diff --git a/caimira/apps/calculator/__init__.py b/caimira/apps/calculator/__init__.py
index b5a11900..51cc1961 100644
--- a/caimira/apps/calculator/__init__.py
+++ b/caimira/apps/calculator/__init__.py
@@ -37,7 +37,7 @@ from .user import AuthenticatedUser, AnonymousUser
# calculator version. If the calculator needs to make breaking changes (e.g. change
# form attributes) then it can also increase its MAJOR version without needing to
# increase the overall CAiMIRA version (found at ``caimira.__version__``).
-__version__ = "4.14.0"
+__version__ = "4.14.1"
LOG = logging.getLogger(__name__)
diff --git a/caimira/apps/calculator/static/js/report.js b/caimira/apps/calculator/static/js/report.js
index 6cfd96a1..57b1511e 100644
--- a/caimira/apps/calculator/static/js/report.js
+++ b/caimira/apps/calculator/static/js/report.js
@@ -59,7 +59,7 @@ function draw_plot(svg_id) {
.attr('class', 'y label')
.attr('fill', 'black')
.attr('text-anchor', 'middle')
- .text('Mean concentration (virions/m³)');
+ .text('Mean concentration (IRP/m³)');
// Y cumulative concentration axis declaration.
var yAxisCumEl = vis.append('svg:g')
@@ -71,7 +71,7 @@ function draw_plot(svg_id) {
.attr('class', 'y label')
.attr('fill', 'black')
.attr('text-anchor', 'middle')
- .text('Mean cumulative dose (infectious virus)');
+ .text('Mean cumulative dose (IRP)');
// Legend for the plot elements - line and area.
@@ -83,7 +83,6 @@ function draw_plot(svg_id) {
// Concentration line text
var legendLineText = vis.append('text')
.text('Mean concentration')
- .style('font-size', '15px');
// Cumulative dose line icon
var legendCumulativeIcon = vis.append('line')
@@ -93,7 +92,6 @@ function draw_plot(svg_id) {
// Cumulative dose line text
var legendCumutiveText = vis.append('text')
.text('Cumulative dose')
- .style('font-size', '15px');
// Area line icon
var legendAreaIcon = vis.append('rect')
@@ -104,7 +102,6 @@ function draw_plot(svg_id) {
// Area line text
var legendAreaText = vis.append('text')
.text('Presence of exposed person(s)')
- .style('font-size', '15px');
sr_unique_activities = [...new Set(short_range_expirations)]
if (show_sr_legend) {
@@ -116,7 +113,6 @@ function draw_plot(svg_id) {
.attr('opacity', 0);
var legendLongCumutiveText = vis.append('text')
.text('Long-range cumulative dose')
- .style('font-size', '15px')
.attr('opacity', 0);
// Short-range area icon
var legendShortRangeAreaIcon = {};
@@ -133,8 +129,7 @@ function draw_plot(svg_id) {
var legendShortRangeText = {};
sr_unique_activities.forEach((b, index) => {
legendShortRangeText[index] = vis.append('text')
- .text('Short-range - ' + sr_unique_activities[index])
- .style('font-size', '15px');
+ .text('Short-range - ' + sr_unique_activities[index]);
});
}
@@ -142,7 +137,7 @@ function draw_plot(svg_id) {
if (show_sr_legend) legendBBox_height = 68 + 20 * sr_unique_activities.length;
else legendBBox_height = 68;
var legendBBox = vis.append('rect')
- .attr('width', 255)
+ .attr('width', 270)
.attr('height', legendBBox_height)
.attr('stroke', 'lightgrey')
.attr('stroke-width', '2')
@@ -310,9 +305,9 @@ function draw_plot(svg_id) {
graph_width = div_width;
graph_height = div_height
var margins = { top: 30, right: 20, bottom: 50, left: 60 };
- if (div_width >= 900) { // For screens with width > 900px legend can be on the graph's right side.
- div_width = 900;
- graph_width = div_width * (2/3);
+ if (div_width >= 1000) { // For screens with width > 1000px legend can be on the graph's right side.
