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Transmission xnexp

TransmissionXNExp

Bases: Transmission

Source code in june/epidemiology/infection/transmission_xnexp.py 
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class TransmissionXNExp(Transmission):
    """ """
    __slots__ = (
        "time_first_infectious",
        "norm_time",
        "n",
        "alpha",
        "norm",
        "probability",
    )

    def __init__(
        self,
        max_probability: float = 1.0,
        time_first_infectious: float = 2.6,
        norm_time: float = 1.0,
        n: float = 1.0,
        alpha: float = 5.0,
        max_symptoms: str = None,
        asymptomatic_infectious_factor: float = None,
        mild_infectious_factor: float = None,
    ):
        """
        Class that defines the time profile of the infectiousness to be of the form x^n exp(-x/alpha)

        Parameters
        ----------
        max_probability:
            value of the infectiousness at its peak. Used to control the number of super spreaders
        time_first_infectious:
            time at which the person becomes infectious
        norm_time:
            controls the definition of x, x = (time_from_infection - time-first_infectious)/norm_time
        n:
            exponent of x in the x^n exp(-x/alpha) function
        alpha:
            denominator in exponential
        max_symptoms:
            maximum symptoms that the person will ever have, used to lower the infectiousness of
            asymptomatic and mild cases
        asymptomatic_infectious_factor:
            multiplier that lowers the infectiousness of asymptomatic cases
        mild_infectious_factor:
            multiplier that lowers the infectiousness of mild cases

        """
        self.time_first_infectious = time_first_infectious
        self.norm_time = norm_time
        self.n = n
        self.alpha = alpha
        max_delta_time = self.n * self.alpha * self.norm_time
        max_tau = max_delta_time / self.norm_time
        self.norm = max_probability / xnexp(max_tau, self.n, self.alpha)
        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,
        time_first_infectious: float,
        n: float,
        alpha: float,
        max_symptoms: "SymptomTag" = None,
        config_path: str = default_config_path,
    ) -> "TransmissionXNExp":
        """Generates transmission class from config file

        Args:
            time_first_infectious (float): time at which the person becomes infectious
            n (float): exponent of x in the x^n exp(-x/alpha) function
            alpha (float): denominator in exponential
            max_symptoms ("SymptomTag", optional): maximum symptoms that the person will ever have, used to lower the infectiousness of
        asymptomatic and mild cases (Default value = None)
            config_path (str, optional): (Default value = default_config_path)

        """
        with open(config_path) as f:
            config = yaml.safe_load(f)
        max_probability = CompletionTime.from_dict(config["max_probability"])()
        norm_time = CompletionTime.from_dict(config["norm_time"])()
        asymptomatic_infectious_factor = CompletionTime.from_dict(
            config["asymptomatic_infectious_factor"]
        )()
        mild_infectious_factor = CompletionTime.from_dict(
            config["mild_infectious_factor"]
        )()
        return TransmissionXNExp(
            max_probability=max_probability,
            time_first_infectious=time_first_infectious,
            norm_time=norm_time,
            n=n,
            alpha=alpha,
            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: "SymptomTag" = None,
        config_path: str = default_config_path,
    ) -> "TransmissionXNExp":
        """Generates transmission class from config file

        Args:
            time_to_symptoms_onset (float): 
            max_symptoms ("SymptomTag", optional): maximum symptoms that the person will ever have, used to lower the infectiousness of
        asymptomatic and mild cases (Default value = None)
            config_path (str, optional): (Default value = default_config_path)

        """
        with open(config_path) as f:
            config = yaml.safe_load(f)
        smearing_time_first_infectious = CompletionTime.from_dict(
            config["smearing_time_first_infectious"]
        )()
        time_first_infectious = time_to_symptoms_onset + smearing_time_first_infectious
        smearing_peak_position = CompletionTime.from_dict(
            config["smearing_peak_position"]
        )()
        alpha = CompletionTime.from_dict(config["alpha"])()
        peak_position = (
            time_to_symptoms_onset - time_first_infectious + smearing_peak_position
        )
        n = peak_position / alpha
        max_probability = CompletionTime.from_dict(config["max_probability"])()
        norm_time = CompletionTime.from_dict(config["norm_time"])()
        asymptomatic_infectious_factor = CompletionTime.from_dict(
            config["asymptomatic_infectious_factor"]
        )()
        mild_infectious_factor = CompletionTime.from_dict(
            config["mild_infectious_factor"]
        )()
        return TransmissionXNExp(
            max_probability=max_probability,
            time_first_infectious=time_first_infectious,
            norm_time=norm_time,
            n=n,
            alpha=alpha,
            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 (in days).

