494 lines
20 KiB
Python
494 lines
20 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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import typing
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import numpy as np
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from scipy import special as sp
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from scipy.stats import weibull_min
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from caimira.enums import ViralLoads, InfectiousDoses, ViableToRNARatios
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import caimira.monte_carlo.models as mc
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from caimira.monte_carlo.sampleable import LogCustom, LogNormal, Normal, LogCustomKernel, CustomKernel, Uniform, Custom
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from caimira.store.data_registry import DataRegistry
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def evaluate_vl(value, data_registry: DataRegistry):
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# FIXME: temp fix until data service values is updated
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# remove old Ref value
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if "covid_overal_vl_data" in value:
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value = ViralLoads.COVID_OVERALL.value
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elif "symptomatic_vl_frequencies" in value:
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value = ViralLoads.SYMPTOMATIC_FREQUENCIES.value
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if value == ViralLoads.COVID_OVERALL.value:
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return covid_overal_vl_data(data_registry)
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elif value == ViralLoads.SYMPTOMATIC_FREQUENCIES.value:
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return symptomatic_vl_frequencies
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else:
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raise ValueError(f"Invalid ViralLoads value {value}")
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def evaluate_infectd(value, data_registry: DataRegistry):
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# FIXME: temp fix until data service values is updated
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# remove old Ref value
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if "infectious_dose_distribution" in value:
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value = InfectiousDoses.DISTRIBUTION.value
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if value == InfectiousDoses.DISTRIBUTION.value:
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return infectious_dose_distribution(data_registry)
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else:
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raise ValueError(f"Invalid InfectiousDoses value {value}")
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def evaluate_vtrr(value, data_registry: DataRegistry):
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# FIXME: temp fix until data service values is updated
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# remove old Ref value
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if "viable_to_RNA_ratio_distribution" in value:
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value = ViableToRNARatios.DISTRIBUTION.value
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if value == ViableToRNARatios.DISTRIBUTION.value:
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return viable_to_RNA_ratio_distribution(data_registry)
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else:
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raise ValueError(f"Invalid ViableToRNARatios value {value}")
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sqrt2pi = np.sqrt(2.*np.pi)
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sqrt2 = np.sqrt(2.)
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def custom_distribution_lookup(dict: dict, key_part: str) -> typing.Any:
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"""
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Look up a custom distribution based on a partial key.
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Args:
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dict (dict): The root to search.
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key_part (str): The distribution key to match.
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Returns:
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str: The associated distribution.
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"""
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try:
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for key, value in dict.items():
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if (key_part in key):
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return value['associated_distribution']
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except KeyError:
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return f"Key '{key_part}' not found."
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def evaluate_reference(reference_variable: str) -> typing.Any:
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"""
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Evaluate a reference variable.
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Args:
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reference_variable (str): The variable to evaluate.
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Returns:
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Any: The evaluated value or an error message if the variable is not defined.
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"""
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try:
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return eval(reference_variable)
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except NameError:
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return f"Variable '{reference_variable}' is not defined."
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def evaluate_custom_distribution(dist: str, params: typing.Dict) -> typing.Any:
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"""
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Evaluate a custom distribution.
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Args:
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dist (str): The type of distribution.
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params (Dict): The parameters for the distribution.
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Returns:
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Any: The generated distribution.
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Raises:
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ValueError: If the distribution type is not recognized.
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"""
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if dist == 'Numpy Linear Space (linspace)':
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return np.linspace(params['start'], params['stop'], params['num'])
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elif dist == 'Numpy Normal Distribution (random.normal)':
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return Normal(params['mean_gaussian'], params['standard_deviation_gaussian'])
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elif dist == 'Numpy Log-normal Distribution (random.lognormal)':
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return LogNormal(params['mean_gaussian'], params['standard_deviation_gaussian'])
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elif dist == 'Numpy Uniform Distribution (random.uniform)':
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return Uniform(params['low'], params['high'])
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else:
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raise ValueError('Bad request - distribution not found.')
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def param_evaluation(root: typing.Dict, param: typing.Union[str, typing.Any]) -> typing.Any:
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"""
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Evaluate a parameter from a nested dictionary.
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Args:
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root (dict): The root dictionary.
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param (Union[str, Any]): The parameter to evaluate.
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Returns:
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Any: The evaluated value.
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Raises:
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TypeError: If the type of the parameter is not defined.
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"""
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value = root.get(param)
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if isinstance(value, str):
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if value == 'Custom':
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custom_distribution: typing.Dict = custom_distribution_lookup(
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root, 'custom distribution')
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for d, p in custom_distribution.items():
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return evaluate_custom_distribution(d, p)
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elif isinstance(value, dict):
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dist: str = root[param]['associated_distribution']
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params: typing.Dict = root[param]['parameters']
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return evaluate_custom_distribution(dist, params)
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elif isinstance(value, float) or isinstance(value, int):
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return value
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else:
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raise TypeError('Bad request - type not allowed.')
