Merge branch 'changes/schema_update' into 'master'
Data registry update (schema v2.1.1) See merge request caimira/caimira!487
This commit is contained in:
Luis Aleixo
commit
c8b6d882cc
11 changed files with 383 additions and 400 deletions
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@ -42,7 +42,7 @@ from .user import AuthenticatedUser, AnonymousUser
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# calculator version. If the calculator needs to make breaking changes (e.g. change
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# form attributes) then it can also increase its MAJOR version without needing to
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# increase the overall CAiMIRA version (found at ``caimira.__version__``).
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__version__ = "4.15.2"
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__version__ = "4.15.3"
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LOG = logging.getLogger("Calculator")
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@ -573,7 +573,11 @@ def make_app(
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data_registry = DataRegistry()
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data_service = None
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data_service_enabled = os.environ.get('DATA_SERVICE_ENABLED', 0)
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try:
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data_service_enabled = int(os.environ.get('DATA_SERVICE_ENABLED', 0))
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except ValueError:
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data_service_enabled = None
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if data_service_enabled: data_service = DataService.create()
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return Application(
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@ -159,7 +159,7 @@ class CO2FormData(FormData):
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return tuple((self.CO2_data['times'][0], self.CO2_data['times'][-1]))
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def build_model(self, size=None) -> models.CO2DataModel: # type: ignore
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size = size or self.data_registry.monte_carlo_sample_size
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size = size or self.data_registry.monte_carlo['sample_size']
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# Build a simple infected and exposed population for the case when presence
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# intervals and number of people are dynamic. Activity type is not needed.
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infected_presence = self.infected_present_interval()
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@ -200,10 +200,10 @@ class VirusFormData(FormData):
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if self.arve_sensors_option == False:
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if self.room_heating_option:
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humidity = self.data_registry.room['defaults']['humidity_with_heating']
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humidity = self.data_registry.room['humidity_with_heating']
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else:
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humidity = self.data_registry.room['defaults']['humidity_without_heating']
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inside_temp = self.data_registry.room['defaults']['inside_temp']
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humidity = self.data_registry.room['humidity_without_heating']
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inside_temp = self.data_registry.room['inside_temp']
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else:
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humidity = float(self.humidity)
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inside_temp = self.inside_temp
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@ -245,11 +245,11 @@ class VirusFormData(FormData):
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)
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def build_model(self, sample_size=None) -> models.ExposureModel:
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sample_size = sample_size or self.data_registry.monte_carlo_sample_size
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sample_size = sample_size or self.data_registry.monte_carlo['sample_size']
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return self.build_mc_model().build_model(size=sample_size)
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def build_CO2_model(self, sample_size=None) -> models.CO2ConcentrationModel:
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sample_size = sample_size or self.data_registry.monte_carlo_sample_size
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sample_size = sample_size or self.data_registry.monte_carlo['sample_size']
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infected_population: models.InfectedPopulation = self.infected_population().build_model(sample_size)
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exposed_population: models.Population = self.exposed_population().build_model(sample_size)
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@ -19,6 +19,7 @@ from caimira.store.data_registry import DataRegistry
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from ... import monte_carlo as mc
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from .model_generator import VirusFormData
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from ... import dataclass_utils
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from caimira.enums import ViralLoads
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def model_start_end(model: models.ExposureModel):
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@ -168,12 +169,27 @@ def calculate_report_data(form: VirusFormData, model: models.ExposureModel, exec
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prob_dist_count, prob_dist_bins = np.histogram(prob/100, bins=100, density=True)
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prob_probabilistic_exposure = np.array(model.total_probability_rule()).mean()
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expected_new_cases = np.array(model.expected_new_cases()).mean()
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uncertainties_plot_src = img2base64(_figure2bytes(uncertainties_plot(model, prob))) if form.conditional_probability_plot else None
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exposed_presence_intervals = [list(interval) for interval in model.exposed.presence_interval().boundaries()]
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conditional_probability_data = {key: value for key, value in
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zip(('viral_loads', 'pi_means', 'lower_percentiles', 'upper_percentiles'),
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manufacture_conditional_probability_data(model, prob))}
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if (model.data_registry.virological_data['virus_distributions'][form.virus_type]['viral_load_in_sputum'] == ViralLoads.COVID_OVERALL.value # type: ignore
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and form.conditional_probability_plot): # Only generate this data if covid_overall_vl_data is selected.
