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Merge branch 'changes/caimira_schema_2.0.2' into 'master'

Browse source 
Changes to reflect schema update 2.0.2

See merge request caimira/caimira!497
This commit is contained in:
Andre Henriques 2024-07-05 09:56:54 +02:00
commit e1a94b9067
4 changed files with 272 additions and 161 deletions
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2
caimira/apps/calculator/model_generator.py
View file

@ -208,7 +208,7 @@ class VirusFormData(FormData):
humidity = float(self.humidity)
inside_temp = self.inside_temp
return models.Room(volume=volume, inside_temp=models.PiecewiseConstant((0, 24), (inside_temp,)), humidity=humidity)
return models.Room(volume=volume, inside_temp=models.PiecewiseConstant((0, 24), (inside_temp,)), humidity=humidity) # type: ignore
def build_mc_model(self) -> mc.ExposureModel:
room = self.initialize_room()

4
caimira/enums.py
View file

@ -1,5 +1,5 @@
from enum import Enum
class ViralLoads(Enum):
COVID_OVERALL = "Ref: Viral load - covid_overal_vl_data"
SYMPTOMATIC_FREQUENCIES = "Ref: Viral load - symptomatic_vl_frequencies"
COVID_OVERALL = "Ref: Viral load - covid overal viral load data"
SYMPTOMATIC_FREQUENCIES = "Ref: Viral load - symptomatic viral load frequencies"