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@dataclass(frozen=True)
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class BLOmodel:
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"""
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Represents the probability distribution from the BLO model.
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It is a sum of three lognormal distributions, each of the form
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A * cn * (1 / d) * (1 / (np.sqrt(2 * np.pi) * sigma)) *
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np.exp(-(np.log(d)-mu) ** 2 / (2 * sigma ** 2))
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with A the factor in front of the B, L or O mode.
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From G. Johnson et al., Modality of human
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expired aerosol size distributions, Journal of Aerosol Science,
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vol. 42, no. 12, pp. 839 – 851, 2011,
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https://doi.org/10.1016/j.jaerosci.2011.07.009).
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"""
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data_registry: DataRegistry
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#: Factors assigned to resp. the B, L and O modes. They are
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# charateristics of the kind of expiratory activity (e.g. breathing,
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# speaking, singing, or shouting). These are applied on top of the
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# cn concentrations (see below), and depend on the kind of activity
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# (breathing, speaking, singing/shouting)
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BLO_factors: typing.Tuple[float, float, float]
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#: cn (cm^-3) for resp. the B, L and O modes. Corresponds to the
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# total concentration of aerosols for each mode.
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@property
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def cn(self) -> typing.Tuple[float, float, float]:
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_cn = self.data_registry.BLOmodel['cn']
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return (_cn['B'],_cn['L'],_cn['O'])
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# Mean of the underlying normal distributions (represents the log of a
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# diameter in microns), for resp. the B, L and O modes.
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@property
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def mu(self) -> typing.Tuple[float, float, float]:
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_mu = self.data_registry.BLOmodel['mu']
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return (_mu['B'], _mu['L'], _mu['O'])
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# Std deviation of the underlying normal distribution, for resp.
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# the B, L and O modes.
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@property
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def sigma(self) -> typing.Tuple[float, float, float]:
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_sigma = self.data_registry.BLOmodel['sigma']
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return (_sigma['B'],_sigma['L'],_sigma['O'])
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def distribution(self, d):
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"""
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Returns the raw value of the probability distribution for a
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given diameter d (microns).
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"""
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return sum((1 / d) * (A * cn / (sqrt2pi * sigma)) *
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np.exp(-(np.log(d) - mu) ** 2 / (2 * sigma ** 2))
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for A, cn, mu, sigma in zip(self.BLO_factors, self.cn,
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self.mu, self.sigma))
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def integrate(self, dmin, dmax):
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"""
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Returns the integral between dmin and dmax (in microns) of the
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probability distribution.
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"""
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result = 0.
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for A, cn, mu, sigma in zip(self.BLO_factors, self.cn, self.mu, self.sigma):
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ymin = (np.log(dmin)-mu)/(sqrt2*sigma)
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ymax = (np.log(dmax)-mu)/(sqrt2*sigma)
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result += A * cn * (sp.erf(ymax)-sp.erf(ymin)) / 2.
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return result
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# From https://doi.org/10.1101/2021.10.14.21264988 and references therein
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def activity_distributions(data_registry):
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return {
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'Seated': mc.Activity(
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inhalation_rate=param_evaluation(
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data_registry.activity_distributions['Seated'], 'inhalation_rate'),
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exhalation_rate=param_evaluation(
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data_registry.activity_distributions['Seated'], 'exhalation_rate'),
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),
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'Standing': mc.Activity(
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inhalation_rate=param_evaluation(
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data_registry.activity_distributions['Standing'], 'inhalation_rate'),
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exhalation_rate=param_evaluation(
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data_registry.activity_distributions['Standing'], 'exhalation_rate'),
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),
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'Light activity': mc.Activity(
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inhalation_rate=param_evaluation(
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data_registry.activity_distributions['Light activity'], 'inhalation_rate'),
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exhalation_rate=param_evaluation(
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data_registry.activity_distributions['Light activity'], 'exhalation_rate'),
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),
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'Moderate activity': mc.Activity(
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inhalation_rate=param_evaluation(
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data_registry.activity_distributions['Moderate activity'], 'inhalation_rate'),
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exhalation_rate=param_evaluation(
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data_registry.activity_distributions['Moderate activity'], 'exhalation_rate'),
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),
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'Heavy exercise': mc.Activity(
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inhalation_rate=param_evaluation(
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data_registry.activity_distributions['Heavy exercise'], 'inhalation_rate'),
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exhalation_rate=param_evaluation(
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data_registry.activity_distributions['Heavy exercise'], 'exhalation_rate'),
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),
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}
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# From https://doi.org/10.1101/2021.10.14.21264988 and references therein
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symptomatic_vl_frequencies = LogCustomKernel(
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np.array((2.46032, 2.67431, 2.85434, 3.06155, 3.25856, 3.47256, 3.66957, 3.85979, 4.09927, 4.27081,
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4.47631, 4.66653, 4.87204, 5.10302, 5.27456, 5.46478, 5.6533, 5.88428, 6.07281, 6.30549,
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6.48552, 6.64856, 6.85407, 7.10373, 7.30075, 7.47229, 7.66081, 7.85782, 8.05653, 8.27053,
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8.48453, 8.65607, 8.90573, 9.06878, 9.27429, 9.473, 9.66152, 9.87552)),
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np.array((0.001206885, 0.007851618, 0.008078144, 0.01502491, 0.013258014, 0.018528495, 0.020053765,
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0.021896167, 0.022047184, 0.018604005, 0.01547796, 0.018075445, 0.021503523, 0.022349217,
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0.025097721, 0.032875078, 0.030594727, 0.032573045, 0.034717482, 0.034792991,
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0.033267721, 0.042887485, 0.036846816, 0.03876473, 0.045016819, 0.040063473, 0.04883754,
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0.043944602, 0.048142864, 0.041588741, 0.048762031, 0.027921732, 0.033871788,
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0.022122693, 0.016927718, 0.008833228, 0.00478598, 0.002807662)),
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kernel_bandwidth=0.1
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)
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# Weibull distribution with a shape factor of 3.47 and a scale factor of 7.01.