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viral_load_in_sputum: models._VectorisedFloat = model.concentration_model.infected.virus.viral_load_in_sputum
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viral_loads, pi_means, lower_percentiles, upper_percentiles = manufacture_conditional_probability_data(model, prob)
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uncertainties_plot_src = img2base64(_figure2bytes(uncertainties_plot(prob, viral_load_in_sputum, viral_loads,
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pi_means, lower_percentiles, upper_percentiles)))
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conditional_probability_data = {key: value for key, value in
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zip(('viral_loads', 'pi_means', 'lower_percentiles', 'upper_percentiles'),
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(viral_loads, pi_means, lower_percentiles, upper_percentiles))}
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vl_dist = list(np.log10(viral_load_in_sputum))
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else:
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uncertainties_plot_src = None
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conditional_probability_data = None
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vl = model.concentration_model.virus.viral_load_in_sputum
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if isinstance(vl, np.ndarray): vl_dist = list(np.log10(model.concentration_model.virus.viral_load_in_sputum))
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else: vl_dist = np.log10(model.concentration_model.virus.viral_load_in_sputum)
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return {
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"model_repr": repr(model),
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@ -194,7 +210,7 @@ def calculate_report_data(form: VirusFormData, model: models.ExposureModel, exec
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"expected_new_cases": expected_new_cases,
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"uncertainties_plot_src": uncertainties_plot_src,
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"CO2_concentrations": CO2_concentrations,
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"vl_dist": list(np.log10(model.concentration_model.virus.viral_load_in_sputum)),
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"vl_dist": vl_dist,
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"conditional_probability_data": conditional_probability_data,
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}
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@ -233,8 +249,8 @@ def conditional_prob_inf_given_vl_dist(
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for vl_log in viral_loads:
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specific_prob = infection_probability[np.where((vl_log-step/2-specific_vl)*(vl_log+step/2-specific_vl)<0)[0]] #type: ignore
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pi_means.append(specific_prob.mean())
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lower_percentiles.append(np.quantile(specific_prob, data_registry.conditional_prob_inf_given_viral_load['lower_percentile']))
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upper_percentiles.append(np.quantile(specific_prob, data_registry.conditional_prob_inf_given_viral_load['upper_percentile']))
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lower_percentiles.append(np.quantile(specific_prob, 0.05))
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upper_percentiles.append(np.quantile(specific_prob, 0.95))
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return pi_means, lower_percentiles, upper_percentiles
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@ -245,8 +261,8 @@ def manufacture_conditional_probability_data(
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):
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data_registry: DataRegistry = exposure_model.data_registry
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min_vl = data_registry.conditional_prob_inf_given_viral_load['min_vl']
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max_vl = data_registry.conditional_prob_inf_given_viral_load['max_vl']
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min_vl = 2
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max_vl = 10
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step = (max_vl - min_vl)/100
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viral_loads = np.arange(min_vl, max_vl, step)
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specific_vl = np.log10(exposure_model.concentration_model.virus.viral_load_in_sputum)
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@ -256,11 +272,12 @@ def manufacture_conditional_probability_data(
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return list(viral_loads), list(pi_means), list(lower_percentiles), list(upper_percentiles)
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def uncertainties_plot(exposure_model: models.ExposureModel, prob: models._VectorisedFloat):
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fig = plt.figure(figsize=(4, 7), dpi=110)
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infection_probability = prob / 100
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viral_loads, pi_means, lower_percentiles, upper_percentiles = manufacture_conditional_probability_data(exposure_model, infection_probability)
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def uncertainties_plot(infection_probability: models._VectorisedFloat,
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viral_load_in_sputum: models._VectorisedFloat,
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viral_loads: models._VectorisedFloat,
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pi_means: models._VectorisedFloat,
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lower_percentiles: models._VectorisedFloat,
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upper_percentiles: models._VectorisedFloat):
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fig, axs = plt.subplots(2, 3,
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gridspec_kw={'width_ratios': [5, 0.5] + [1],
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@ -273,8 +290,8 @@ def uncertainties_plot(exposure_model: models.ExposureModel, prob: models._Vecto
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axs[0, 1].set_visible(False)
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axs[0, 0].plot(viral_loads, pi_means, label='Predictive total probability')
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axs[0, 0].fill_between(viral_loads, lower_percentiles, upper_percentiles, alpha=0.1, label='5ᵗʰ and 95ᵗʰ percentile')
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axs[0, 0].plot(viral_loads, np.array(pi_means)/100, label='Predictive total probability')
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axs[0, 0].fill_between(viral_loads, np.array(lower_percentiles)/100, np.array(upper_percentiles)/100, alpha=0.1, label='5ᵗʰ and 95ᵗʰ percentile')
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axs[0, 2].hist(infection_probability, bins=30, orientation='horizontal')
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axs[0, 2].set_xticks([])
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@ -285,8 +302,8 @@ def uncertainties_plot(exposure_model: models.ExposureModel, prob: models._Vecto
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axs[0, 2].set_xlim(0, highest_bar)
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axs[0, 2].text(highest_bar * 0.5, 0.5,
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rf"$\bf{np.round(np.mean(infection_probability) * 100, 1)}$%", ha='center', va='center')
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axs[1, 0].hist(np.log10(exposure_model.concentration_model.infected.virus.viral_load_in_sputum),
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rf"$\bf{np.round(np.mean(infection_probability), 1)}$%", ha='center', va='center')
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axs[1, 0].hist(np.log10(viral_load_in_sputum),
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bins=150, range=(2, 10), color='grey')
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axs[1, 0].set_facecolor("lightgrey")
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axs[1, 0].set_yticks([])
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@ -442,7 +459,7 @@ def scenario_statistics(
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sample_times: typing.List[float],
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compute_prob_exposure: bool
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):
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model = mc_model.build_model(size=mc_model.data_registry.monte_carlo_sample_size)
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model = mc_model.build_model(size=mc_model.data_registry.monte_carlo['sample_size'])
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if (compute_prob_exposure):
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# It means we have data to calculate the total_probability_rule
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prob_probabilistic_exposure = model.total_probability_rule()
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@ -214,11 +214,12 @@
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draw_histogram("prob_inf_hist", {{ prob_inf }}, {{ prob_inf_sd }});
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</script>
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<br>
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<div class="form-check">
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<input type="checkbox" id="conditional_probability_plot" class="tabbed form-check-input" name="conditional_probability_plot" value="1" onClick="conditional_probability_plot(this.checked, {{ form.conditional_probability_plot | int }});">
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<label id="label_conditional_probability_plot" for="conditional_probability_plot" class="form-check-label col-sm-12">Generate full uncertainty data (as function of the viral load)</label>
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</div>
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{% if model.data_registry.virological_data['virus_distributions'][form.virus_type]['viral_load_in_sputum'] == 'Ref: Viral load - covid_overal_vl_data' %}
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<div class="form-check">
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<input type="checkbox" id="conditional_probability_plot" class="tabbed form-check-input" name="conditional_probability_plot" value="1" onClick="conditional_probability_plot(this.checked, {{ form.conditional_probability_plot | int }});">
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<label id="label_conditional_probability_plot" for="conditional_probability_plot" class="form-check-label col-sm-12">Generate full uncertainty data (as function of the viral load)</label>
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</div>
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{% endif %}
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{% if form.conditional_probability_plot %}
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<div id="conditional_probability_div">
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<img src= "{{ uncertainties_plot_src }}" />
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@ -3,11 +3,3 @@ from enum import Enum
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class ViralLoads(Enum):
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COVID_OVERALL = "Ref: Viral load - covid_overal_vl_data"
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SYMPTOMATIC_FREQUENCIES = "Ref: Viral load - symptomatic_vl_frequencies"
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class InfectiousDoses(Enum):
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DISTRIBUTION = "Ref: Infectious dose - infectious_dose_distribution"
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class ViableToRNARatios(Enum):
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DISTRIBUTION = "Ref: Viable to RNA ratio - viable_to_RNA_ratio_distribution"
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@ -879,7 +879,7 @@ class _PopulationWithVirus(Population):
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The fraction of infectious virus.