90
caimira/monte_carlo/data.py
View file

@ -18,7 +18,7 @@ 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 root[value] == ViralLoads.SYMPTOMATIC_FREQUENCIES.value:
return symptomatic_vl_frequencies
return symptomatic_vl_frequencies(data_registry)
elif root[value] == 'Custom':
return param_evaluation(root, 'Viral load custom')
else:
@ -66,11 +66,26 @@ def evaluate_custom_value_type(value_type: str, params: typing.Dict) -> typing.A
if value_type == 'Constant value':
return params
elif value_type == 'Normal distribution':
return Normal(params['normal_mean_gaussian'], params['normal_standard_deviation_gaussian'])
return Normal(
mean=params['normal_mean_gaussian'],
standard_deviation=params['normal_standard_deviation_gaussian']
)
elif value_type == 'Log-normal distribution':
return LogNormal(params['lognormal_mean_gaussian'], params['lognormal_standard_deviation_gaussian'])
return LogNormal(
mean_gaussian=params['lognormal_mean_gaussian'],
standard_deviation_gaussian=params['lognormal_standard_deviation_gaussian']
)
elif value_type == 'Uniform distribution':
return Uniform(params['low'], params['high'])
return Uniform(
low=params['low'],
high=params['high']
)
elif value_type == 'Log Custom Kernel distribution':
return LogCustomKernel(
log_variable=np.array(params['log_variable']),
frequencies=np.array(params['frequencies']),
kernel_bandwidth=params['kernel_bandwidth']
)
else:
raise ValueError('Bad request - value type not found.')
@ -211,11 +226,7 @@ def activity_distributions(data_registry):
# From https://doi.org/10.1101/2021.10.14.21264988 and references therein
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']
)
return param_evaluation(data_registry.virological_data, 'symptomatic_vl_frequencies')
# Weibull distribution with a shape factor of 3.47 and a scale factor of 7.01.
@ -224,34 +235,34 @@ def symptomatic_vl_frequencies(data_registry):
def viral_load(data_registry):
return np.linspace(
weibull_min.ppf(
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']
data_registry.virological_data['covid_overal_vl_data']['parameters']['start'],
c=data_registry.virological_data['covid_overal_vl_data']['parameters']['shape_factor'],
scale=data_registry.virological_data['covid_overal_vl_data']['parameters']['scale_factor']
),
weibull_min.ppf(
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']
data_registry.virological_data['covid_overal_vl_data']['parameters']['stop'],
c=data_registry.virological_data['covid_overal_vl_data']['parameters']['shape_factor'],
scale=data_registry.virological_data['covid_overal_vl_data']['parameters']['scale_factor']
),
int(data_registry.virological_data['covid_overal_vl_data']['num'])
int(data_registry.virological_data['covid_overal_vl_data']['parameters']['num'])
)
def frequencies_pdf(data_registry):
return weibull_min.pdf(
viral_load(data_registry),
c=data_registry.virological_data['covid_overal_vl_data']['shape_factor'],
scale=data_registry.virological_data['covid_overal_vl_data']['scale_factor']
c=data_registry.virological_data['covid_overal_vl_data']['parameters']['shape_factor'],
scale=data_registry.virological_data['covid_overal_vl_data']['parameters']['scale_factor']
)
def covid_overal_vl_data(data_registry):
return LogCustom(
bounds=(data_registry.virological_data['covid_overal_vl_data']['min_bound'], data_registry.virological_data['covid_overal_vl_data']['max_bound']),
bounds=(data_registry.virological_data['covid_overal_vl_data']['parameters']['min_bound'], data_registry.virological_data['covid_overal_vl_data']['parameters']['max_bound']),
function=lambda d: np.interp(
d,
viral_load(data_registry),
frequencies_pdf(data_registry),
data_registry.virological_data['covid_overal_vl_data']['interpolation_fp_left'],
data_registry.virological_data['covid_overal_vl_data']['interpolation_fp_right']
data_registry.virological_data['covid_overal_vl_data']['parameters']['interpolation_fp_left'],
data_registry.virological_data['covid_overal_vl_data']['parameters']['interpolation_fp_right']
),
max_function=data_registry.virological_data['covid_overal_vl_data']['max_function']
max_function=data_registry.virological_data['covid_overal_vl_data']['parameters']['max_function']
)
@ -274,37 +285,43 @@ def virus_distributions(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'],
transmissibility_factor=vd['SARS_CoV_2']['transmissibility_factor']['value'],
infectiousness_days=vd['SARS_CoV_2']['infectiousness_days']['value'],
),
'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=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'],
transmissibility_factor=vd['SARS_CoV_2_ALPHA']['transmissibility_factor']['value'],
infectiousness_days=vd['SARS_CoV_2_ALPHA']['infectiousness_days']['value'],
),
'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=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'],
transmissibility_factor=vd['SARS_CoV_2_BETA']['transmissibility_factor']['value'],
infectiousness_days=vd['SARS_CoV_2_BETA']['infectiousness_days']['value'],
),
'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=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'],
transmissibility_factor=vd['SARS_CoV_2_GAMMA']['transmissibility_factor']['value'],
infectiousness_days=vd['SARS_CoV_2_GAMMA']['infectiousness_days']['value'],
),
'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=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'],
transmissibility_factor=vd['SARS_CoV_2_DELTA']['transmissibility_factor']['value'],
infectiousness_days=vd['SARS_CoV_2_DELTA']['infectiousness_days']['value'],
),
'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=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'],
transmissibility_factor=vd['SARS_CoV_2_OMICRON']['transmissibility_factor']['value'],
infectiousness_days=vd['SARS_CoV_2_OMICRON']['infectiousness_days']['value'],
),
}
@ -321,21 +338,24 @@ 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'].get('η_exhale') is not None else None
if data_registry.mask_distributions['Type I'].get('η_exhale') is not None else None,
factor_exhale=data_registry.mask_distributions['Type I']['factor_exhale']['value']
),
'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'].get('η_exhale') is not None else None
if data_registry.mask_distributions['FFP2'].get('η_exhale') is not None else None,
factor_exhale=data_registry.mask_distributions['FFP2']['factor_exhale']['value']
),
'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'].get('η_exhale') is not None else None
if data_registry.mask_distributions['Cloth'].get('η_exhale') is not None else None,
factor_exhale=data_registry.mask_distributions['Cloth']['factor_exhale']['value']
),
}
@ -399,8 +419,8 @@ def expiration_distributions(data_registry):
exp_type: expiration_distribution(
data_registry=data_registry,
BLO_factors=BLO_factors,
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')
d_min=param_evaluation(data_registry.expiration_particle['long_range_particle_diameter'], 'minimum_diameter'),
d_max=param_evaluation(data_registry.expiration_particle['long_range_particle_diameter'], 'maximum_diameter')
)
for exp_type, BLO_factors in expiration_BLO_factors(data_registry).items()
}
@ -411,8 +431,8 @@ 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.expiration_particle['short_range_expiration_distributions'], 'minimum_diameter'),
d_max=param_evaluation(data_registry.expiration_particle['short_range_expiration_distributions'], 'maximum_diameter')
d_min=param_evaluation(data_registry.expiration_particle['short_range_particle_diameter'], 'minimum_diameter'),
d_max=param_evaluation(data_registry.expiration_particle['short_range_particle_diameter'], 'maximum_diameter')
)
for exp_type, BLO_factors in expiration_BLO_factors(data_registry).items()
}