+ div_width = 1000;
+ graph_width = 600;
const svg_margins = {'margin-left': '0rem'};
Object.entries(svg_margins).forEach(([prop,val]) => vis.style(prop,val));
}
@@ -361,7 +356,7 @@ function draw_plot(svg_id) {
yAxisCumLabelEl.attr('transform', 'rotate(-90, 0,' + graph_height + ')')
.attr('x', (graph_height + margins.bottom) / 2.1);
- if (plot_div.clientWidth >= 900) {
+ if (plot_div.clientWidth >= 1000) {
yAxisCumLabelEl.attr('y', graph_width * 1.7);
}
else {
@@ -373,7 +368,7 @@ function draw_plot(svg_id) {
const space_between_text_icon = 30;
const text_height = 6;
// Legend on right side.
- if (plot_div.clientWidth >= 900) {
+ if (plot_div.clientWidth >= 1000) {
legendLineIcon.attr('x', graph_width + legend_x_start)
.attr('y', margins.top + size);
legendLineText.attr('x', graph_width + legend_x_start + space_between_text_icon)
@@ -610,7 +605,7 @@ function draw_generic_concentration_plot(
max_key_length = Math.max(Math.max(...(Object.keys(data_for_scenarios).map(el => el.length))), h_line_max_key);
var legendBBox = vis.append('rect')
- .attr('width', 9 * max_key_length )
+ .attr('width', 10 * max_key_length )
.attr('height', 25 * ((Object.keys(data_for_scenarios).length) + h_lines_lenght))
.attr('stroke', 'lightgrey')
.attr('stroke-width', '2')
@@ -646,8 +641,7 @@ function draw_generic_concentration_plot(
.style('fill', colors[scenario_index]);
label_text[scenario_name] = vis.append('text')
- .text(scenario_name)
- .style('font-size', '15px');
+ .text(scenario_name);
}
if (h_lines) {
var h_lines_draw = {}, h_line_label_icon = {}, h_line_label_text = {};
@@ -664,8 +658,7 @@ function draw_generic_concentration_plot(
.attr('stroke-width', '2')
.style("stroke", line.color);
h_line_label_text[line.label] = vis.append('text')
- .text(line.label)
- .style('font-size', '15px');
+ .text(line.label);
})
}
@@ -746,9 +739,9 @@ function draw_generic_concentration_plot(
graph_width = div_width;
graph_height = div_height;
var margins = { top: 30, right: 20, bottom: 50, left: 60 };
- if (window_width >= 900) { // For screens with width > 900px legend can be on the graph's right side.
- div_width = 900;
- graph_width = div_width * (2/3);
+ if (window_width >= 1000) { // For screens with width > 1000px legend can be on the graph's right side.
+ div_width = 1000;
+ graph_width = 600;
const svg_margins = {'margin-left': '0rem'};
Object.entries(svg_margins).forEach(([prop,val]) => vis.style(prop,val));
}
@@ -799,7 +792,7 @@ function draw_generic_concentration_plot(
var scenario_index = Object.keys(data_for_scenarios).indexOf(scenario_name)
// Legend on right side.
var size = 20 * (scenario_index + 1);
- if (window_width >= 900) {
+ if (window_width >= 1000) {
label_icons[scenario_name].attr('x', graph_width + legend_x_start)
.attr('y', margins.top + size);
label_text[scenario_name].attr('x', graph_width + legend_x_start + space_between_text_icon)
@@ -817,7 +810,7 @@ function draw_generic_concentration_plot(
if (h_lines) {
h_lines.map((line, index) => {
size = 21 * (scenario_index + index + 2); // account for previous legend elements
- if (window_width >= 900) {
+ if (window_width >= 1000) {
h_line_label_icon[line.label].attr("x1", graph_width + legend_x_start)
.attr("x2", graph_width + legend_x_start + 20)
.attr("y1", margins.top + size)
@@ -839,7 +832,7 @@ function draw_generic_concentration_plot(
}
// Legend on right side.