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"""
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return self.data_registry.population_with_virus['fraction_of_infectious_virus'] # type: ignore
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return 1
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def aerosols(self):
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"""
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@ -1052,7 +1052,7 @@ class _ConcentrationModelBase:
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(in the same unit as the concentration). Its the value towards which
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the concentration will decay to.
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"""
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return self.data_registry.concentration_model['min_background_concentration'] # type: ignore
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return self.data_registry.concentration_model['virus_concentration_model']['min_background_concentration'] # type: ignore
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def normalization_factor(self) -> _VectorisedFloat:
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"""
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@ -1242,7 +1242,7 @@ class ConcentrationModel(_ConcentrationModelBase):
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def __post_init__(self):
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if self.evaporation_factor is None:
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self.evaporation_factor = self.data_registry.particle['evaporation_factor']
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self.evaporation_factor = self.data_registry.expiration_particle['particle']['evaporation_factor']
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@property
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def population(self) -> InfectedPopulation:
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@ -1335,7 +1335,7 @@ class ShortRangeModel:
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'''
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The dilution factor for the respective expiratory activity type.
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'''
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_dilution_factor = self.data_registry.short_range_model['dilution_factor']
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_dilution_factor = self.data_registry.short_range_model['dilution_factor']
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# Average mouth opening diameter (m)
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mouth_diameter: float = _dilution_factor['mouth_diameter'] # type: ignore
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@ -1355,11 +1355,14 @@ class ShortRangeModel:
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# Initial velocity of the exhalation airflow (m/s)
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u0 = np.array(Q_exh/Am)
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# Duration of the expiration period(s), assuming a 4s breath-cycle
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tstar: float = _dilution_factor['tstar'] # type: ignore
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# Duration of one breathing cycle
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breathing_cicle: float = _dilution_factor['breathing_cycle'] # type: ignore
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# Duration of the expiration period(s)
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tstar: float = breathing_cicle / 2
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# Streamwise and radial penetration coefficients
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_df_pc = _dilution_factor['penetration_coefficients']
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_df_pc = _dilution_factor['penetration_coefficients'] # type: ignore
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𝛽r1: float = _df_pc['𝛽r1'] # type: ignore
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𝛽r2: float = _df_pc['𝛽r2'] # type: ignore
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𝛽x1: float = _df_pc['𝛽x1'] # type: ignore
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@ -1585,7 +1588,7 @@ class ExposureModel:
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#: The number of times the exposure event is repeated (default 1).
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@property
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def repeats(self) -> int:
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return self.data_registry.exposure_model['repeats'] # type: ignore
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return 1
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def __post_init__(self):
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"""
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@ -7,84 +7,72 @@ import numpy as np
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from scipy import special as sp
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from scipy.stats import weibull_min
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from caimira.enums import ViralLoads, InfectiousDoses, ViableToRNARatios
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from caimira.enums import ViralLoads
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import caimira.monte_carlo.models as mc
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from caimira.monte_carlo.sampleable import LogCustom, LogNormal, Normal, LogCustomKernel, CustomKernel, Uniform, Custom
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from caimira.store.data_registry import DataRegistry
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def evaluate_vl(value, data_registry: DataRegistry):
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if value == ViralLoads.COVID_OVERALL.value:
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def evaluate_vl(root: typing.Dict, value: str, data_registry: DataRegistry):
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if root[value] == ViralLoads.COVID_OVERALL.value:
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return covid_overal_vl_data(data_registry)
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elif value == ViralLoads.SYMPTOMATIC_FREQUENCIES.value:
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elif root[value] == ViralLoads.SYMPTOMATIC_FREQUENCIES.value:
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return symptomatic_vl_frequencies
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elif root[value] == 'Custom':
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return param_evaluation(root, 'Viral load custom')
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else:
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raise ValueError(f"Invalid ViralLoads value {value}")
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def evaluate_infectd(value, data_registry: DataRegistry):
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if value == InfectiousDoses.DISTRIBUTION.value:
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return infectious_dose_distribution(data_registry)
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else:
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raise ValueError(f"Invalid InfectiousDoses value {value}")
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def evaluate_vtrr(value, data_registry: DataRegistry):
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if value == ViableToRNARatios.DISTRIBUTION.value:
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return viable_to_RNA_ratio_distribution(data_registry)
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else:
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raise ValueError(f"Invalid ViableToRNARatios value {value}")
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sqrt2pi = np.sqrt(2.*np.pi)
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sqrt2 = np.sqrt(2.)
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def custom_distribution_lookup(dict: dict, key_part: str) -> typing.Any:
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def custom_value_type_lookup(dict: dict, key_part: str) -> typing.Any:
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"""
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Look up a custom distribution based on a partial key.
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Look up a custom value type based on a partial key.
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Args:
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dict (dict): The root to search.
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key_part (str): The distribution key to match.
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key_part (str): The value type key to match.
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Returns:
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str: The associated distribution.
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str: The associated value.
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"""
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try:
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for key, value in dict.items():
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if (key_part in key):
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return value['associated_distribution']
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return value['associated_value']
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except KeyError:
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return f"Key '{key_part}' not found."
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def evaluate_custom_distribution(dist: str, params: typing.Dict) -> typing.Any:
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def evaluate_custom_value_type(value_type: str, params: typing.Dict) -> typing.Any:
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"""
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Evaluate a custom distribution.
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Evaluate a custom value type.
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Args:
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dist (str): The type of distribution.
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params (Dict): The parameters for the distribution.
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dist (str): The type of value.
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params (Dict): The parameters for the value type.
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Returns:
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Any: The generated distribution.