337
caimira/store/data_registry.py
View file

@ -7,28 +7,33 @@ class DataRegistry:
version = None
expiration_particle = {
"long_range_expiration_distributions": {
"long_range_particle_diameter": {
"minimum_diameter": 0.1,
"maximum_diameter": 30,
"references": "Morawska et al. (https://doi.org/10.1016/j.jaerosci.2008.11.002); Johnson et al. (https://doi.org/10.1016/j.jaerosci.2011.07.009).",
},
"short_range_expiration_distributions": {
"short_range_particle_diameter": {
"minimum_diameter": 0.1,
"maximum_diameter": 100,
"references": "Morawska et al. (https://doi.org/10.1016/j.jaerosci.2008.11.002); Johnson et al. (https://doi.org/10.1016/j.jaerosci.2011.07.009).",
},
"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},
"references": "Morawska et al. (https://doi.org/10.1016/j.jaerosci.2008.11.002); Johnson et al. (https://doi.org/10.1016/j.jaerosci.2011.07.009).",
},
"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., },
"references": "Morawska et al. (https://doi.org/10.1016/j.jaerosci.2008.11.002); Johnson et al. (https://doi.org/10.1016/j.jaerosci.2011.07.009).",
},
"particle": {
"evaporation_factor": 0.3,
}
"references": "Marr et al. (https://doi.org/10.1098/rsif.2018.0298).",
},
}
activity_distributions = {
@ -39,6 +44,7 @@ class DataRegistry:
"lognormal_mean_gaussian": -0.6872121723362303,
"lognormal_standard_deviation_gaussian": 0.10498338229297108,
},
"references": "Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
"exhalation_rate": {
"associated_value": "Log-normal distribution",
@ -46,6 +52,7 @@ class DataRegistry:
"lognormal_mean_gaussian": -0.6872121723362303,
"lognormal_standard_deviation_gaussian": 0.10498338229297108,
},
"references": "Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
},
"Standing": {
@ -55,6 +62,7 @@ class DataRegistry:
"lognormal_mean_gaussian": -0.5742377578494785,
"lognormal_standard_deviation_gaussian": 0.09373162411398223,
},
"references": "Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
"exhalation_rate": {
"associated_value": "Log-normal distribution",
@ -62,6 +70,7 @@ class DataRegistry:
"lognormal_mean_gaussian": -0.5742377578494785,
"lognormal_standard_deviation_gaussian": 0.09373162411398223,
},
"references": "Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
},
"Light activity": {
@ -71,6 +80,7 @@ class DataRegistry:
"lognormal_mean_gaussian": 0.21380242785625422,
"lognormal_standard_deviation_gaussian": 0.09435378091059601,
},
"references": "Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
"exhalation_rate": {
"associated_value": "Log-normal distribution",
@ -78,6 +88,7 @@ class DataRegistry:
"lognormal_mean_gaussian": 0.21380242785625422,
"lognormal_standard_deviation_gaussian": 0.09435378091059601,
},
"references": "Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
},
"Moderate activity": {
@ -87,6 +98,7 @@ class DataRegistry:
"lognormal_mean_gaussian": 0.551771330362601,
"lognormal_standard_deviation_gaussian": 0.1894616357138137,
},
"references": "Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
"exhalation_rate": {
"associated_value": "Log-normal distribution",
@ -94,6 +106,7 @@ class DataRegistry:
"lognormal_mean_gaussian": 0.551771330362601,
"lognormal_standard_deviation_gaussian": 0.1894616357138137,
},
"references": "Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
},
"Heavy exercise": {
@ -103,6 +116,7 @@ class DataRegistry:
"lognormal_mean_gaussian": 1.1644665696723049,
"lognormal_standard_deviation_gaussian": 0.21744554768657565,
},
"references": "Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
"exhalation_rate": {
