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Transmission

Transmission

Source code in june/epidemiology/infection/transmission.py 
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class Transmission:
    """ """
    __slots__ = "probability"

    def __init__(self):
        self.probability = 0.0

    def update_infection_probability(self, time_from_infection):
        """

        Args:
            time_from_infection: 

        """
        raise NotImplementedError()

update_infection_probability(time_from_infection)

Parameters:

Name Type Description Default
time_from_infection
required
Source code in june/epidemiology/infection/transmission.py 
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def update_infection_probability(self, time_from_infection):
    """

    Args:
        time_from_infection: 

    """
    raise NotImplementedError()

TransmissionConstant

Bases: Transmission

Source code in june/epidemiology/infection/transmission.py 
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class TransmissionConstant(Transmission):
    """ """
    def __init__(self, probability=0.3):
        super().__init__()
        self.probability = probability

    @classmethod
    def from_file(
        cls, config_path: str = default_config_path
    ) -> "TransmissionConstant":
        """

        Args:
            config_path (str, optional): (Default value = default_config_path)

        """
        with open(config_path) as f:
            config = yaml.safe_load(f)
        probability = CompletionTime.from_dict(config["probability"])()
        return TransmissionConstant(probability=probability)

    def update_infection_probability(self, time_from_infection):
        """

        Args:
            time_from_infection: 

        """
        pass

from_file(config_path=default_config_path) classmethod

Parameters:

Name Type Description Default
config_path str

(Default value = default_config_path)

 default_config_path
Source code in june/epidemiology/infection/transmission.py 
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@classmethod
def from_file(
    cls, config_path: str = default_config_path
) -> "TransmissionConstant":
    """

    Args:
        config_path (str, optional): (Default value = default_config_path)

    """
    with open(config_path) as f:
        config = yaml.safe_load(f)
    probability = CompletionTime.from_dict(config["probability"])()
    return TransmissionConstant(probability=probability)

update_infection_probability(time_from_infection)

Parameters:

Name Type Description Default
time_from_infection
required
Source code in june/epidemiology/infection/transmission.py 
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def update_infection_probability(self, time_from_infection):
    """

    Args:
        time_from_infection: 

    """
    pass

TransmissionGamma

Bases: Transmission

Module to simulate the infectiousness profiles found in : - https://www.nature.com/articles/s41591-020-0869-5 - https://arxiv.org/pdf/2007.06602.pdf

Source code in june/epidemiology/infection/transmission.py 
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class TransmissionGamma(Transmission):
    """Module to simulate the infectiousness profiles found in :
        - https://www.nature.com/articles/s41591-020-0869-5
        - https://arxiv.org/pdf/2007.06602.pdf

    """

    __slots__ = ("shape", "shift", "scale", "norm", "probability")

    def __init__(
        self,
        max_infectiousness: float = 1.0,
        shape: float = 2.0,
        rate: float = 3.0,
        shift: float = -2.0,
        max_symptoms: Optional[str] = None,
        asymptomatic_infectious_factor: Optional[float] = None,
        mild_infectious_factor: Optional[float] = None,
    ):
        """
        Parameters
        ----------
        max_infectiousness:
            value of the infectiousness at its peak
        shape:
            shape parameter of the gamma distribution (a for scipy stats)
        rate:
            rate parameter of the gamma distribution (1/rate = scale for scipy stats)
        shift:
            location parameter of the gamma distribution
        max_symptoms:
            maximum symptoms the individual will develop, used to reduce the infectiousness
            of asymptomatic and mild individuals if wanted
        asymptomatic_infectious_factor:
            factor to reduce the infectiousness of asymptomatic individuals
        mild_infectious_factor:
            factor to reduce the infectiousness of mild individuals
        """
        self.shape = shape
        self.shift = shift
        self.scale = 1.0 / rate
        self.norm = max_infectiousness
        if (
            asymptomatic_infectious_factor is not None
            and mild_infectious_factor is not None
        ):
            self.norm *= self._modify_infectiousness_for_symptoms(
                max_symptoms=max_symptoms,
                asymptomatic_infectious_factor=asymptomatic_infectious_factor,
                mild_infectious_factor=mild_infectious_factor,
            )
        self.probability = 0.0