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Any: The generated value.
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|
||||
Raises:
|
||||
ValueError: If the distribution type is not recognized.
|
||||
ValueError: If the value type is not recognized.
|
||||
|
||||
"""
|
||||
if dist == 'Linear Space':
|
||||
return np.linspace(params['start'], params['stop'], params['num'])
|
||||
elif dist == 'Normal':
|
||||
if value_type == 'Constant value':
|
||||
return params
|
||||
elif value_type == 'Normal distribution':
|
||||
return Normal(params['normal_mean_gaussian'], params['normal_standard_deviation_gaussian'])
|
||||
elif dist == 'Log-normal':
|
||||
elif value_type == 'Log-normal distribution':
|
||||
return LogNormal(params['lognormal_mean_gaussian'], params['lognormal_standard_deviation_gaussian'])
|
||||
elif dist == 'Uniform':
|
||||
elif value_type == 'Uniform distribution':
|
||||
return Uniform(params['low'], params['high'])
|
||||
else:
|
||||
raise ValueError('Bad request - distribution not found.')
|
||||
raise ValueError('Bad request - value type not found.')
|
||||
|
||||
|
||||
def param_evaluation(root: typing.Dict, param: typing.Union[str, typing.Any]) -> typing.Any:
|
||||
|
|
@ -104,17 +92,10 @@ def param_evaluation(root: typing.Dict, param: typing.Union[str, typing.Any]) ->
|
|||
"""
|
||||
value = root.get(param)
|
||||
|
||||
if isinstance(value, str):
|
||||
if value == 'Custom':
|
||||
custom_distribution: typing.Dict = custom_distribution_lookup(
|
||||
root, 'custom distribution')
|
||||
for d, p in custom_distribution.items():
|
||||
return evaluate_custom_distribution(d, p)
|
||||
|
||||
elif isinstance(value, dict):
|
||||
dist: str = root[param]['associated_distribution']
|
||||
if isinstance(value, dict):
|
||||
value_type: str = root[param]['associated_value']
|
||||
params: typing.Dict = root[param]['parameters']
|
||||
return evaluate_custom_distribution(dist, params)
|
||||
return evaluate_custom_value_type(value_type, params)
|
||||
|
||||
elif isinstance(value, float) or isinstance(value, int):
|
||||
return value
|
||||
|
|
@ -148,21 +129,21 @@ class BLOmodel:
|
|||
# total concentration of aerosols for each mode.
|
||||
@property
|
||||
def cn(self) -> typing.Tuple[float, float, float]:
|
||||
_cn = self.data_registry.BLOmodel['cn']
|
||||
_cn = self.data_registry.expiration_particle['BLOmodel']['cn'] # type: ignore
|
||||
return (_cn['B'],_cn['L'],_cn['O'])
|
||||
|
||||
# Mean of the underlying normal distributions (represents the log of a
|
||||
# diameter in microns), for resp. the B, L and O modes.
|
||||
@property
|
||||
def mu(self) -> typing.Tuple[float, float, float]:
|
||||
_mu = self.data_registry.BLOmodel['mu']
|
||||
_mu = self.data_registry.expiration_particle['BLOmodel']['mu'] # type: ignore
|
||||
return (_mu['B'], _mu['L'], _mu['O'])
|
||||
|
||||
# Std deviation of the underlying normal distribution, for resp.
|
||||
# the B, L and O modes.
|
||||
@property
|
||||
def sigma(self) -> typing.Tuple[float, float, float]:
|
||||
_sigma = self.data_registry.BLOmodel['sigma']
|
||||
_sigma = self.data_registry.expiration_particle['BLOmodel']['sigma'] # type: ignore
|
||||
return (_sigma['B'],_sigma['L'],_sigma['O'])
|
||||
|
||||
def distribution(self, d):
|
||||
|
|
@ -229,19 +210,12 @@ def activity_distributions(data_registry):
|
|||
|
||||
|
||||
# From https://doi.org/10.1101/2021.10.14.21264988 and references therein
|
||||
symptomatic_vl_frequencies = LogCustomKernel(
|
||||
np.array((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)),
|
||||
np.array((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)),
|
||||
kernel_bandwidth=0.1
|
||||
)
|
||||
def symptomatic_vl_frequencies(data_registry):
|
||||
return LogCustomKernel(
|
||||
np.array(data_registry.virological_data['symptomatic_vl_frequencies']['log_variable']),
|
||||
np.array(data_registry.virological_data['symptomatic_vl_frequencies']['frequencies']),
|
||||
kernel_bandwidth=data_registry.virological_data['symptomatic_vl_frequencies']['kernel_bandwidth']
|
||||
)