"associated_value": "Log-normal distribution",
@ -110,105 +124,114 @@ class DataRegistry:
"lognormal_mean_gaussian": 1.1644665696723049,
"lognormal_standard_deviation_gaussian": 0.21744554768657565,
},
"references": "Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
},
}
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,
"associated_value": "Log Custom Kernel distribution",
"parameters": {
"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,
},
"references": "Henriques et al. (https://doi.org/10.1101/2021.10.14.21264988) and references therein.",
},
'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,
"covid_overal_vl_data": {
"associated_value": "Weibull distribution",
"parameters": {
"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,
},
"references": "Chen et al. (https://elifesciences.org/articles/65774); First line of the figure in https://iiif.elifesciences.org/lax:65774%2Felife-65774-fig4-figsupp3-v2.tif/full/1500,/0/default.jpg.",
},
"virus_distributions": {
"SARS_CoV_2": {
@ -216,78 +239,126 @@ class DataRegistry:
"infectious_dose": {
"associated_value": "Uniform distribution",
"parameters": {"low": 10, "high": 100},
"references": "Lednicky et al. (https://doi.org/10.1016/j.ijid.2020.09.025); Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
"viable_to_RNA_ratio": {
'associated_value': 'Uniform distribution',
'parameters': {'low': 0.01, 'high': 0.6},
"associated_value": "Uniform distribution",
"parameters": {"low": 0.01, "high": 0.6},
"references": "",
},
"transmissibility_factor": {
"value": 1,
"references": "Campbell et al. (https://doi.org/10.2807/1560-7917.ES.2021.26.24.2100509.)",
},
"infectiousness_days": {
"value": 14,
"references": "",
},
"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},
"references": "Lednicky et al. (https://doi.org/10.1016/j.ijid.2020.09.025); Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
"viable_to_RNA_ratio": {
'associated_value': 'Uniform distribution',
'parameters': {'low': 0.01, 'high': 0.6},
"associated_value": "Uniform distribution",
"parameters": {"low": 0.01, "high": 0.6},
"references": "",
},
"transmissibility_factor": {
"value": 0.78,
"references": "Campbell et al. (https://doi.org/10.2807/1560-7917.ES.2021.26.24.2100509.)",
},
"infectiousness_days": {
"value": 14,
"references": "",
},
"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},
"references": "Lednicky et al. (https://doi.org/10.1016/j.ijid.2020.09.025); Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
"viable_to_RNA_ratio": {
'associated_value': 'Uniform distribution',
'parameters': {'low': 0.01, 'high': 0.6},
"associated_value": "Uniform distribution",
"parameters": {"low": 0.01, "high": 0.6},
"references": "",
},
"transmissibility_factor": {
"value": 0.8,
"references": "Campbell et al. (https://doi.org/10.2807/1560-7917.ES.2021.26.24.2100509.)",
},
"infectiousness_days": {
"value": 14,
"references": "",
},
"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},
"references": "Lednicky et al. (https://doi.org/10.1016/j.ijid.2020.09.025); Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
"viable_to_RNA_ratio": {
'associated_value': 'Uniform distribution',
'parameters': {'low': 0.01, 'high': 0.6},
"associated_value": "Uniform distribution",
"parameters": {"low": 0.01, "high": 0.6},
"references": "",
},
"transmissibility_factor": {
"value": 0.72,
"references": "Campbell et al. (https://doi.org/10.2807/1560-7917.ES.2021.26.24.2100509.)",
},
"infectiousness_days": {
"value": 14,
"references": "",
},
"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},