- if (window_width >= 900) {
+ if (window_width >= 1000) {
legendBBox.attr('x', graph_width * 1.02)
.attr('y', margins.top * 1.15);
@@ -899,10 +892,10 @@ function draw_histogram(svg_id, prob, prob_sd) {
var vis = d3.select(plot_div).append('svg');
// set the dimensions and margins of the graph
- if (div_width > 900) {
- div_width = 900;
+ if (div_width > 1000) {
+ div_width = 1000;
var margins = { top: 30, right: 20, bottom: 50, left: 60 };
- var graph_width = div_width * (2/3);
+ var graph_width = 600;
const svg_margins = {'margin-left': '0rem'};
Object.entries(svg_margins).forEach(([prop,val]) => vis.style(prop,val));
}
@@ -1035,7 +1028,6 @@ function draw_histogram(svg_id, prob, prob_sd) {
// CDF line text
vis.append('text')
.text('CDF')
- .style('font-size', '15px')
.attr('x', graph_width + legend_x_start + space_between_text_icon)
.attr('y', margins.top + size + text_height);
// Hist icon
@@ -1048,7 +1040,6 @@ function draw_histogram(svg_id, prob, prob_sd) {
// Hist text
vis.append('text')
.text('Histogram')
- .style('font-size', '15px')
.attr('x', graph_width + legend_x_start + space_between_text_icon)
.attr('y', margins.top + 2 * size + text_height*2);
// Mean text
@@ -1063,7 +1054,6 @@ function draw_histogram(svg_id, prob, prob_sd) {
// Mean line text
vis.append('text')
.text('Mean')
- .style('font-size', '15px')
.attr('x', graph_width + legend_x_start + space_between_text_icon)
.attr('y', margins.top + 3 * size + text_height*3);
diff --git a/caimira/apps/expert.py b/caimira/apps/expert.py
index 5162d8f0..240e90cb 100644
--- a/caimira/apps/expert.py
+++ b/caimira/apps/expert.py
@@ -131,13 +131,13 @@ class ExposureModelResult(View):
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_xlabel('Time (hours)')
- ax.set_ylabel('Mean concentration ($virions/m^{3}$)')
- ax.set_title('Concentration of virions \nand Cumulative dose')
+ ax.set_ylabel('Mean concentration ($IRP/m^{3}$)')
+ ax.set_title('Concentration and Cumulative\ndose of Infectious Respiratory Particles')
ax2 = ax.twinx()
ax2.spines['left'].set_visible(False)
ax2.spines['top'].set_visible(False)
- ax2.set_ylabel('Mean cumulative dose (infectious virus)')
+ ax2.set_ylabel('Mean cumulative dose (IRP)')
ax2.spines['right'].set_linestyle((0,(1,4)))
return ax, ax2
@@ -236,15 +236,15 @@ class ExposureComparisonResult(View):
ax.spines['top'].set_visible(False)
ax.set_xlabel('Time (hours)')
- ax.set_ylabel('Mean concentration ($virions/m^{3}$)')
- ax.set_title('Concentration of virions \nand Cumulative dose')
+ ax.set_ylabel('Mean concentration ($IRP/m^{3}$)')
+ ax.set_title('Concentration and Cumulative\ndose of Infectious Respiratory Particles')
ax2 = ax.twinx()
ax2.spines['left'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.spines['right'].set_linestyle((0,(1,4)))
- ax2.set_ylabel('Mean cumulative dose (infectious virus)')
+ ax2.set_ylabel('Mean cumulative dose (IRP)')
return ax, ax2
diff --git a/caimira/apps/templates/base/calculator.report.html.j2 b/caimira/apps/templates/base/calculator.report.html.j2
index 58bf8f68..6f37e7e2 100644
--- a/caimira/apps/templates/base/calculator.report.html.j2
+++ b/caimira/apps/templates/base/calculator.report.html.j2
@@ -181,6 +181,7 @@
let short_range_expirations = {{ short_range_expirations | JSONify }};
draw_plot("concentration_plot");
+ IRP - Infectious Respiratory Particles.