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# From https://elifesciences.org/articles/65774 and first line of the figure in
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# https://iiif.elifesciences.org/lax:65774%2Felife-65774-fig4-figsupp3-v2.tif/full/1500,/0/default.jpg
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def viral_load(data_registry):
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return np.linspace(
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weibull_min.ppf(
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data_registry.covid_overal_vl_data['start'],
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c=data_registry.covid_overal_vl_data['shape_factor'],
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scale=data_registry.covid_overal_vl_data['scale_factor']
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),
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weibull_min.ppf(
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data_registry.covid_overal_vl_data['stop'],
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c=data_registry.covid_overal_vl_data['shape_factor'],
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scale=data_registry.covid_overal_vl_data['scale_factor']
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),
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int(data_registry.covid_overal_vl_data['num'])
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)
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def frequencies_pdf(data_registry):
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return weibull_min.pdf(
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viral_load(data_registry),
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c=data_registry.covid_overal_vl_data['shape_factor'],
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scale=data_registry.covid_overal_vl_data['scale_factor']
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)
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def covid_overal_vl_data(data_registry):
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return LogCustom(
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bounds=(data_registry.covid_overal_vl_data['min_bound'], data_registry.covid_overal_vl_data['max_bound']),
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function=lambda d: np.interp(
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d,
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viral_load(data_registry),
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frequencies_pdf(data_registry),
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data_registry.covid_overal_vl_data['interpolation_fp_left'],
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data_registry.covid_overal_vl_data['interpolation_fp_right']
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),
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max_function=data_registry.covid_overal_vl_data['max_function']
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)
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# Derived from data in doi.org/10.1016/j.ijid.2020.09.025 and
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# https://iosh.com/media/8432/aerosol-infection-risk-hospital-patient-care-full-report.pdf (page 60)
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def viable_to_RNA_ratio_distribution(data_registry):
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return Uniform(data_registry.viable_to_RNA_ratio_distribution['low'], data_registry.viable_to_RNA_ratio_distribution['high'])
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# From discussion with virologists
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def infectious_dose_distribution(data_registry):
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return Uniform(data_registry.infectious_dose_distribution['low'], data_registry.infectious_dose_distribution['high'])
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# From https://doi.org/10.1101/2021.10.14.21264988 and references therein
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def virus_distributions(data_registry):
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vd = data_registry.virus_distributions
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return {
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'SARS_CoV_2': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2']['viable_to_RNA_ratio'], data_registry),
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transmissibility_factor=vd['SARS_CoV_2']['transmissibility_factor'],
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),
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'SARS_CoV_2_ALPHA': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_ALPHA']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_ALPHA']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_ALPHA']['viable_to_RNA_ratio'], data_registry),
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transmissibility_factor=vd['SARS_CoV_2_ALPHA']['transmissibility_factor'],
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),
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'SARS_CoV_2_BETA': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_BETA']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_BETA']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_BETA']['viable_to_RNA_ratio'], data_registry),
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transmissibility_factor=vd['SARS_CoV_2_BETA']['transmissibility_factor'],
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),
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'SARS_CoV_2_GAMMA': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_GAMMA']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_GAMMA']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_GAMMA']['viable_to_RNA_ratio'], data_registry),
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transmissibility_factor=vd['SARS_CoV_2_GAMMA']['transmissibility_factor'],
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),
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'SARS_CoV_2_DELTA': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_DELTA']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_DELTA']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_DELTA']['viable_to_RNA_ratio'], data_registry),
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transmissibility_factor=vd['SARS_CoV_2_DELTA']['transmissibility_factor'],
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),
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'SARS_CoV_2_OMICRON': mc.SARSCoV2(
