updated half-life formula and respective tests
This commit is contained in:
Luis Aleixo
parent
f518a893d5
commit
1e689af19c
6 changed files with 43 additions and 41 deletions
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@ -459,14 +459,15 @@ class SARSCoV2(Virus):
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CERN-OPEN-2021-004, DOI: 10.17181/CERN.1GDQ.5Y75)
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"""
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# Updated to use the formula from Dabish et al. with correction https://doi.org/10.1080/02786826.2020.1829536
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# with a maximum at hl = 6.43. Note that humidity is in percentage and inside_temp in °C.
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hl_calc = ((0.693/((0.16030 + 0.04018*(((inside_temp-273.15)-20.615)/10.585)
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# with a maximum at hl = 6.43 (compensate for the negative decay values in the paper).
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# Note that humidity is in percentage and inside_temp in °C.
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# factor np.log(2) -> decay rate to half-life; factor 60 -> minutes to hours
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hl_calc = ((np.log(2)/((0.16030 + 0.04018*(((inside_temp-273.15)-20.615)/10.585)
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+0.02176*(((humidity*100)-45.235)/28.665)
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-0.14369
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-0.02636*((inside_temp-273.15)-20.615)/10.585)))/60)
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if (hl_calc <= 0):
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hl_calc = 6.43
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return hl_calc
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return np.where(hl_calc <= 0, 6.43, np.minimum(6.43, hl_calc))
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Virus.types = {
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@ -179,12 +179,12 @@ def sr_model():
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@pytest.mark.parametrize(
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["exposed_time_interval", "expected_deposited_exposure"],
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[
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[(0., 1.), 49.60355486823376],
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[(1., 1.01), 0.5876509666617122],
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[(1.01, 1.02), 0.5726560233646302],
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[(12., 12.01), 0.016288631412456556],
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[(12., 24.), 716.6229828782525],
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[(0., 24.), 777.8717785392312],
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[(0., 1.), 42.63222033436878],
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[(1., 1.01), 0.485377549596179],
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[(1.01, 1.02), 0.47058239520823814],
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[(12., 12.01), 0.01622776617499709],
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[(12., 24.), 595.1115223695439],
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[(0., 24.), 645.8401125684933],
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]
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)
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def test_exposure_model_integral_accuracy(exposed_time_interval,
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@ -7,18 +7,18 @@ from cara import models
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@pytest.mark.parametrize(
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"inside_temp, humidity, expected_halflife, expected_decay_constant",
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[
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[293.15, 0.5, 35.67710693238622, 0.01942834607844098],
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[272.15, 0.7, 96.40459058258793, 0.007189981061805762],
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[300.15, 1., 10.418034697539541, 0.0665333914393324],
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[300.15, 0., 1.1, 0.6301338005090411],
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[293.15, 0.5, 0.5947447349860315, 1.1654532436949188],
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[272.15, 0.7, 1.6070844193207476, 0.4313072619127947],
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[300.15, 1., 0.17367078830147223, 3.9911558376571805],
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[300.15, 0., 6.43, 0.10779893943389507],
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[np.array([272.15, 300.15]), np.array([0.7, 0.]),
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np.array([96.40459058258793, 1.1]), np.array([0.007189981061805762, 0.6301338005090411])],
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np.array([1.60708442, 6.43]), np.array([0.43130726, 0.10779894])],
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[np.array([293.15, 300.15]), np.array([0.5, 1.]),
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np.array([35.67710693238622, 10.418034697539541]), np.array([0.01942834607844098, 0.0665333914393324])]
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np.array([0.59474473, 0.17367079]), np.array([1.16545324, 3.99115584])]
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],
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)
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def test_decay_constant(inside_temp, humidity, expected_halflife, expected_decay_constant):
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npt.assert_equal(models.Virus.types['SARS_CoV_2'].halflife(humidity, inside_temp),
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expected_halflife)
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npt.assert_equal(models.Virus.types['SARS_CoV_2'].decay_constant(humidity, inside_temp),
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expected_decay_constant)
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npt.assert_almost_equal(models.Virus.types['SARS_CoV_2'].halflife(humidity, inside_temp),
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expected_halflife)
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npt.assert_almost_equal(models.Virus.types['SARS_CoV_2'].decay_constant(humidity, inside_temp),
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expected_decay_constant)
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@ -84,12 +84,13 @@ class SimpleConcentrationModel:
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"""
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removal rate lambda in h^-1, excluding the deposition rate.