@@ -277,7 +278,7 @@
var alternative_scenarios = {{ alternative_scenarios.stats | JSONify }}
draw_generic_concentration_plot(
"alternative_scenario_plot",
- "Mean concentration (virions/m³)",
+ "Mean concentration (IRP/m³)",
);
diff --git a/caimira/models.py b/caimira/models.py
index 476a71b3..fd5121cb 100644
--- a/caimira/models.py
+++ b/caimira/models.py
@@ -979,6 +979,7 @@ class InfectedPopulation(_PopulationWithVirus):
ER = (self.virus.viral_load_in_sputum *
self.activity.exhalation_rate *
+ self.fraction_of_infectious_virus() *
10 ** 6)
return ER
@@ -1641,7 +1642,6 @@ class ExposureModel:
emission_rate_per_aerosol_per_person = \
self.concentration_model.infected.emission_rate_per_aerosol_per_person_when_present()
aerosols = self.concentration_model.infected.aerosols()
- f_inf = self.concentration_model.infected.fraction_of_infectious_virus()
fdep = self.long_range_fraction_deposited()
diameter = self.concentration_model.infected.particle.diameter
@@ -1667,7 +1667,7 @@ class ExposureModel:
(1 - self.exposed.mask.inhale_efficiency()))
# In the end we multiply the final results by the fraction of infectious virus of the vD equation.
- return deposited_exposure * f_inf
+ return deposited_exposure
def deposited_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat:
"""
@@ -1716,9 +1716,8 @@ class ExposureModel:
# Then we multiply by diameter-independent quantities: viral load
# and fraction of infected virions
- f_inf = self.concentration_model.infected.fraction_of_infectious_virus()
- deposited_exposure *= (f_inf
- * self.concentration_model.virus.viral_load_in_sputum
+ deposited_exposure *= (
+ self.concentration_model.virus.viral_load_in_sputum
* (1 - self.exposed.mask.inhale_efficiency()))
# Long-range concentration
deposited_exposure += self.long_range_deposited_exposure_between_bounds(time1, time2)
diff --git a/caimira/tests/models/test_exposure_model.py b/caimira/tests/models/test_exposure_model.py
index bfe59c1c..07722dd6 100644
--- a/caimira/tests/models/test_exposure_model.py
+++ b/caimira/tests/models/test_exposure_model.py
@@ -75,19 +75,19 @@ def known_concentrations(func):
@pytest.mark.parametrize(
"population, cm, expected_exposure, expected_probability", [
- [populations[1], known_concentrations(lambda t: 36.),
+ [populations[1], known_concentrations(lambda t: 18.),
np.array([64.02320633, 59.45012016]), np.array([67.9503762594, 65.2366759251])],
- [populations[2], known_concentrations(lambda t: 36.),
+ [populations[2], known_concentrations(lambda t: 18.),
np.array([40.91708675, 45.73086166]), np.array([51.6749232285, 55.6374622042])],
- [populations[0], known_concentrations(lambda t: np.array([36., 72.])),
+ [populations[0], known_concentrations(lambda t: np.array([18., 36.])),
np.array([45.73086166, 91.46172332]), np.array([55.6374622042, 80.3196524031])],
- [populations[1], known_concentrations(lambda t: np.array([36., 72.])),
+ [populations[1], known_concentrations(lambda t: np.array([18., 36.])),
np.array([64.02320633, 118.90024032]), np.array([67.9503762594, 87.9151129926])],
- [populations[2], known_concentrations(lambda t: np.array([36., 72.])),
+ [populations[2], known_concentrations(lambda t: np.array([18., 36.])),
np.array([40.91708675, 91.46172332]), np.array([51.6749232285, 80.3196524031])],
])
def test_exposure_model_ndarray(population, cm,
@@ -113,7 +113,7 @@ def test_exposure_model_ndarray(population, cm,
])
def test_exposure_model_ndarray_and_float_mix(population, expected_deposited_exposure, sr_model, cases_model):
