cleanup
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markus
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1 changed files with 0 additions and 215 deletions
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@ -254,221 +254,6 @@ def print_qr_info(qr_values: np.ndarray) -> None:
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for quantile in (0.01, 0.05, 0.25, 0.50, 0.75, 0.95, 0.99):
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print(f"qR_{quantile} = {np.quantile(qr_values, quantile)}")
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@dataclass(frozen=True)
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class MCVirus:
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#: Biological decay (inactivation of the virus in air)
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halflife: float
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#: RNA copies / mL
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viral_load_in_sputum: typing.Tuple[float, float]
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@property
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def decay_constant(self):
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# Viral inactivation per hour (h^-1)
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return np.log(2) / self.halflife
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@dataclass(frozen=True)
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class MCInfectedPopulation(models.Population):
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#: The virus with which the population is infected.
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virus: MCVirus
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#: An integer signifying the expiratory activity of the infected subject
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# (1 = breathing, 2 = speaking, 3 = speaking loudly)
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expiratory_activity: int
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# The total number of samples to be generated
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samples: int
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# The quantum infectious dose to be used in the calculations
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qid: int
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viral_load: typing.Optional[float] = None
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def emission_rate_when_present(self) -> np.ndarray:
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"""
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Randomly samples values for the quantum generation rate
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:return: A numpy array of length = samples, containing randomly generated qr-values
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"""
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# Extracting only the needed information from the pre-existing Mask class
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masked = self.mask.exhale_efficiency != 0
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(e_k, e_lambda), (d_k, d_lambda) = weibull_parameters[self.expiratory_activity]
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emissions = sct.weibull_min.isf(sct.norm.sf(np.random.normal(size=self.samples)), e_k, loc=0, scale=e_lambda)
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diameters = sct.weibull_min.isf(sct.norm.sf(np.random.normal(size=self.samples)), d_k, loc=0, scale=d_lambda)
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if self.viral_load is None:
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viral_loads = np.random.normal(loc=7.8, scale=1.7, size=self.samples)
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else:
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viral_loads = np.full(self.samples, self.viral_load)
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mask_efficiency = [0.75, 0.81, 0.81][self.expiratory_activity] if masked else 0
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qr_func = np.vectorize(self._calculate_qr)
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# TODO: Add distributions for parameters
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breathing_rate = 1
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return qr_func(viral_loads, emissions, diameters, mask_efficiency, self.qid)
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@staticmethod
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def _calculate_qr(viral_load: float, emission: float, diameter: float, mask_efficiency: float,
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copies_per_quantum: float, breathing_rate: typing.Optional[float] = None) -> float:
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"""
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Calculates the quantum generation rate given a set of parameters.
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"""
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# Unit conversions
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diameter *= 1e-4
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viral_load = 10 ** viral_load
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emission = (emission * 3600) if breathing_rate is None else (emission * 1e6)
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volume = (4 * np.pi * (diameter / 2) ** 3) / 3
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if breathing_rate is None:
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breathing_rate = 1
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return viral_load * emission * volume * (1 - mask_efficiency) * breathing_rate / copies_per_quantum
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def individual_emission_rate(self, time) -> np.ndarray:
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"""
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The emission rate of a single individual in the population.
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"""
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# Note: The original model avoids time dependence on the emission rate
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# at the cost of implementing a piecewise (on time) concentration function.
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if not self.person_present(time):
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return np.zeros(self.samples)
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# Note: It is essential that the value of the emission rate is not
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# itself a function of time. Any change in rate must be accompanied
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# with a declaration of state change time, as is the case for things
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# like Ventilation.
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return self.emission_rate_when_present()
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@functools.lru_cache()
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def emission_rate(self, time) -> float:
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"""
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The emission rate of the entire population.
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"""
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return self.individual_emission_rate(time) * self.number
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@dataclass(frozen=True)
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class MCConcentrationModel:
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room: models.Room
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ventilation: models.Ventilation
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infected: MCInfectedPopulation
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@property
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def virus(self):
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return self.infected.virus
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def infectious_virus_removal_rate(self, time: float) -> float:
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# Particle deposition on the floor
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vg = 1 * 10 ** -4
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# Height of the emission source to the floor - i.e. mouth/nose (m)
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h = 1.5
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# Deposition rate (h^-1)
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k = (vg * 3600) / h
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return k + self.virus.decay_constant + self.ventilation.air_exchange(self.room, time)
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@functools.lru_cache()
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def state_change_times(self):
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"""
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All time dependent entities on this model must provide information about
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the times at which their state changes.
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"""
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state_change_times = set()
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state_change_times.update(self.infected.presence.transition_times())
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state_change_times.update(self.ventilation.transition_times())
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return sorted(state_change_times)
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def last_state_change(self, time: float):
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"""
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Find the most recent state change.
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"""
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for change_time in self.state_change_times()[::-1]:
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if change_time < time:
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return change_time
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return 0
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@functools.lru_cache()
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def concentration(self, time: float) -> np.ndarray:
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if time == 0:
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return np.zeros(self.infected.samples)
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IVRR = self.infectious_virus_removal_rate(time)
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V = self.room.volume
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t_last_state_change = self.last_state_change(time)
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concentration_at_last_state_change = self.concentration(t_last_state_change)
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delta_time = time - t_last_state_change
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fac = np.exp(-IVRR * delta_time)
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concentration_limit = (self.infected.emission_rate(time)) / (IVRR * V)
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return concentration_limit * (1 - fac) + concentration_at_last_state_change * fac
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@dataclass(frozen=True)
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class MCExposureModel:
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#: The virus concentration model which this exposure model should consider.
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concentration_model: MCConcentrationModel
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#: The population of non-infected people to be used in the model.
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exposed: models.Population
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#: The number of times the exposure event is repeated (default 1).
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repeats: int = 1
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def quanta_exposure(self) -> np.ndarray:
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"""The number of virus quanta per meter^3."""
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exposure = np.zeros(self.concentration_model.infected.samples)
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for start, stop in self.exposed.presence.boundaries():
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concentrations = np.asarray([self.concentration_model.concentration(t) for t in np.linspace(start, stop)])
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integrals = np.trapz(concentrations, axis=0)
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exposure += integrals
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return exposure * self.repeats
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def infection_probability(self):
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exposure = self.quanta_exposure()
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inf_aero = (
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self.exposed.activity.inhalation_rate *
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(1 - self.exposed.mask.η_inhale) *
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exposure
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)
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# Probability of infection.
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return (1 - np.exp(-inf_aero)) * 100
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def expected_new_cases(self):
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prob = self.infection_probability()
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exposed_occupants = self.exposed.number
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return prob * exposed_occupants / 100
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# def reproduction_number(self):
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# """
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# The reproduction number can be thought of as the expected number of
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# cases directly generated by one infected case in a population.
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#
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# """
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# if self.concentration_model.infected.number == 1:
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# return self.expected_new_cases()
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#
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# # Create an equivalent exposure model but with precisely
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# # one infected case.
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# single_exposure_model = nested_replace(
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# self, {'concentration_model.infected.number': 1}
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# )
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#
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# return single_exposure_model.expected_new_cases()
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baseline_mc_exposure_model = MCExposureModel(
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concentration_model=MCConcentrationModel(
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