|
||||
|
||||
|
||||
# Weibull distribution with a shape factor of 3.47 and a scale factor of 7.01.
|
||||
|
|
@ -250,86 +224,86 @@ symptomatic_vl_frequencies = LogCustomKernel(
|
|||
def viral_load(data_registry):
|
||||
return np.linspace(
|
||||
weibull_min.ppf(
|
||||
data_registry.covid_overal_vl_data['start'],
|
||||
c=data_registry.covid_overal_vl_data['shape_factor'],
|
||||
scale=data_registry.covid_overal_vl_data['scale_factor']
|
||||
data_registry.virological_data['covid_overal_vl_data']['start'],
|
||||
c=data_registry.virological_data['covid_overal_vl_data']['shape_factor'],
|
||||
scale=data_registry.virological_data['covid_overal_vl_data']['scale_factor']
|
||||
),
|
||||
weibull_min.ppf(
|
||||
data_registry.covid_overal_vl_data['stop'],
|
||||
c=data_registry.covid_overal_vl_data['shape_factor'],
|
||||
scale=data_registry.covid_overal_vl_data['scale_factor']
|
||||
data_registry.virological_data['covid_overal_vl_data']['stop'],
|
||||
c=data_registry.virological_data['covid_overal_vl_data']['shape_factor'],
|
||||
scale=data_registry.virological_data['covid_overal_vl_data']['scale_factor']
|
||||
),
|
||||
int(data_registry.covid_overal_vl_data['num'])
|
||||
int(data_registry.virological_data['covid_overal_vl_data']['num'])
|
||||
)
|
||||
def frequencies_pdf(data_registry):
|
||||
return weibull_min.pdf(
|
||||
viral_load(data_registry),
|
||||
c=data_registry.covid_overal_vl_data['shape_factor'],
|
||||
scale=data_registry.covid_overal_vl_data['scale_factor']
|
||||
c=data_registry.virological_data['covid_overal_vl_data']['shape_factor'],
|
||||
scale=data_registry.virological_data['covid_overal_vl_data']['scale_factor']
|
||||
)
|
||||
def covid_overal_vl_data(data_registry):
|
||||
return LogCustom(
|
||||
bounds=(data_registry.covid_overal_vl_data['min_bound'], data_registry.covid_overal_vl_data['max_bound']),
|
||||
bounds=(data_registry.virological_data['covid_overal_vl_data']['min_bound'], data_registry.virological_data['covid_overal_vl_data']['max_bound']),
|
||||
function=lambda d: np.interp(
|
||||
d,
|
||||
viral_load(data_registry),
|
||||
frequencies_pdf(data_registry),
|
||||
data_registry.covid_overal_vl_data['interpolation_fp_left'],
|
||||
data_registry.covid_overal_vl_data['interpolation_fp_right']
|
||||
data_registry.virological_data['covid_overal_vl_data']['interpolation_fp_left'],
|
||||
data_registry.virological_data['covid_overal_vl_data']['interpolation_fp_right']
|
||||
),
|
||||
max_function=data_registry.covid_overal_vl_data['max_function']
|
||||
max_function=data_registry.virological_data['covid_overal_vl_data']['max_function']
|
||||
)
|
||||
|
||||
|
||||
# Derived from data in doi.org/10.1016/j.ijid.2020.09.025 and
|
||||
# https://iosh.com/media/8432/aerosol-infection-risk-hospital-patient-care-full-report.pdf (page 60)
|
||||
def viable_to_RNA_ratio_distribution(data_registry):
|
||||
return Uniform(data_registry.viable_to_RNA_ratio_distribution['low'], data_registry.viable_to_RNA_ratio_distribution['high'])
|
||||
def viable_to_RNA_ratio_distribution():
|
||||
return Uniform(0.01, 0.6)
|
||||
|
||||
|
||||
# From discussion with virologists
|
||||
def infectious_dose_distribution(data_registry):
|
||||
return Uniform(data_registry.infectious_dose_distribution['low'], data_registry.infectious_dose_distribution['high'])
|
||||
def infectious_dose_distribution():
|
||||
return Uniform(10., 100.)
|
||||
|
||||
|
||||
# From https://doi.org/10.1101/2021.10.14.21264988 and references therein
|
||||
def virus_distributions(data_registry):
|
||||
vd = data_registry.virus_distributions
|
||||
vd = data_registry.virological_data['virus_distributions']
|
||||
return {
|
||||
'SARS_CoV_2': mc.SARSCoV2(
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2']['viral_load_in_sputum'], data_registry),
|
||||
infectious_dose=evaluate_infectd(vd['SARS_CoV_2']['infectious_dose'], data_registry),
|
||||
viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2']['viable_to_RNA_ratio'], data_registry),
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2'], 'viral_load_in_sputum', data_registry),
|
||||
infectious_dose=param_evaluation(vd['SARS_CoV_2'], 'infectious_dose'),
|
||||
viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2'], 'viable_to_RNA_ratio'),
|
||||
transmissibility_factor=vd['SARS_CoV_2']['transmissibility_factor'],
|
||||
),
|
||||
'SARS_CoV_2_ALPHA': mc.SARSCoV2(
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_ALPHA']['viral_load_in_sputum'], data_registry),
|
||||
infectious_dose=evaluate_infectd(vd['SARS_CoV_2_ALPHA']['infectious_dose'], data_registry),
|
||||
viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_ALPHA']['viable_to_RNA_ratio'], data_registry),
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_ALPHA'], 'viral_load_in_sputum', data_registry),
|
||||
infectious_dose=param_evaluation(vd['SARS_CoV_2_ALPHA'], 'infectious_dose'),
|
||||
viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2_ALPHA'], 'viable_to_RNA_ratio'),
|
||||
transmissibility_factor=vd['SARS_CoV_2_ALPHA']['transmissibility_factor'],
|
||||
),
|
||||
'SARS_CoV_2_BETA': mc.SARSCoV2(
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_BETA']['viral_load_in_sputum'], data_registry),
|
||||
infectious_dose=evaluate_infectd(vd['SARS_CoV_2_BETA']['infectious_dose'], data_registry),
|
||||
viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_BETA']['viable_to_RNA_ratio'], data_registry),
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_BETA'], 'viral_load_in_sputum', data_registry),