"references": "Lednicky et al. (https://doi.org/10.1016/j.ijid.2020.09.025); Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
"viable_to_RNA_ratio": {
'associated_value': 'Uniform distribution',
'parameters': {'low': 0.01, 'high': 0.6},
"associated_value": "Uniform distribution",
"parameters": {"low": 0.01, "high": 0.6},
"references": "",
},
"transmissibility_factor": {
"value": 0.51,
"references": "Campbell et al. (https://doi.org/10.2807/1560-7917.ES.2021.26.24.2100509.)",
},
"infectiousness_days": {
"value": 14,
"references": "",
},
"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},
"references": "Lednicky et al. (https://doi.org/10.1016/j.ijid.2020.09.025); Henriques et al. (https://doi.org/10.1098/rsfs.2021.0076) and references therein.",
},
"viable_to_RNA_ratio": {
'associated_value': 'Uniform distribution',
'parameters': {'low': 0.01, 'high': 0.6},
"associated_value": "Uniform distribution",
"parameters": {"low": 0.01, "high": 0.6},
"references": "",
},
"transmissibility_factor": {
"value": 0.2,
"references": "",
},
"infectiousness_days": {
"value": 14,
"references": "",
},
"transmissibility_factor": 0.2,
"infectiousness_days": 14,
},
},
}
@ -300,8 +371,12 @@ class DataRegistry:
"low": 0.25,
"high": 0.80,
},
"references": "Pan et al. (https://doi.org/10.1080/02786826.2021.1890687); Booth et al. (https://doi.org/10.1016/j.jhin.2013.02.007); Monn et al. (https://doi.org/10.4209/aaqr.2020.08.0531).",
},
"factor_exhale": {
"value": 1,
"references": "",
},
"factor_exhale": 1,
},
"FFP2": {
"η_inhale": {
@ -310,8 +385,12 @@ class DataRegistry:
"low": 0.83,
"high": 0.91,
},
"references": "Pan et al. (https://doi.org/10.1080/02786826.2021.1890687); Booth et al. (https://doi.org/10.1016/j.jhin.2013.02.007); Monn et al. (https://doi.org/10.4209/aaqr.2020.08.0531).",
},
"factor_exhale": {
"value": 1,
"references": "",
},
"factor_exhale": 1,
},
"Cloth": {
"η_inhale": {
@ -320,6 +399,7 @@ class DataRegistry:
"low": 0.05,
"high": 0.40,
},
"references": "Pan et al. (https://doi.org/10.1080/02786826.2021.1890687); Booth et al. (https://doi.org/10.1016/j.jhin.2013.02.007); Monn et al. (https://doi.org/10.4209/aaqr.2020.08.0531).",
},
"η_exhale": {
"associated_value": "Uniform distribution",
@ -327,17 +407,22 @@ class DataRegistry:
"low": 0.20,
"high": 0.50,
},
"references": "Pan et al. (https://doi.org/10.1080/02786826.2021.1890687); Booth et al. (https://doi.org/10.1016/j.jhin.2013.02.007); Monn et al. (https://doi.org/10.4209/aaqr.2020.08.0531).",
},
"factor_exhale": {
"value": 1,
"references": "",
},
"factor_exhale": 1,
},
}
####################################
room = {
"inside_temp": 293,
"inside_temp": 293.,
"humidity_with_heating": 0.3,
"humidity_without_heating": 0.5,
"references": "",
}
ventilation = {
@ -347,15 +432,18 @@ class DataRegistry:
},
},
"infiltration_ventilation": 0.25,
"references": "Henriques et al. (https://doi.org/10.1101/2021.10.14.21264988).",
}
concentration_model = {
"virus_concentration_model": {
"min_background_concentration": 0.0,
"references": "",
},
"CO2_concentration_model": {
"CO2_atmosphere_concentration": 440.44,
"CO2_fraction_exhaled": 0.042,
"references": "",
},
}
@ -369,15 +457,18 @@ class DataRegistry:
"𝛽r2": 0.2,
"𝛽x1": 2.4,
},
"references": "Jia et al. (https://doi.org/10.1016/j.buildenv.2022.109166).",
},
"conversational_distance": {
"minimum_distance": 0.5,
"maximum_distance": 2.0,
"references": "Derived from Fig. 8 a) 'stand-stand' in Zhang et al. (https://www.mdpi.com/1660-4601/17/4/1445).",
},
}
monte_carlo = {
"sample_size": 250000,
"references": "",
}
population_scenario_activity = {