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viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_OMICRON']['viral_load_in_sputum'], data_registry),
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infectious_dose=evaluate_infectd(vd['SARS_CoV_2_OMICRON']['infectious_dose'], data_registry),
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viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_OMICRON']['viable_to_RNA_ratio'], data_registry),
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transmissibility_factor=vd['SARS_CoV_2_OMICRON']['transmissibility_factor'],
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),
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}
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# From:
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# https://doi.org/10.1080/02786826.2021.1890687
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# https://doi.org/10.1016/j.jhin.2013.02.007
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# https://doi.org/10.4209/aaqr.2020.08.0531
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# https://doi.org/10.1080/02786826.2021.1890687
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def mask_distributions(data_registry):
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return {
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'Type I': mc.Mask(
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η_inhale=param_evaluation(
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data_registry.mask_distributions['Type I'], 'η_inhale'),
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η_exhale=param_evaluation(
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data_registry.mask_distributions['Type I'], 'η_exhale')
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if data_registry.mask_distributions['Type I']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
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),
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'FFP2': mc.Mask(
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η_inhale=param_evaluation(
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data_registry.mask_distributions['FFP2'], 'η_inhale'),
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η_exhale=param_evaluation(
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data_registry.mask_distributions['FFP2'], 'η_exhale')
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if data_registry.mask_distributions['FFP2']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
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),
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'Cloth': mc.Mask(
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η_inhale=param_evaluation(
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data_registry.mask_distributions['Cloth'], 'η_inhale'),
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η_exhale=param_evaluation(
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data_registry.mask_distributions['Cloth'], 'η_exhale')
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if data_registry.mask_distributions['Cloth']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
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),
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}
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def expiration_distribution(
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data_registry,
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BLO_factors,
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d_min=0.1,
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d_max=30.,
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):
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"""
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Returns an Expiration with an aerosol diameter distribution, defined
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by the BLO factors (a length-3 tuple).
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The total concentration of aerosols, cn, is computed by integrating
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the distribution between 0.1 and 30 microns - these boundaries are
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an historical choice based on previous implementations of the model
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(it limits the influence of the O-mode).
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"""
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dscan = np.linspace(d_min, d_max, 3000)
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return mc.Expiration(
|
||
CustomKernel(
|
||
dscan,
|
||
BLOmodel(data_registry, BLO_factors).distribution(dscan),
|
||
kernel_bandwidth=0.1,
|
||
),
|
||
cn=BLOmodel(data_registry, BLO_factors).integrate(d_min, d_max),
|
||
)
|
||
|
||
|
||
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']
|
||
return {
|
||
'Breathing': (
|
||
param_evaluation(breathing, 'B'),
|
||
param_evaluation(breathing, 'L'),
|
||
param_evaluation(breathing, 'O')
|
||
),
|
||
'Speaking': (
|
||
param_evaluation(speaking, 'B'),
|
||
param_evaluation(speaking, 'L'),
|
||
param_evaluation(speaking, 'O')
|
||
),
|
||
'Singing': (
|
||
param_evaluation(singing, 'B'),
|
||
param_evaluation(singing, 'L'),
|
||
param_evaluation(singing, 'O')
|
||
),
|
||
'Shouting': (
|
||
param_evaluation(shouting, 'B'),
|
||
param_evaluation(shouting, 'L'),
|
||
param_evaluation(shouting, 'O')
|
||
),
|
||
}
|
||
|
||
|
||
def expiration_distributions(data_registry):
|
||
return {
|
||
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')
|
||
)
|
||
for exp_type, BLO_factors in expiration_BLO_factors(data_registry).items()
|
||
}
|
||
|
||
|
||
def short_range_expiration_distributions(data_registry):
|
||
return {
|
||
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')
|
||
)
|
||
for exp_type, BLO_factors in expiration_BLO_factors(data_registry).items()
|
||
}
|
||
|
||
|
||
# Derived from Fig 8 a) "stand-stand" in https://www.mdpi.com/1660-4601/17/4/1445/htm
|
||
distances = np.array((0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2,
|
||
1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2))
|
||
frequencies = np.array((0.0598036, 0.0946154, 0.1299152, 0.1064905, 0.1099066, 0.0998209, 0.0845298,
|
||
0.0479286, 0.0406084, 0.039795, 0.0205997, 0.0152316, 0.0118155, 0.0118155, 0.018485, 0.0205997))
|
||
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')
|
||
),
|
||
function=lambda x: np.interp(x, distances, frequencies, left=0., right=0.),
|
||
max_function=0.13
|
||
)
|