        """
        self.probability = update_probability(
            time_from_infection,
            self.time_first_infectious,
            self.norm,
            self.norm_time,
            self.alpha,
            self.n,
        )

    def _modify_infectiousness_for_symptoms(
        self, max_symptoms: str, asymptomatic_infectious_factor, mild_infectious_factor
    ):
        """Lowers the infectiousness of asymptomatic and mild cases, by modifying
        self.norm

        Args:
            max_symptoms (str): maximum symptom severity the person will ever have
            asymptomatic_infectious_factor: 
            mild_infectious_factor: 

        """
        if (
            asymptomatic_infectious_factor is not None
            and max_symptoms == "asymptomatic"
        ):
            self.norm *= asymptomatic_infectious_factor
        elif mild_infectious_factor is not None and max_symptoms == "mild":
            self.norm *= mild_infectious_factor

__init__(max_probability=1.0, time_first_infectious=2.6, norm_time=1.0, n=1.0, alpha=5.0, max_symptoms=None, asymptomatic_infectious_factor=None, mild_infectious_factor=None)

Class that defines the time profile of the infectiousness to be of the form x^n exp(-x/alpha)

Parameters

max_probability: value of the infectiousness at its peak. Used to control the number of super spreaders time_first_infectious: time at which the person becomes infectious norm_time: controls the definition of x, x = (time_from_infection - time-first_infectious)/norm_time n: exponent of x in the x^n exp(-x/alpha) function alpha: denominator in exponential max_symptoms: maximum symptoms that the person will ever have, used to lower the infectiousness of asymptomatic and mild cases asymptomatic_infectious_factor: multiplier that lowers the infectiousness of asymptomatic cases mild_infectious_factor: multiplier that lowers the infectiousness of mild cases

Source code in june/epidemiology/infection/transmission_xnexp.py 
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def __init__(
    self,
    max_probability: float = 1.0,
    time_first_infectious: float = 2.6,
    norm_time: float = 1.0,
    n: float = 1.0,
    alpha: float = 5.0,
    max_symptoms: str = None,
    asymptomatic_infectious_factor: float = None,
    mild_infectious_factor: float = None,
):
    """
    Class that defines the time profile of the infectiousness to be of the form x^n exp(-x/alpha)

    Parameters
    ----------
    max_probability:
        value of the infectiousness at its peak. Used to control the number of super spreaders
    time_first_infectious:
        time at which the person becomes infectious
    norm_time:
        controls the definition of x, x = (time_from_infection - time-first_infectious)/norm_time
    n:
        exponent of x in the x^n exp(-x/alpha) function
    alpha:
        denominator in exponential
    max_symptoms:
        maximum symptoms that the person will ever have, used to lower the infectiousness of
        asymptomatic and mild cases
    asymptomatic_infectious_factor:
        multiplier that lowers the infectiousness of asymptomatic cases
    mild_infectious_factor:
        multiplier that lowers the infectiousness of mild cases

    """
    self.time_first_infectious = time_first_infectious
    self.norm_time = norm_time
    self.n = n
    self.alpha = alpha
    max_delta_time = self.n * self.alpha * self.norm_time
    max_tau = max_delta_time / self.norm_time
    self.norm = max_probability / xnexp(max_tau, self.n, self.alpha)
    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(time_first_infectious, n, alpha, max_symptoms=None, config_path=default_config_path) classmethod

Generates transmission class from config file

Parameters:

Name Type Description Default
time_first_infectious float

time at which the person becomes infectious

 required
n float

exponent of x in the x^n exp(-x/alpha) function

 required
alpha float

denominator in exponential

 required
max_symptoms SymptomTag

maximum symptoms that the person will ever have, used to lower the infectiousness of

 None

asymptomatic and mild cases (Default value = None) config_path (str, optional): (Default value = default_config_path)

Source code in june/epidemiology/infection/transmission_xnexp.py 
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@classmethod
def from_file(
    cls,
    time_first_infectious: float,
    n: float,
    alpha: float,
    max_symptoms: "SymptomTag" = None,
    config_path: str = default_config_path,
) -> "TransmissionXNExp":
    """Generates transmission class from config file

    Args:
        time_first_infectious (float): time at which the person becomes infectious
        n (float): exponent of x in the x^n exp(-x/alpha) function
        alpha (float): denominator in exponential
        max_symptoms ("SymptomTag", optional): maximum symptoms that the person will ever have, used to lower the infectiousness of
    asymptomatic and mild cases (Default value = None)
        config_path (str, optional): (Default value = default_config_path)