|
||||
infectious_dose=param_evaluation(vd['SARS_CoV_2_BETA'], 'infectious_dose'),
|
||||
viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2_BETA'], 'viable_to_RNA_ratio'),
|
||||
transmissibility_factor=vd['SARS_CoV_2_BETA']['transmissibility_factor'],
|
||||
),
|
||||
'SARS_CoV_2_GAMMA': mc.SARSCoV2(
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_GAMMA']['viral_load_in_sputum'], data_registry),
|
||||
infectious_dose=evaluate_infectd(vd['SARS_CoV_2_GAMMA']['infectious_dose'], data_registry),
|
||||
viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_GAMMA']['viable_to_RNA_ratio'], data_registry),
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_GAMMA'], 'viral_load_in_sputum', data_registry),
|
||||
infectious_dose=param_evaluation(vd['SARS_CoV_2_GAMMA'], 'infectious_dose'),
|
||||
viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2_GAMMA'], 'viable_to_RNA_ratio'),
|
||||
transmissibility_factor=vd['SARS_CoV_2_GAMMA']['transmissibility_factor'],
|
||||
),
|
||||
'SARS_CoV_2_DELTA': mc.SARSCoV2(
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_DELTA']['viral_load_in_sputum'], data_registry),
|
||||
infectious_dose=evaluate_infectd(vd['SARS_CoV_2_DELTA']['infectious_dose'], data_registry),
|
||||
viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_DELTA']['viable_to_RNA_ratio'], data_registry),
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_DELTA'], 'viral_load_in_sputum', data_registry),
|
||||
infectious_dose=param_evaluation(vd['SARS_CoV_2_DELTA'], 'infectious_dose'),
|
||||
viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2_DELTA'], 'viable_to_RNA_ratio'),
|
||||
transmissibility_factor=vd['SARS_CoV_2_DELTA']['transmissibility_factor'],
|
||||
),
|
||||
'SARS_CoV_2_OMICRON': mc.SARSCoV2(
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_OMICRON']['viral_load_in_sputum'], data_registry),
|
||||
infectious_dose=evaluate_infectd(vd['SARS_CoV_2_OMICRON']['infectious_dose'], data_registry),
|
||||
viable_to_RNA_ratio=evaluate_vtrr(vd['SARS_CoV_2_OMICRON']['viable_to_RNA_ratio'], data_registry),
|
||||
viral_load_in_sputum=evaluate_vl(vd['SARS_CoV_2_OMICRON'], 'viral_load_in_sputum', data_registry),
|
||||
infectious_dose=param_evaluation(vd['SARS_CoV_2_OMICRON'], 'infectious_dose'),
|
||||
viable_to_RNA_ratio=param_evaluation(vd['SARS_CoV_2_OMICRON'], 'viable_to_RNA_ratio'),
|
||||
transmissibility_factor=vd['SARS_CoV_2_OMICRON']['transmissibility_factor'],
|
||||
),
|
||||
}
|
||||
|
|
@ -347,21 +321,21 @@ def mask_distributions(data_registry):
|
|||
data_registry.mask_distributions['Type I'], 'η_inhale'),
|
||||
η_exhale=param_evaluation(
|
||||
data_registry.mask_distributions['Type I'], 'η_exhale')
|
||||
if data_registry.mask_distributions['Type I']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
|
||||
if data_registry.mask_distributions['Type I'].get('η_exhale') is not None else None
|
||||
),
|
||||
'FFP2': mc.Mask(
|
||||
η_inhale=param_evaluation(
|
||||
data_registry.mask_distributions['FFP2'], 'η_inhale'),
|
||||
η_exhale=param_evaluation(
|
||||
data_registry.mask_distributions['FFP2'], 'η_exhale')
|
||||
if data_registry.mask_distributions['FFP2']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
|
||||
if data_registry.mask_distributions['FFP2'].get('η_exhale') is not None else None
|
||||
),
|
||||
'Cloth': mc.Mask(
|
||||
η_inhale=param_evaluation(
|
||||
data_registry.mask_distributions['Cloth'], 'η_inhale'),
|
||||
η_exhale=param_evaluation(
|
||||
data_registry.mask_distributions['Cloth'], 'η_exhale')
|
||||
if data_registry.mask_distributions['Cloth']['Known filtration efficiency of masks when exhaling?'] == 'Yes' else None,
|
||||
if data_registry.mask_distributions['Cloth'].get('η_exhale') is not None else None
|
||||
),
|
||||
}
|
||||
|
||||
|
|
@ -392,10 +366,10 @@ def expiration_distribution(
|
|||
|
||||
|
||||
def expiration_BLO_factors(data_registry):
|
||||
breathing = data_registry.expiration_BLO_factors['Breathing']
|
||||
speaking = data_registry.expiration_BLO_factors['Speaking']
|
||||
singing = data_registry.expiration_BLO_factors['Singing']
|
||||
shouting = data_registry.expiration_BLO_factors['Shouting']
|
||||
breathing = data_registry.expiration_particle['expiration_BLO_factors']['Breathing']
|
||||
speaking = data_registry.expiration_particle['expiration_BLO_factors']['Speaking']
|
||||
singing = data_registry.expiration_particle['expiration_BLO_factors']['Singing']
|
||||
shouting = data_registry.expiration_particle['expiration_BLO_factors']['Shouting']
|
||||
return {
|
||||
'Breathing': (
|
||||
param_evaluation(breathing, 'B'),
|
||||
|
|
@ -425,8 +399,8 @@ def expiration_distributions(data_registry):
|
|||
exp_type: expiration_distribution(
|
||||
data_registry=data_registry,
|
||||
BLO_factors=BLO_factors,
|
||||
d_min=param_evaluation(data_registry.long_range_expiration_distributions, 'minimum_diameter'),
|
||||
d_max=param_evaluation(data_registry.long_range_expiration_distributions, 'maximum_diameter')
|
||||
d_min=param_evaluation(data_registry.expiration_particle['long_range_expiration_distributions'], 'minimum_diameter'),
|
||||
d_max=param_evaluation(data_registry.expiration_particle['long_range_expiration_distributions'], 'maximum_diameter')
|
||||
)
|
||||
for exp_type, BLO_factors in expiration_BLO_factors(data_registry).items()
|
||||
}
|
||||
|
|
@ -437,8 +411,8 @@ def short_range_expiration_distributions(data_registry):
|
|||
exp_type: expiration_distribution(
|
||||
data_registry=data_registry,
|
||||