cm = known_concentrations(
- lambda t: 0. if np.floor(t) % 2 else np.array([1.2, 1.2]))
+ lambda t: 0. if np.floor(t) % 2 else np.array([0.6, 0.6]))
model = ExposureModel(cm, sr_model, population, cases_model)
np.testing.assert_almost_equal(
@@ -130,7 +130,7 @@ def test_exposure_model_ndarray_and_float_mix(population, expected_deposited_exp
[populations[2], np.array([1.36390289, 1.52436206])],
])
def test_exposure_model_vector(population, expected_deposited_exposure, sr_model, cases_model):
- cm_array = known_concentrations(lambda t: np.array([1.2, 1.2]))
+ cm_array = known_concentrations(lambda t: np.array([0.6, 0.6]))
model_array = ExposureModel(cm_array, sr_model, population, cases_model)
np.testing.assert_almost_equal(
model_array.deposited_exposure(), np.array(expected_deposited_exposure)
@@ -138,7 +138,7 @@ def test_exposure_model_vector(population, expected_deposited_exposure, sr_model
def test_exposure_model_scalar(sr_model, cases_model):
- cm_scalar = known_concentrations(lambda t: 1.2)
+ cm_scalar = known_concentrations(lambda t: 0.6)
model_scalar = ExposureModel(cm_scalar, sr_model, populations[0], cases_model)
expected_deposited_exposure = 1.52436206
np.testing.assert_almost_equal(
@@ -234,7 +234,7 @@ def test_infectious_dose_vectorisation(sr_model, cases_model):
expiration=models.Expiration.types['Speaking'],
host_immunity=0.,
)
- cm = known_concentrations(lambda t: 1.2)
+ cm = known_concentrations(lambda t: 0.6)
cm = replace(cm, infected=infected_population)
presence_interval = models.SpecificInterval(((0., 1.),))
@@ -289,13 +289,13 @@ def test_prob_meet_infected_person(pop, cases, AB, exposed, infected, prob_meet_
@pytest.mark.parametrize(
"exposed_population, cm, pop, cases, AB, probabilistic_exposure_probability",[
- [10, known_concentrations(lambda t: 36.),
+ [10, known_concentrations(lambda t: 18.),
100000, 68, 5, 41.50971131],
- [10, known_concentrations(lambda t: 0.2),
+ [10, known_concentrations(lambda t: 0.1),
100000, 68, 5, 2.185785075],
- [20, known_concentrations(lambda t: 72.),
+ [20, known_concentrations(lambda t: 36.),
100000, 68, 5, 64.09068488],
- [30, known_concentrations(lambda t: 1.2),
+ [30, known_concentrations(lambda t: 0.6),
100000, 68, 5, 55.93154502],
])
def test_probabilistic_exposure_probability(sr_model, exposed_population, cm,
@@ -396,8 +396,8 @@ def test_diameter_vectorisation_room(diameter_dependent_model, sr_model, cases_m
@pytest.mark.parametrize(
["cm", "host_immunity", "expected_probability"],
[
- [known_concentrations(lambda t: 36.), np.array([0.25, 0.5]), np.array([57.40415859, 41.03956914])],
- [known_concentrations(lambda t: 36.), np.array([0., 1.]), np.array([67.95037626, 0.])],
+ [known_concentrations(lambda t: 18.), np.array([0.25, 0.5]), np.array([57.40415859, 41.03956914])],
+ [known_concentrations(lambda t: 18.), np.array([0., 1.]), np.array([67.95037626, 0.])],
]
)
def test_host_immunity_vectorisation(sr_model, cases_model, cm, host_immunity, expected_probability):
diff --git a/caimira/tests/test_full_algorithm.py b/caimira/tests/test_full_algorithm.py
index 9f2ee70d..ff5f3843 100644
--- a/caimira/tests/test_full_algorithm.py
+++ b/caimira/tests/test_full_algorithm.py
@@ -56,6 +56,12 @@ class SimpleConcentrationModel:
#: Number of infected people
num_infected: int = 1
+ #: Fraction of infected viruses (viable to RNA ratio)
+ viable_to_RNA: _VectorisedFloat = 0.5
+
+ #: Host immunity factor (0. for not immune)
+ HI: _VectorisedFloat = 0.
