_registry),
- infectious_dose=evaluate_infectd(vd['SARS_CoV_2_BETA']['infectious_dose'], data_registry),
- viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_BETA']['viable_to_RNA_ratio'], data_registry),
+ viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_BETA'], 'viral_load_in_sputum', data_registry),
+ infectious_dose=param_evaluation(vd['SARS_CoV_2_BETA'], 'infectious_dose'),
+ viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2_BETA'], 'viable_to_RNA_ratio'),
transmissibility_factor=vd['SARS_CoV_2_BETA']['transmissibility_factor'],
),
'SARS_CoV_2_GAMMA': mc.SARSCoV2(
- viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_GAMMA']['viral_load_in_sputum'], data_registry),
- infectious_dose=evaluate_infectd(vd['SARS_CoV_2_GAMMA']['infectious_dose'], data_registry),
- viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_GAMMA']['viable_to_RNA_ratio'], data_registry),
+ viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_GAMMA'], 'viral_load_in_sputum', data_registry),
+ infectious_dose=param_evaluation(vd['SARS_CoV_2_GAMMA'], 'infectious_dose'),
+ viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2_GAMMA'], 'viable_to_RNA_ratio'),
transmissibility_factor=vd['SARS_CoV_2_GAMMA']['transmissibility_factor'],
),
'SARS_CoV_2_DELTA': mc.SARSCoV2(
- viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_DELTA']['viral_load_in_sputum'], data_registry),
- infectious_dose=evaluate_infectd(vd['SARS_CoV_2_DELTA']['infectious_dose'], data_registry),
- viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_DELTA']['viable_to_RNA_ratio'], data_registry),
+ viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_DELTA'], 'viral_load_in_sputum', data_registry),
+ infectious_dose=param_evaluation(vd['SARS_CoV_2_DELTA'], 'infectious_dose'),
+ viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2_DELTA'], 'viable_to_RNA_ratio'),
transmissibility_factor=vd['SARS_CoV_2_DELTA']['transmissibility_factor'],
),
'SARS_CoV_2_OMICRON': mc.SARSCoV2(
- viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_OMICRON']['viral_load_in_sputum'], data_registry),
- infectious_dose=evaluate_infectd(vd['SARS_CoV_2_OMICRON']['infectious_dose'], data_registry),
- viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_OMICRON']['viable_to_RNA_ratio'], data_registry),
+ viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_OMICRON'], 'viral_load_in_sputum', data_registry),
+ infectious_dose=param_evaluation(vd['SARS_CoV_2_OMICRON'], 'infectious_dose'),
+ viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2_OMICRON'], 'viable_to_RNA_ratio'),
transmissibility_factor=vd['SARS_CoV_2_OMICRON']['transmissibility_factor'],
),
}
@@ -347,21 +321,21 @@ def mask_distributions(data_registry):
data_registry.mask_distributions['Type I'], 'η_inhale'),
η_exhale=param_evaluation(
data_registry.mask_distributions['Type I'], 'η_exhale')
- if data_registry.mask_distributions['Type I']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
+ if data_registry.mask_distributions['Type I'].get('η_exhale') is not None else None
),
'FFP2': mc.Mask(
η_inhale=param_evaluation(
data_registry.mask_distributions['FFP2'], 'η_inhale'),
η_exhale=param_evaluation(
data_registry.mask_distributions['FFP2'], 'η_exhale')
- if data_registry.mask_distributions['FFP2']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
+ if data_registry.mask_distributions['FFP2'].get('η_exhale') is not None else None
),
'Cloth': mc.Mask(
η_inhale=param_evaluation(
data_registry.mask_distributions['Cloth'], 'η_inhale'),
η_exhale=param_evaluation(
data_registry.mask_distributions['Cloth'], 'η_exhale')
- if data_registry.mask_distributions['Cloth']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
+ if data_registry.mask_distributions['Cloth'].get('η_exhale') is not None else None
),
}
@@ -392,10 +366,10 @@ 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 +399,8 @@ 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 +411,8 @@ 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 +426,8 @@ 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