    @classmethod
    def from_file(
        cls, max_symptoms: str = None, config_path: str = default_gamma_config_path
    ) -> "TransmissionGamma":
        """Generate transmission class reading parameters from config file

        Args:
            max_symptoms (str, optional): maximum symptoms the individual will develop, used to reduce the infectiousness
        of asymptomatic and mild individuals if wanted (Default value = None)
            config_path (str, optional): path to config parameters (Default value = default_gamma_config_path)

        """
        with open(config_path) as f:
            config = yaml.safe_load(f)
        max_infectiousness = CompletionTime.from_dict(config["max_infectiousness"])()
        shape = CompletionTime.from_dict(config["shape"])()
        rate = CompletionTime.from_dict(config["rate"])()
        shift = CompletionTime.from_dict(config["shift"])()
        asymptomatic_infectious_factor = CompletionTime.from_dict(
            config["asymptomatic_infectious_factor"]
        )()
        mild_infectious_factor = CompletionTime.from_dict(
            config["mild_infectious_factor"]
        )()

        return cls(
            max_infectiousness=max_infectiousness,
            shape=shape,
            rate=rate,
            shift=shift,
            max_symptoms=max_symptoms,
            asymptomatic_infectious_factor=asymptomatic_infectious_factor,
            mild_infectious_factor=mild_infectious_factor,
        )

    @classmethod
    def from_file_linked_symptoms(
        cls,
        time_to_symptoms_onset: float,
        max_symptoms: str = None,
        config_path: str = default_gamma_config_path,
    ) -> "TransmissionGamma":
        """Generate transmission class reading parameters from config file, linked to
        the time of symptoms onset

        Args:
            time_to_symptoms_onset (float): time (from infection) at which the person becomes symptomatic
            max_symptoms (str, optional): maximum symptoms the individual will develop, used to reduce the infectiousness
        of asymptomatic and mild individuals if wanted (Default value = None)
            config_path (str, optional): path to config parameters (Default value = default_gamma_config_path)

        """

        with open(config_path) as f:
            config = yaml.safe_load(f)
        max_infectiousness = CompletionTime.from_dict(config["max_infectiousness"])()
        shape = CompletionTime.from_dict(config["shape"])()
        rate = CompletionTime.from_dict(config["rate"])()
        shift = CompletionTime.from_dict(config["shift"])() + time_to_symptoms_onset
        asymptomatic_infectious_factor = CompletionTime.from_dict(
            config["asymptomatic_infectious_factor"]
        )()
        mild_infectious_factor = CompletionTime.from_dict(
            config["mild_infectious_factor"]
        )()

        return cls(
            max_infectiousness=max_infectiousness,
            shape=shape,
            rate=rate,
            shift=shift,
            max_symptoms=max_symptoms,
            asymptomatic_infectious_factor=asymptomatic_infectious_factor,
            mild_infectious_factor=mild_infectious_factor,
        )

    def update_infection_probability(self, time_from_infection: float):
        """Performs a probability update given time from infection

        Args:
            time_from_infection (float): time elapsed since person became infected

        """
        self.probability = self.norm * gamma_pdf(
            x=time_from_infection, a=self.shape, loc=self.shift, scale=self.scale
        )

    @property
    def time_at_maximum_infectivity(self) -> float:
        """Computes the time at which the individual is maximally infectious (in this case for
        a gamma distribution


        Returns:
            : time at maximal infectiousness

        """
        return (self.shape - 1) * self.scale + self.shift

    def _modify_infectiousness_for_symptoms(
        self,
        max_symptoms: str,
        asymptomatic_infectious_factor=None,
        mild_infectious_factor=None,
    ):
        """Lowers the infectiousness of asymptomatic and mild cases, by modifying
        the norm of the distribution

        Args:
            max_symptoms (str): maximum symptom severity the person will ever have
            asymptomatic_infectious_factor: factor to reduce the infectiousness of asymptomatic individuals (Default value = None)
            mild_infectious_factor: factor to reduce the infectiousness of mild individuals (Default value = None)

        """
        if (
            asymptomatic_infectious_factor is not None
            and max_symptoms == "asymptomatic"
        ):
            return asymptomatic_infectious_factor
        elif mild_infectious_factor is not None and max_symptoms == "mild":
            return mild_infectious_factor
        return 1.0

time_at_maximum_infectivity property

Computes the time at which the individual is maximally infectious (in this case for a gamma distribution