+
#: Relative humidity RH
humidity: float = 0.3
@@ -176,7 +182,8 @@ class SimpleConcentrationModel:
return ( ( (0 if not self.infected_presence.triggered(t)
else self.f(lambda_rate,0))
+ result * np.exp(-lambda_rate*(t-ti)) )
- * self.num_infected/self.room_volume)
+ * self.num_infected * self.viable_to_RNA
+ * (1. - self.HI) / self.room_volume)
@dataclass(frozen=True)
@@ -295,12 +302,6 @@ class SimpleExposureModel(SimpleConcentrationModel):
interaction intervals are within presence intervals of the infected.
"""
- #: Fraction of infected viruses
- finf: _VectorisedFloat = 0.5
-
- #: Host immunity factor (0. for not immune)
- HI: _VectorisedFloat = 0.
-
#: Infectious dose (ID50)
ID50: _VectorisedFloat = 50.
@@ -410,7 +411,8 @@ class SimpleExposureModel(SimpleConcentrationModel):
else self.f_with_fdep(lambda_rate,0,evaporation)*(t2-t1))
+ (primitive(t2) * np.exp(-lambda_rate*(t2-ti)) -
primitive(t1) * np.exp(-lambda_rate*(t1-ti)) ) )
- * self.num_infected/self.room_volume)
+ * self.num_infected * self.viable_to_RNA
+ * (1. - self.HI) / self.room_volume)
@method_cache
def integrated_shortrange_concentration(self) -> _VectorisedFloat:
@@ -448,7 +450,7 @@ class SimpleExposureModel(SimpleConcentrationModel):
result += self.integrated_shortrange_concentration()
- return result * self.finf * (1. - self.HI)
+ return result
def probability_infection(self):
"""
@@ -528,6 +530,8 @@ def simple_c_model() -> SimpleConcentrationModel:
room_volume = 50.,
lambda_ventilation= 1.,
BLO_factors = expiration_BLO_factors['Breathing'],
+ viable_to_RNA = models.Virus.types['SARS_CoV_2_DELTA'].viable_to_RNA_ratio,
+ HI = 0.,
)
@@ -574,7 +578,7 @@ def simple_expo_sr_model(simple_sr_models) -> SimpleExposureModel:
room_volume = 50.,
lambda_ventilation= 1.,
BLO_factors = expiration_BLO_factors['Breathing'],
- finf = models.Virus.types['SARS_CoV_2_DELTA'].viable_to_RNA_ratio,
+ viable_to_RNA = models.Virus.types['SARS_CoV_2_DELTA'].viable_to_RNA_ratio,
HI = 0.,
ID50 = models.Virus.types['SARS_CoV_2_DELTA'].infectious_dose,
transmissibility = models.Virus.types['SARS_CoV_2_DELTA'].transmissibility_factor,
@@ -622,7 +626,7 @@ def simple_expo_sr_model_distr() -> SimpleExposureModel:
room_volume = 50.,
lambda_ventilation= 1.,
BLO_factors = expiration_BLO_factors['Breathing'],
- finf = virus_distributions['SARS_CoV_2_DELTA'
+ viable_to_RNA = virus_distributions['SARS_CoV_2_DELTA'
].build_model(SAMPLE_SIZE).viable_to_RNA_ratio,
HI = 0.,
ID50 = virus_distributions['SARS_CoV_2_DELTA'
@@ -683,7 +687,7 @@ def test_longrange_exposure(c_model):
room_volume = 50.,
lambda_ventilation= 1.,
BLO_factors = expiration_BLO_factors['Breathing'],
- finf = models.Virus.types['SARS_CoV_2_DELTA'].viable_to_RNA_ratio,
+ viable_to_RNA = models.Virus.types['SARS_CoV_2_DELTA'].viable_to_RNA_ratio,
HI = 0.,
ID50 = models.Virus.types['SARS_CoV_2_DELTA'].infectious_dose,
transmissibility = models.Virus.types['SARS_CoV_2_DELTA'].transmissibility_factor,
@@ -724,6 +728,9 @@ def test_longrange_concentration_with_distributions(c_model_distr,time):
room_volume = 50.,