diff --git a/caimira/store/data_registry.py b/caimira/store/data_registry.py
index cced1032..498db95b 100644
--- a/caimira/store/data_registry.py
+++ b/caimira/store/data_registry.py
@@ -1,4 +1,4 @@
-from caimira.enums import ViralLoads, InfectiousDoses, ViableToRNARatios
+from caimira.enums import ViralLoads
class DataRegistry:
@@ -6,34 +6,42 @@ 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": {
- "associated_distribution": "Log-normal",
+ "associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": -0.6872121723362303,
"lognormal_standard_deviation_gaussian": 0.10498338229297108,
},
},
"exhalation_rate": {
- "associated_distribution": "Log-normal",
+ "associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": -0.6872121723362303,
"lognormal_standard_deviation_gaussian": 0.10498338229297108,
@@ -42,14 +50,14 @@ class DataRegistry:
},
"Standing": {
"inhalation_rate": {
- "associated_distribution": "Log-normal",
+ "associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": -0.5742377578494785,
"lognormal_standard_deviation_gaussian": 0.09373162411398223,
},
},
"exhalation_rate": {
- "associated_distribution": "Log-normal",
+ "associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": -0.5742377578494785,
"lognormal_standard_deviation_gaussian": 0.09373162411398223,
@@ -58,14 +66,14 @@ class DataRegistry:
},
"Light activity": {
"inhalation_rate": {
- "associated_distribution": "Log-normal",
+ "associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 0.21380242785625422,
"lognormal_standard_deviation_gaussian": 0.09435378091059601,
},
},
"exhalation_rate": {
- "associated_distribution": "Log-normal",
+ "associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 0.21380242785625422,
"lognormal_standard_deviation_gaussian": 0.09435378091059601,
@@ -74,14 +82,14 @@ class DataRegistry:
},
"Moderate activity": {
"inhalation_rate": {
- "associated_distribution": "Log-normal",
+ "associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 0.551771330362601,
"lognormal_standard_deviation_gaussian": 0.1894616357138137,
},
},
"exhalation_rate": {
- "associated_distribution": "Log-normal",
+ "associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 0.551771330362601,
"lognormal_standard_deviation_gaussian": 0.1894616357138137,
@@ -90,14 +98,14 @@ class DataRegistry:
},
"Heavy exercise": {
"inhalation_rate": {
- "associated_distribution": "Log-normal",
+ "associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 1.1644665696723049,
"lognormal_standard_deviation_gaussian": 0.21744554768657565,
},
},
"exhalation_rate": {
- "associated_distribution": "Log-normal",
+ "associated_value": "Log-normal distribution",
"parameters": {
"lognormal_mean_gaussian": 1.1644665696723049,
"lognormal_standard_deviation_gaussian": 0.21744554768657565,
@@ -105,194 +113,216 @@ 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,
- },
- "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,
+ "virus_distributions": {
+ "SARS_CoV_2": {
+ "viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
+ "infectious_dose": {
+ "associated_value": "Uniform distribution",
+ "parameters": {"low": 10, "high": 100},
+ },
+ "viable_to_RNA_ratio": {
+ 'associated_value': 'Uniform distribution',
+ 'parameters': {'low': 0.01, 'high': 0.6},
+ },
+ "transmissibility_factor": 1,
+ "infectiousness_days": 14,
+ },
+ "SARS_CoV_2_ALPHA": {
+ "viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
+ "infectious_dose": {
+ "associated_value": "Uniform distribution",
+ "parameters": {"low": 10, "high": 100},
+ },
+ "viable_to_RNA_ratio": {
+ 'associated_value': 'Uniform distribution',
+ 'parameters': {'low': 0.01, 'high': 0.6},
+ },
+ "transmissibility_factor": 0.78,
+ "infectiousness_days": 14,
+ },
+ "SARS_CoV_2_BETA": {
+ "viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
+ "infectious_dose": {
+ "associated_value": "Uniform distribution",
+ "parameters": {"low": 10, "high": 100},
+ },
+ "viable_to_RNA_ratio": {
+ 'associated_value': 'Uniform distribution',
+ 'parameters': {'low': 0.01, 'high': 0.6},
