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Removing pre-defined distributions; removing coefficient_of_infectivity

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This commit is contained in:
Nicolas Mounet 2021-05-25 16:25:30 +02:00
parent 19d25310dc
commit 3e9652d012
1 changed files with 2 additions and 39 deletions
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41
cara/models.py
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@ -425,19 +425,11 @@ class Virus:
#: Pre-populated examples of Viruses.
types: typing.ClassVar[typing.Dict[str, "Virus"]]
#: Pre-defined examples of virus distributions.
distributions: typing.ClassVar[typing.Dict[str, typing.Callable[[int], "Virus"]]]
@property
def decay_constant(self) -> _VectorisedFloat:
# Viral inactivation per hour (h^-1)
return np.log(2) / self.halflife
@property
def coefficient_of_infectivity(self) -> _VectorisedFloat:
# Ratio between infectious aerosols and dose to cause infection.
return 1/self.qID
Virus.types = {
'SARS_CoV_2': Virus(
@ -461,35 +453,6 @@ Virus.types = {
),
}
@cached
def _generate_virus_distribution(params: typing.Tuple[int, float]) -> Virus:
samples , qID = params
log_symptomatic_vl_frequencies = ((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),
(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))
kde_model = KernelDensity(kernel='gaussian', bandwidth=0.1)
kde_model.fit(np.asarray(log_symptomatic_vl_frequencies)[0, :][:, np.newaxis],
sample_weight=np.asarray(log_symptomatic_vl_frequencies)[1, :])
viral_load_distribution = 10 ** kde_model.sample(n_samples=samples)[:, 0]
return Virus(
halflife=1.1,
viral_load_in_sputum=viral_load_distribution,
qID=qID,
)
Virus.distributions = {
'SARS_CoV_2': lambda n: _generate_virus_distribution((n, 100)),
'SARS_CoV_2_B117': lambda n: _generate_virus_distribution((n, 60)),
'SARS_CoV_2_P1': lambda n: _generate_virus_distribution((n, 100/2.25)),
}
@dataclass(frozen=True)
class Mask:
@ -621,10 +584,10 @@ class InfectedPopulation(Population):
aerosols = self.expiration.aerosols(self.mask)
ER = (self.virus.viral_load_in_sputum *
self.virus.coefficient_of_infectivity *
self.activity.exhalation_rate *
10 ** 6 *
aerosols)
aerosols /
self.virus.qID)
# For superspreading event, where ejection_factor is infinite we fix the ER
# based on Miller et al. (2020).