Merge branch 'feature/activity_and_viral_loads_distributions' into 'feature/MonteCarlo'
activity and viral loads pre-defined distributions See merge request cara/cara!193
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Nicolas Mounet
commit
bab0c877ec
3 changed files with 105 additions and 0 deletions
56
cara/monte_carlo/data.py
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56
cara/monte_carlo/data.py
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import numpy as np
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import cara.monte_carlo as mc
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from cara.monte_carlo.sampleable import Normal,LogNormal,LogCustomKernel
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# From CERN-OPEN-2021-04 and refererences therein
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activity_distributions = {
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'Seated': mc.Activity(LogNormal(-0.6872121723362303, 0.10498338229297108),
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LogNormal(-0.6872121723362303, 0.10498338229297108)),
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'Standing': mc.Activity(LogNormal(-0.5742377578494785, 0.09373162411398223),
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LogNormal(-0.5742377578494785, 0.09373162411398223)),
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'Light activity': mc.Activity(LogNormal(0.21380242785625422,0.09435378091059601),
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LogNormal(0.21380242785625422,0.09435378091059601)),
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'Moderate activity': mc.Activity(LogNormal(0.551771330362601, 0.1894616357138137),
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LogNormal(0.551771330362601, 0.1894616357138137)),
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'Heavy exercise': mc.Activity(LogNormal(1.1644665696723049, 0.21744554768657565),
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LogNormal(1.1644665696723049, 0.21744554768657565)),
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}
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# From CERN-OPEN-2021-04 and refererences 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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# From CERN-OPEN-2021-04 and refererences therein
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virus_distributions = {
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'SARS_CoV_2': mc.SARSCoV2(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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quantum_infectious_dose=100,
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),
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'SARS_CoV_2_B117': mc.SARSCoV2(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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quantum_infectious_dose=60,
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),
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'SARS_CoV_2_P1': mc.SARSCoV2(
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viral_load_in_sputum=symptomatic_vl_frequencies,
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quantum_infectious_dose=100/2.25,
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),
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}
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47
cara/tests/test_predefined_distributions.py
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cara/tests/test_predefined_distributions.py
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import numpy as np
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import numpy.testing as npt
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import pytest
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from cara.monte_carlo.data import activity_distributions, virus_distributions
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# TODO: seed better the random number generators
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np.random.seed(0)
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# mean & std deviations from CERN-OPEN-2021-04 (Table 4)
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# NOTE: a mistake was corrected for the std deviation of the
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# "Moderate exercise" case (0.37 in the report, but should be 0.34)
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@pytest.mark.parametrize(
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"distribution, mean, std",[
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['Seated', 0.51, 0.053],
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['Standing', 0.57, 0.053],
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['Light activity', 1.24, 0.12],
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['Moderate activity', 1.77, 0.34],
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['Heavy exercise', 3.28, 0.72,],
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]
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)
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def test_activity_distributions(distribution, mean, std):
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activity = activity_distributions[distribution].build_model(size=1000000)
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npt.assert_allclose(activity.inhalation_rate.mean(), mean, atol=0.01)
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npt.assert_allclose(activity.inhalation_rate.std(), std, atol=0.01)
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# mean & std deviations from CERN-OPEN-2021-04 (Table 4) - with a refined
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# precision on the values
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@pytest.mark.parametrize(
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"distribution, mean, std",[
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['SARS_CoV_2', 6.59, 1.74],
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['SARS_CoV_2_B117', 6.59, 1.74],
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['SARS_CoV_2_P1', 6.59, 1.74],
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]
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)
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def test_viral_load_logdistribution(distribution, mean, std):
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virus = virus_distributions[distribution].build_model(size=1000000)
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npt.assert_allclose(np.log10(virus.viral_load_in_sputum).mean(), mean, atol=0.01)
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npt.assert_allclose(np.log10(virus.viral_load_in_sputum).std(), std, atol=0.01)
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@ -3,9 +3,11 @@ import numpy.testing as npt
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import pytest
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from cara.monte_carlo import sampleable
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# TODO: seed better the random number generators
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np.random.seed(2000)
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@pytest.mark.parametrize(
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"mean, std",[
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[1., 0.5],
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