BLO_factors=BLO_factors,
|
||||
d_min=param_evaluation(data_registry.short_range_expiration_distributions, 'minimum_diameter'),
|
||||
d_max=param_evaluation(data_registry.short_range_expiration_distributions, 'maximum_diameter')
|
||||
d_min=param_evaluation(data_registry.expiration_particle['short_range_expiration_distributions'], 'minimum_diameter'),
|
||||
d_max=param_evaluation(data_registry.expiration_particle['short_range_expiration_distributions'], 'maximum_diameter')
|
||||
)
|
||||
for exp_type, BLO_factors in expiration_BLO_factors(data_registry).items()
|
||||
}
|
||||
|
|
@ -452,8 +426,8 @@ frequencies = np.array((0.0598036, 0.0946154, 0.1299152, 0.1064905, 0.1099066, 0
|
|||
def short_range_distances(data_registry):
|
||||
return Custom(
|
||||
bounds=(
|
||||
param_evaluation(data_registry.short_range_distances, 'minimum_distance'),
|
||||
param_evaluation(data_registry.short_range_distances, 'maximum_distance')
|
||||
param_evaluation(data_registry.short_range_model['conversational_distance'], 'minimum_distance'),
|
||||
param_evaluation(data_registry.short_range_model['conversational_distance'], 'maximum_distance')
|
||||
),
|
||||
function=lambda x: np.interp(x, distances, frequencies, left=0., right=0.),
|
||||
max_function=0.13
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from caimira.enums import ViralLoads, InfectiousDoses, ViableToRNARatios
|
||||
from caimira.enums import ViralLoads
|
||||
|
||||
|
||||
class DataRegistry:
|
||||
|
|
@ -6,34 +6,42 @@ class DataRegistry:
|
|||
|
||||
version = None
|
||||
|
||||
BLOmodel = {
|
||||
"cn": {
|
||||
"B": 0.06,
|
||||
"L": 0.2,
|
||||
"O": 0.0010008,
|
||||
expiration_particle = {
|
||||
"long_range_expiration_distributions": {
|
||||
"minimum_diameter": 0.1,
|
||||
"maximum_diameter": 30,
|
||||
},
|
||||
"mu": {
|
||||
"B": 0.989541,
|
||||
"L": 1.38629,
|
||||
"O": 4.97673,
|
||||
"short_range_expiration_distributions": {
|
||||
"minimum_diameter": 0.1,
|
||||
"maximum_diameter": 100,
|
||||
},
|
||||
"sigma": {
|
||||
"B": 0.262364,
|
||||
"L": 0.506818,
|
||||
"O": 0.585005,
|
||||
"BLOmodel": {
|
||||
"cn": {"B": 0.06, "L": 0.2, "O": 0.0010008},
|
||||
"mu": {"B": 0.989541, "L": 1.38629, "O": 4.97673},
|
||||
"sigma": {"B": 0.262364, "L": 0.506818, "O": 0.585005},
|
||||
},
|
||||
"expiration_BLO_factors": {
|
||||
"Breathing": {"B": 1., "L": 0., "O": 0., },
|
||||
"Speaking": {"B": 1., "L": 1., "O": 1., },
|
||||
"Singing": {"B": 1., "L": 5., "O": 5., },
|
||||
"Shouting": {"B": 1., "L": 5., "O": 5., },
|
||||
},
|
||||
"particle": {
|
||||
"evaporation_factor": 0.3,
|
||||
}
|
||||
}
|
||||
|
||||
activity_distributions = {
|
||||
"Seated": {
|
||||
"inhalation_rate": {
|
||||
"associated_distribution": "Log-normal",
|
||||
"associated_value": "Log-normal distribution",
|
||||
"parameters": {
|
||||
"lognormal_mean_gaussian": -0.6872121723362303,
|
||||
"lognormal_standard_deviation_gaussian": 0.10498338229297108,
|
||||
},
|
||||
},
|
||||
"exhalation_rate": {
|
||||
"associated_distribution": "Log-normal",
|
||||
"associated_value": "Log-normal distribution",
|
||||
"parameters": {
|
||||
"lognormal_mean_gaussian": -0.6872121723362303,
|
||||
"lognormal_standard_deviation_gaussian": 0.10498338229297108,
|
||||
|
|
@ -42,14 +50,14 @@ class DataRegistry:
|
|||
},
|
||||
"Standing": {
|
||||
"inhalation_rate": {
|
||||
"associated_distribution": "Log-normal",
|
||||
"associated_value": "Log-normal distribution",
|
||||
"parameters": {
|
||||
"lognormal_mean_gaussian": -0.5742377578494785,
|
||||
"lognormal_standard_deviation_gaussian": 0.09373162411398223,
|
||||
},
|
||||
},
|
||||
"exhalation_rate": {
|
||||
"associated_distribution": "Log-normal",
|
||||
"associated_value": "Log-normal distribution",
|
||||
"parameters": {
|
||||
"lognormal_mean_gaussian": -0.5742377578494785,
|
||||
"lognormal_standard_deviation_gaussian": 0.09373162411398223,
|
||||
|
|
@ -58,14 +66,14 @@ class DataRegistry:
|
|||
},
|
||||
"Light activity": {
|
||||
"inhalation_rate": {
|
||||
"associated_distribution": "Log-normal",
|
||||
"associated_value": "Log-normal distribution",
|
||||
"parameters": {
|
||||
"lognormal_mean_gaussian": 0.21380242785625422,
|
||||
"lognormal_standard_deviation_gaussian": 0.09435378091059601,
|
||||
},
|
||||
},
|
||||
"exhalation_rate": {
|
||||
"associated_distribution": "Log-normal",
|
||||
"associated_value": "Log-normal distribution",
|
||||
"parameters": {
|
||||
"lognormal_mean_gaussian": 0.21380242785625422,
|
||||
"lognormal_standard_deviation_gaussian": 0.09435378091059601,
|
||||
|
|
@ -74,14 +82,14 @@ class DataRegistry:
|
|||
},
|
||||
"Moderate activity": {
|
||||
"inhalation_rate": {
|
||||
"associated_distribution": "Log-normal",
|
||||
"associated_value": "Log-normal distribution",
|
||||
"parameters": {
|
||||
"lognormal_mean_gaussian": 0.551771330362601,
|
||||
"lognormal_standard_deviation_gaussian": 0.1894616357138137,
|
||||
},
|
||||
},
|
||||
"exhalation_rate": {
|
||||
"associated_distribution": "Log-normal",
|
||||
"associated_value": "Log-normal distribution",
|
||||
"parameters": {
|
||||
"lognormal_mean_gaussian": 0.551771330362601,
|
||||
"lognormal_standard_deviation_gaussian": 0.1894616357138137,
|
||||
|
|
@ -90,14 +98,14 @@ class DataRegistry:
|
|||
},
|
||||
"Heavy exercise": {
|
||||
"inhalation_rate": {
|
||||
"associated_distribution": "Log-normal",
|
||||
"associated_value": "Log-normal distribution",
|
||||
"parameters": {
|
||||
"lognormal_mean_gaussian": 1.1644665696723049,
|
||||