    """
    with open(config_path) as f:
        config = yaml.safe_load(f)
    max_probability = CompletionTime.from_dict(config["max_probability"])()
    norm_time = CompletionTime.from_dict(config["norm_time"])()
    asymptomatic_infectious_factor = CompletionTime.from_dict(
        config["asymptomatic_infectious_factor"]
    )()
    mild_infectious_factor = CompletionTime.from_dict(
        config["mild_infectious_factor"]
    )()
    return TransmissionXNExp(
        max_probability=max_probability,
        time_first_infectious=time_first_infectious,
        norm_time=norm_time,
        n=n,
        alpha=alpha,
        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_config_path) classmethod

Generates transmission class from config file

Parameters:

Name Type Description Default
time_to_symptoms_onset float
required
max_symptoms SymptomTag

maximum symptoms that the person will ever have, used to lower the infectiousness of

 None

asymptomatic and mild cases (Default value = None) config_path (str, optional): (Default value = default_config_path)

Source code in june/epidemiology/infection/transmission_xnexp.py 
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@classmethod
def from_file_linked_symptoms(
    cls,
    time_to_symptoms_onset: float,
    max_symptoms: "SymptomTag" = None,
    config_path: str = default_config_path,
) -> "TransmissionXNExp":
    """Generates transmission class from config file

    Args:
        time_to_symptoms_onset (float): 
        max_symptoms ("SymptomTag", optional): maximum symptoms that the person will ever have, used to lower the infectiousness of
    asymptomatic and mild cases (Default value = None)
        config_path (str, optional): (Default value = default_config_path)

    """
    with open(config_path) as f:
        config = yaml.safe_load(f)
    smearing_time_first_infectious = CompletionTime.from_dict(
        config["smearing_time_first_infectious"]
    )()
    time_first_infectious = time_to_symptoms_onset + smearing_time_first_infectious
    smearing_peak_position = CompletionTime.from_dict(
        config["smearing_peak_position"]
    )()
    alpha = CompletionTime.from_dict(config["alpha"])()
    peak_position = (
        time_to_symptoms_onset - time_first_infectious + smearing_peak_position
    )
    n = peak_position / alpha
    max_probability = CompletionTime.from_dict(config["max_probability"])()
    norm_time = CompletionTime.from_dict(config["norm_time"])()
    asymptomatic_infectious_factor = CompletionTime.from_dict(
        config["asymptomatic_infectious_factor"]
    )()
    mild_infectious_factor = CompletionTime.from_dict(
        config["mild_infectious_factor"]
    )()
    return TransmissionXNExp(
        max_probability=max_probability,
        time_first_infectious=time_first_infectious,
        norm_time=norm_time,
        n=n,
        alpha=alpha,
        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 (in days).

 required
Source code in june/epidemiology/infection/transmission_xnexp.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 (in days).

    """
    self.probability = update_probability(
        time_from_infection,
        self.time_first_infectious,
        self.norm,
        self.norm_time,
        self.alpha,
        self.n,
    )

update_probability(time_from_infection, time_first_infectious, norm, norm_time, alpha, n)

Determines how the infectiousness profile is updated over time

Parameters:

Name Type Description Default
time_from_infection float

time from infection

 required
time_first_infectious float

time from infection at which the person becomes infectious

 required
norm float

multiplier to the infectiousness profile

 required
norm_time float

controls the definition of tau

 required
alpha float

demominator in exponential for xnexp function

 required
n float

exponent of x in xnexp

 required
Source code in june/epidemiology/infection/transmission_xnexp.py 
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@nb.jit(nopython=True)
def update_probability(
    time_from_infection: float,
    time_first_infectious: float,
    norm: float,
    norm_time: float,
    alpha: float,
    n: float,
) -> float:
    """Determines how the infectiousness profile is updated over time

    Args:
        time_from_infection (float): time from infection
        time_first_infectious (float): time from infection at which the person becomes infectious
        norm (float): multiplier to the infectiousness profile
        norm_time (float): controls the definition of tau
        alpha (float): demominator in exponential for xnexp function
        n (float): exponent of x in xnexp

    """

    if time_from_infection > time_first_infectious:
        delta_tau = (time_from_infection - time_first_infectious) / norm_time
        return norm * xnexp(x=delta_tau, n=n, alpha=alpha)
    else:
        return 0.0

xnexp(x, n, alpha)

Implementation of x^n exp(-x/alpha)

Parameters:

Name Type Description Default
x float

x variable

 required
n float

exponent of x

 required
alpha float

denominator in exponential

 required
Source code in june/epidemiology/infection/transmission_xnexp.py 
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@nb.jit(nopython=True)
def xnexp(x: float, n: float, alpha: float) -> float:
    """Implementation of x^n exp(-x/alpha)

    Args:
        x (float): x variable
        n (float): exponent of x
        alpha (float): denominator in exponential

    """
    return x**n * np.exp(-x / alpha)