Returns:

Type Description
float

time at maximal infectiousness

__init__(max_infectiousness=1.0, shape=2.0, rate=3.0, shift=-2.0, max_symptoms=None, asymptomatic_infectious_factor=None, mild_infectious_factor=None)

Parameters

max_infectiousness: value of the infectiousness at its peak shape: shape parameter of the gamma distribution (a for scipy stats) rate: rate parameter of the gamma distribution (1/rate = scale for scipy stats) shift: location parameter of the gamma distribution max_symptoms: maximum symptoms the individual will develop, used to reduce the infectiousness of asymptomatic and mild individuals if wanted asymptomatic_infectious_factor: factor to reduce the infectiousness of asymptomatic individuals mild_infectious_factor: factor to reduce the infectiousness of mild individuals

Source code in june/epidemiology/infection/transmission.py 
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def __init__(
    self,
    max_infectiousness: float = 1.0,
    shape: float = 2.0,
    rate: float = 3.0,
    shift: float = -2.0,
    max_symptoms: Optional[str] = None,
    asymptomatic_infectious_factor: Optional[float] = None,
    mild_infectious_factor: Optional[float] = None,
):
    """
    Parameters
    ----------
    max_infectiousness:
        value of the infectiousness at its peak
    shape:
        shape parameter of the gamma distribution (a for scipy stats)
    rate:
        rate parameter of the gamma distribution (1/rate = scale for scipy stats)
    shift:
        location parameter of the gamma distribution
    max_symptoms:
        maximum symptoms the individual will develop, used to reduce the infectiousness
        of asymptomatic and mild individuals if wanted
    asymptomatic_infectious_factor:
        factor to reduce the infectiousness of asymptomatic individuals
    mild_infectious_factor:
        factor to reduce the infectiousness of mild individuals
    """
    self.shape = shape
    self.shift = shift
    self.scale = 1.0 / rate
    self.norm = max_infectiousness
    if (
        asymptomatic_infectious_factor is not None
        and mild_infectious_factor is not None
    ):
        self.norm *= self._modify_infectiousness_for_symptoms(
            max_symptoms=max_symptoms,
            asymptomatic_infectious_factor=asymptomatic_infectious_factor,
            mild_infectious_factor=mild_infectious_factor,
        )
    self.probability = 0.0

from_file(max_symptoms=None, config_path=default_gamma_config_path) classmethod

Generate transmission class reading parameters from config file

Parameters:

Name Type Description Default
max_symptoms str

maximum symptoms the individual will develop, used to reduce the infectiousness

 None

of asymptomatic and mild individuals if wanted (Default value = None) config_path (str, optional): path to config parameters (Default value = default_gamma_config_path)

Source code in june/epidemiology/infection/transmission.py 
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@classmethod
def from_file(
    cls, max_symptoms: str = None, config_path: str = default_gamma_config_path
) -> "TransmissionGamma":
    """Generate transmission class reading parameters from config file

    Args:
        max_symptoms (str, optional): maximum symptoms the individual will develop, used to reduce the infectiousness
    of asymptomatic and mild individuals if wanted (Default value = None)
        config_path (str, optional): path to config parameters (Default value = default_gamma_config_path)

    """
    with open(config_path) as f:
        config = yaml.safe_load(f)
    max_infectiousness = CompletionTime.from_dict(config["max_infectiousness"])()
    shape = CompletionTime.from_dict(config["shape"])()
    rate = CompletionTime.from_dict(config["rate"])()
    shift = CompletionTime.from_dict(config["shift"])()
    asymptomatic_infectious_factor = CompletionTime.from_dict(
        config["asymptomatic_infectious_factor"]
    )()
    mild_infectious_factor = CompletionTime.from_dict(
        config["mild_infectious_factor"]
    )()

    return cls(
        max_infectiousness=max_infectiousness,
        shape=shape,
        rate=rate,
        shift=shift,
        max_symptoms=max_symptoms,
        asymptomatic_infectious_factor=asymptomatic_infectious_factor,
        mild_infectious_factor=mild_infectious_factor,
    )

from_file_linked_symptoms(time_to_symptoms_onset, max_symptoms=None, config_path=default_gamma_config_path) classmethod