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"""
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hl_calc = ((ln2/((0.16030 + 0.04018*(((293-273.15)-20.615)/10.585)
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+0.02176*(((self.humidity*100)-45.235)/28.665)
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-0.14369
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-0.02636*((293-273.15)-20.615)/10.585)))/60)
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return (self.lambda_ventilation
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+ ln2/(np.maximum(1.1, (0.693 / ((0.16030 + 0.04018 * (((21) - 20.615) / 10.585)
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+ 0.02176*(((self.humidity * 100) - 45.235) / 28.665)
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- 0.14369
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- 0.02636*((21-20.615)/10.585)))))))
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+ ln2/(np.where(hl_calc <= 0, 6.43, np.minimum(6.43, hl_calc))))
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@method_cache
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def deposition_removal_coefficient(self) -> float:
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@ -43,7 +43,7 @@ def test_concentrations(baseline_concentration_model):
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concentrations = [baseline_concentration_model.concentration(float(t)) for t in ts]
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npt.assert_allclose(
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concentrations,
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[0.000000e+00, 2.122276e+01, 1.240684e-12, 2.122276e+01, 7.253047e-26],
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[0.000000e+00, 2.046096e+01, 3.846725e-13, 2.046096e+01, 7.231966e-27],
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rtol=1e-6
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)
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@ -94,7 +94,7 @@ def test_r0(baseline_exposure_model):
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# expected r0 was computed with a trapezoidal integration, using
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# a mesh of 100'000 pts per exposed presence interval.
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r0 = baseline_exposure_model.reproduction_number()
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npt.assert_allclose(r0, 783.490035)
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npt.assert_allclose(r0, 771.380385)
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def test_periodic_window(baseline_periodic_window, baseline_room):
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@ -381,8 +381,8 @@ def build_exposure_model(concentration_model, short_range_model):
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@pytest.mark.parametrize(
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"month, expected_deposited_exposure",
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[
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['Jan', 401.300989],
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['Jun', 2420.383151],
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['Jan', 359.140499],
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['Jun', 1385.917562],
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],
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)
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def test_exposure_hourly_dep(month,expected_deposited_exposure, baseline_sr_model):
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@ -402,8 +402,8 @@ def test_exposure_hourly_dep(month,expected_deposited_exposure, baseline_sr_mode
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@pytest.mark.parametrize(
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"month, expected_deposited_exposure",
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[
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['Jan', 402.348745],
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['Jun', 2558.632473],
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['Jan', 359.983716],
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['Jun', 1439.267381],
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],
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)
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def test_exposure_hourly_dep_refined(month,expected_deposited_exposure, baseline_sr_model):
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@ -310,13 +310,13 @@ def waiting_room_mc():
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@pytest.mark.parametrize(
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"mc_model, expected_pi, expected_new_cases, expected_dose, expected_ER",
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[
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["shared_office_mc", 6.75, 0.20, 3.594, 809],
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["classroom_mc", 9.58, 1.82, 9.664, 5624],
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["shared_office_mc", 5.55, 0.17, 2.699, 809],
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["classroom_mc", 9.58, 1.82, 9.034, 5624],
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["ski_cabin_mc", 16.0, 0.47, 17.315, 7966],
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["skagit_chorale_mc",72.20, 43.32, 134.234, 190422],
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["bus_ride_mc", 14.08, 9.43, 9.26, 5419],
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["gym_mc", 0.49, 0.14, 0.226, 1145],
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["waiting_room_mc", 2.02, 0.28, 1.104, 737],
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["skagit_chorale_mc",61.01, 36.53, 84.730, 190422],
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["bus_ride_mc", 10.59, 7.06, 6.65, 5419],
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["gym_mc", 0.43, 0.12, 0.197, 1145],
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["waiting_room_mc", 1.34, 0.18, 0.670, 737],
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]
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)
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def test_report_models(mc_model, expected_pi, expected_new_cases,
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@ -337,10 +337,10 @@ def test_report_models(mc_model, expected_pi, expected_new_cases,
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@pytest.mark.parametrize(
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"mask_type, month, expected_pi, expected_dose, expected_ER",
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[
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["No mask", "Jul", 11.81, 14.137, 809],
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["Type I", "Jul", 2.33, 1.305, 149],
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["FFP2", "Jul", 0.73, 0.351, 149],
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["Type I", "Feb", 0.62, 0.291, 149],
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["No mask", "Jul", 8.46, 8.113, 809],
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["Type I", "Jul", 1.44, 0.727, 149],
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["FFP2", "Jul", 0.43, 0.197, 149],
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["Type I", "Feb", 0.54, 0.253, 149],
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],
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)
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def test_small_shared_office_Geneva(mask_type, month, expected_pi,
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