+ },
+ "transmissibility_factor": 0.8,
+ "infectiousness_days": 14,
+ },
+ "SARS_CoV_2_GAMMA": {
+ "viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
+ "infectious_dose": {
+ "associated_value": "Uniform distribution",
+ "parameters": {"low": 10, "high": 100},
+ },
+ "viable_to_RNA_ratio": {
+ 'associated_value': 'Uniform distribution',
+ 'parameters': {'low': 0.01, 'high': 0.6},
+ },
+ "transmissibility_factor": 0.72,
+ "infectiousness_days": 14,
+ },
+ "SARS_CoV_2_DELTA": {
+ "viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
+ "infectious_dose": {
+ "associated_value": "Uniform distribution",
+ "parameters": {"low": 10, "high": 100},
+ },
+ "viable_to_RNA_ratio": {
+ 'associated_value': 'Uniform distribution',
+ 'parameters': {'low': 0.01, 'high': 0.6},
+ },
+ "transmissibility_factor": 0.51,
+ "infectiousness_days": 14,
+ },
+ "SARS_CoV_2_OMICRON": {
+ "viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
+ "infectious_dose": {
+ "associated_value": "Uniform distribution",
+ "parameters": {"low": 10, "high": 100},
+ },
+ "viable_to_RNA_ratio": {
+ 'associated_value': 'Uniform distribution',
+ 'parameters': {'low': 0.01, 'high': 0.6},
+ },
+ "transmissibility_factor": 0.2,
+ "infectiousness_days": 14,
+ },
},
}
+
mask_distributions = {
"Type I": {
"η_inhale": {
- "associated_distribution": "Uniform",
+ "associated_value": "Uniform distribution",
"parameters": {
"low": 0.25,
"high": 0.80,
},
},
- "Known filtration efficiency of masks when exhaling?": "No",
"factor_exhale": 1,
},
"FFP2": {
"η_inhale": {
- "associated_distribution": "Uniform",
+ "associated_value": "Uniform distribution",
"parameters": {
"low": 0.83,
"high": 0.91,
},
},
- "Known filtration efficiency of masks when exhaling?": "No",
"factor_exhale": 1,
},
"Cloth": {
"η_inhale": {
- "associated_distribution": "Uniform",
+ "associated_value": "Uniform distribution",
"parameters": {
"low": 0.05,
"high": 0.40,
},
},
- "Known filtration efficiency of masks when exhaling?": "Yes",
"η_exhale": {
- "associated_distribution": "Uniform",
+ "associated_value": "Uniform distribution",
"parameters": {
"low": 0.20,
"high": 0.50,
@@ -301,50 +331,15 @@ 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": {
@@ -353,41 +348,38 @@ 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,
"exhalation_coefficient": 2,
- "tstar": 2,
+ "breathing_cycle": 4,
"penetration_coefficients": {
"𝛽r1": 0.18,
"𝛽r2": 0.2,
"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": {
diff --git a/caimira/store/data_service.py b/caimira/store/data_service.py
index 80463f42..1ecbac82 100644
--- a/caimira/store/data_service.py
+++ b/caimira/store/data_service.py
@@ -20,7 +20,7 @@ class DataService:
self._host = host
@classmethod
- def create(cls, host: str = "https://caimira-data-api.app.cern.ch"):
+ def create(cls, host: str = "https://caimira-data-api-qa.app.cern.ch"):
"""Factory."""
return cls(host)
diff --git a/caimira/tests/test_monte_carlo_full_models.py b/caimira/tests/test_monte_carlo_full_models.py
index f5b6bc74..ec4f6496 100644
--- a/caimira/tests/test_monte_carlo_full_models.py
+++ b/caimira/tests/test_monte_carlo_full_models.py
@@ -186,8 +186,8 @@ def skagit_chorale_mc(data_registry):
presence=models.SpecificInterval(((0, 2.5), )),
virus=mc.SARSCoV2(
viral_load_in_sputum=10**9,
- infectious_dose=infectious_dose_distribution(data_registry),
- viable_to_RNA_ratio=viable_to_RNA_ratio_distribution(data_registry),
+ infectious_dose=infectious_dose_distribution(),
+ viable_to_RNA_ratio=viable_to_RNA_ratio_distribution(),
transmissibility_factor=1.,
),
mask=models.Mask.types['No mask'],
@@ -230,8 +230,8 @@ def bus_ride_mc(data_registry):
presence=models.SpecificInterval(((0, 1.67), )),
virus=mc.SARSCoV2(
viral_load_in_sputum=5*10**8,
- infectious_dose=infectious_dose_distribution(data_registry),
- viable_to_RNA_ratio=viable_to_RNA_ratio_distribution(data_registry),
+ infectious_dose=infectious_dose_distribution(),
+ viable_to_RNA_ratio=viable_to_RNA_ratio_distribution(),
transmissibility_factor=1.,
),
mask=models.Mask.types['No mask'],