Generate transmission class reading parameters from config file, linked to the time of symptoms onset

Parameters:

Name Type Description Default
time_to_symptoms_onset float

time (from infection) at which the person becomes symptomatic

 required
max_symptoms str

maximum symptoms the individual will develop, used to reduce the infectiousness

 None

of asymptomatic and mild individuals if wanted (Default value = None) config_path (str, optional): path to config parameters (Default value = default_gamma_config_path)

Source code in june/epidemiology/infection/transmission.py 
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@classmethod
def from_file_linked_symptoms(
    cls,
    time_to_symptoms_onset: float,
    max_symptoms: str = None,
    config_path: str = default_gamma_config_path,
) -> "TransmissionGamma":
    """Generate transmission class reading parameters from config file, linked to
    the time of symptoms onset

    Args:
        time_to_symptoms_onset (float): time (from infection) at which the person becomes symptomatic
        max_symptoms (str, optional): maximum symptoms the individual will develop, used to reduce the infectiousness
    of asymptomatic and mild individuals if wanted (Default value = None)
        config_path (str, optional): path to config parameters (Default value = default_gamma_config_path)

    """

    with open(config_path) as f:
        config = yaml.safe_load(f)
    max_infectiousness = CompletionTime.from_dict(config["max_infectiousness"])()
    shape = CompletionTime.from_dict(config["shape"])()
    rate = CompletionTime.from_dict(config["rate"])()
    shift = CompletionTime.from_dict(config["shift"])() + time_to_symptoms_onset
    asymptomatic_infectious_factor = CompletionTime.from_dict(
        config["asymptomatic_infectious_factor"]
    )()
    mild_infectious_factor = CompletionTime.from_dict(
        config["mild_infectious_factor"]
    )()

    return cls(
        max_infectiousness=max_infectiousness,
        shape=shape,
        rate=rate,
        shift=shift,
        max_symptoms=max_symptoms,
        asymptomatic_infectious_factor=asymptomatic_infectious_factor,
        mild_infectious_factor=mild_infectious_factor,
    )

update_infection_probability(time_from_infection)

Performs a probability update given time from infection

Parameters:

Name Type Description Default
time_from_infection float

time elapsed since person became infected

 required
Source code in june/epidemiology/infection/transmission.py 
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def update_infection_probability(self, time_from_infection: float):
    """Performs a probability update given time from infection

    Args:
        time_from_infection (float): time elapsed since person became infected

    """
    self.probability = self.norm * gamma_pdf(
        x=time_from_infection, a=self.shape, loc=self.shift, scale=self.scale
    )

gamma_pdf(x, a, loc, scale)

Implementation of gamma PDF in numba

Parameters:

Name Type Description Default
x float

x variable

 required
a float

shape factor

 required
loc float

denominator in exponential

 required
scale float
required
Source code in june/epidemiology/infection/transmission.py 
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@nb.jit(nopython=True)
def gamma_pdf(x: float, a: float, loc: float, scale: float) -> float:
    """Implementation of gamma PDF in numba

    Args:
        x (float): x variable
        a (float): shape factor
        loc (float): denominator in exponential
        scale (float): 

    """
    if x < loc:
        return 0.0
    return (
        1.0
        / gamma(a)
        * ((x - loc) / scale) ** (a - 1)
        * np.exp(-(x - loc) / scale)
        / scale
    )

gamma_pdf_vectorised(x, a, loc, scale)

Implementation of gamma PDF in numba

Parameters:

Name Type Description Default
x float

x variable

 required
a float

shape factor

 required
loc float

denominator in exponential

 required
scale float
required
Source code in june/epidemiology/infection/transmission.py 
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@nb.jit(nopython=True)
def gamma_pdf_vectorised(x: float, a: float, loc: float, scale: float) -> float:
    """Implementation of gamma PDF in numba

    Args:
        x (float): x variable
        a (float): shape factor
        loc (float): denominator in exponential
        scale (float): 

    """
    return np.where(
        x < loc,
        0.0,
        1.0
        / gamma(a)
        * ((x - loc) / scale) ** (a - 1)
        * np.exp(-(x - loc) / scale)
        / scale,
    )