lambda_ventilation= 1.,
BLO_factors = expiration_BLO_factors['Breathing'],
+ viable_to_RNA = virus_distributions['SARS_CoV_2_DELTA'
+ ].build_model(SAMPLE_SIZE).viable_to_RNA_ratio,
+ HI = 0.,
)
npt.assert_allclose(
c_model_distr.build_model(SAMPLE_SIZE).concentration(time).mean(),
@@ -741,7 +748,7 @@ def test_longrange_exposure_with_distributions(c_model_distr):
room_volume = 50.,
lambda_ventilation= 1.,
BLO_factors = expiration_BLO_factors['Breathing'],
- finf = virus_distributions['SARS_CoV_2_DELTA'
+ viable_to_RNA = virus_distributions['SARS_CoV_2_DELTA'
].build_model(SAMPLE_SIZE).viable_to_RNA_ratio,
HI = 0.,
ID50 = virus_distributions['SARS_CoV_2_DELTA'
diff --git a/caimira/tests/test_monte_carlo_full_models.py b/caimira/tests/test_monte_carlo_full_models.py
index b6e3948c..60cdf477 100644
--- a/caimira/tests/test_monte_carlo_full_models.py
+++ b/caimira/tests/test_monte_carlo_full_models.py
@@ -311,17 +311,22 @@ def waiting_room_mc():
)
+# In the following tests all the initial expected values for emission
+# rate were multiplied by the average of the distribution of fraction
+# of infected viruses (0.305, for a uniform distribution from 0.01 to 0.6)
+# following the change of convention that this ratio should be
+# applied at the emission level.
@retry(tries=10)
@pytest.mark.parametrize(
"mc_model, expected_pi, expected_new_cases, expected_dose, expected_ER_per_person",
[
- ["shared_office_mc", 5.38, 0.16, 3.350, 1056],
- ["classroom_mc", 8.21, 1.56, 11.356, 7416],
- ["ski_cabin_mc", 12.92, 0.39, 21.796, 10231],
- ["skagit_chorale_mc",61.01, 36.53, 84.730, 190422],
- ["bus_ride_mc", 10.59, 7.06, 6.650, 5419],
- ["gym_mc", 0.52, 0.14, 0.249, 1450/2.], # there are two infected in this case
- ["waiting_room_mc", 1.53, 0.21, 0.844, 929],
+ ["shared_office_mc", 5.38, 0.16, 3.350, 1056*0.305],
+ ["classroom_mc", 8.21, 1.56, 11.356, 7416*0.305],
+ ["ski_cabin_mc", 12.92, 0.39, 21.796, 10231*0.305],
+ ["skagit_chorale_mc",61.01, 36.53, 84.730, 190422*0.305],
+ ["bus_ride_mc", 10.59, 7.06, 6.650, 5419*0.305],
+ ["gym_mc", 0.52, 0.14, 0.249, 1450*0.305/2.], # there are two infected in this case
+ ["waiting_room_mc", 1.53, 0.21, 0.844, 929*0.305],
]
)
def test_report_models(mc_model, expected_pi, expected_new_cases,
@@ -343,12 +348,12 @@ def test_report_models(mc_model, expected_pi, expected_new_cases,
@pytest.mark.parametrize(
"mask_type, month, expected_pi, expected_dose, expected_ER",
[
- ["No mask", "Jul", 7.689, 10.050, 1034.435],
- ["Type I", "Jul", 1.663, 0.938, 193.52],
- ["FFP2", "Jul", 0.523, 0.253, 193.52],
- ["Type I", "Feb", 0.659, 0.325, 193.52],
- ["Cloth", "Feb", 2.653, 1.741, 673.10],
- ["Cloth", "Jul", 5.322, 5.064, 673.10],
+ ["No mask", "Jul", 7.689, 10.050, 1034.435*0.305],
+ ["Type I", "Jul", 1.663, 0.938, 193.52*0.305],
+ ["FFP2", "Jul", 0.523, 0.253, 193.52*0.305],
+ ["Type I", "Feb", 0.659, 0.325, 193.52*0.305],
+ ["Cloth", "Feb", 2.653, 1.741, 673.10*0.305],
+ ["Cloth", "Jul", 5.322, 5.064, 673.10*0.305],
],
)
def test_small_shared_office_Geneva(mask_type, month, expected_pi,