"lognormal_standard_deviation_gaussian": 0.21744554768657565,
|
||||
},
|
||||
},
|
||||
"exhalation_rate": {
|
||||
"associated_distribution": "Log-normal",
|
||||
"associated_value": "Log-normal distribution",
|
||||
"parameters": {
|
||||
"lognormal_mean_gaussian": 1.1644665696723049,
|
||||
"lognormal_standard_deviation_gaussian": 0.21744554768657565,
|
||||
|
|
@ -105,194 +113,216 @@ class DataRegistry:
|
|||
},
|
||||
},
|
||||
}
|
||||
symptomatic_vl_frequencies = {
|
||||
"log_variable": [
|
||||
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,
|
||||
],
|
||||
"frequencies": [
|
||||
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,
|
||||
],
|
||||
"kernel_bandwidth": 0.1,
|
||||
}
|
||||
covid_overal_vl_data = {
|
||||
"shape_factor": 3.47,
|
||||
"scale_factor": 7.01,
|
||||
"start": 0.01,
|
||||
"stop": 0.99,
|
||||
"num": 30.0,
|
||||
"min_bound": 2,
|
||||
"max_bound": 10,
|
||||
"interpolation_fp_left": 0,
|
||||
"interpolation_fp_right": 0,
|
||||
"max_function": 0.2,
|
||||
}
|
||||
viable_to_RNA_ratio_distribution = {
|
||||
"low": 0.01,
|
||||
"high": 0.6,
|
||||
}
|
||||
infectious_dose_distribution = {
|
||||
"low": 10,
|
||||
"high": 100,
|
||||
}
|
||||
virus_distributions = {
|
||||
"SARS_CoV_2": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
|
||||
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
|
||||
"transmissibility_factor": 1,
|
||||
"infectiousness_days": 14,
|
||||
|
||||
virological_data = {
|
||||
"symptomatic_vl_frequencies": {
|
||||
"log_variable": [
|
||||
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,
|
||||
],
|
||||
"frequencies": [
|
||||
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,
|
||||
],
|
||||
"kernel_bandwidth": 0.1,
|
||||
},
|
||||
"SARS_CoV_2_ALPHA": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
|
||||
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
|
||||
"transmissibility_factor": 0.78,
|
||||
"infectiousness_days": 14,
|
||||
'covid_overal_vl_data': {
|
||||
"shape_factor": 3.47,
|
||||
"scale_factor": 7.01,
|
||||
"start": 0.01,
|
||||
"stop": 0.99,
|
||||
"num": 30.0,
|
||||
"min_bound": 2,
|
||||
"max_bound": 10,
|
||||
"interpolation_fp_left": 0,
|
||||
"interpolation_fp_right": 0,
|
||||
"max_function": 0.2,
|
||||
},
|
||||
"SARS_CoV_2_BETA": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
|
||||
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
|
||||
"transmissibility_factor": 0.8,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_GAMMA": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
|
||||
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
|
||||
"transmissibility_factor": 0.72,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_DELTA": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
|
||||
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
|
||||
"transmissibility_factor": 0.51,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_OMICRON": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
|
||||
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
|
||||
"transmissibility_factor": 0.2,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_Other": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": InfectiousDoses.DISTRIBUTION.value,
|
||||
"viable_to_RNA_ratio": ViableToRNARatios.DISTRIBUTION.value,
|
||||
"transmissibility_factor": 0.1,
|
||||
"infectiousness_days": 14,
|
||||
"virus_distributions": {
|
||||
"SARS_CoV_2": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": {
|
||||
"associated_value": "Uniform distribution",
|
||||
"parameters": {"low": 10, "high": 100},
|
||||
},
|
||||
"viable_to_RNA_ratio": {
|
||||
'associated_value': 'Uniform distribution',
|
||||
'parameters': {'low': 0.01, 'high': 0.6},
|
||||
},
|
||||
"transmissibility_factor": 1,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_ALPHA": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": {
|
||||
"associated_value": "Uniform distribution",
|
||||
"parameters": {"low": 10, "high": 100},
|
||||
},
|
||||
"viable_to_RNA_ratio": {
|
||||
'associated_value': 'Uniform distribution',
|
||||
'parameters': {'low': 0.01, 'high': 0.6},
|
||||
},
|
||||
"transmissibility_factor": 0.78,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_BETA": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": {
|
||||
"associated_value": "Uniform distribution",
|
||||
"parameters": {"low": 10, "high": 100},
|
||||
},
|
||||
"viable_to_RNA_ratio": {
|
||||
'associated_value': 'Uniform distribution',
|
||||
'parameters': {'low': 0.01, 'high': 0.6},
|
||||
},
|
||||
"transmissibility_factor": 0.8,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_GAMMA": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": {
|
||||
"associated_value": "Uniform distribution",
|
||||
"parameters": {"low": 10, "high": 100},
|
||||
},
|
||||
"viable_to_RNA_ratio": {
|
||||
'associated_value': 'Uniform distribution',
|
||||
'parameters': {'low': 0.01, 'high': 0.6},
|
||||
},
|
||||
"transmissibility_factor": 0.72,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_DELTA": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": {
|
||||
"associated_value": "Uniform distribution",
|
||||
"parameters": {"low": 10, "high": 100},
|
||||
},
|
||||
"viable_to_RNA_ratio": {
|
||||
'associated_value': 'Uniform distribution',
|
||||
'parameters': {'low': 0.01, 'high': 0.6},
|
||||
},
|
||||
"transmissibility_factor": 0.51,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
"SARS_CoV_2_OMICRON": {
|
||||
"viral_load_in_sputum": ViralLoads.COVID_OVERALL.value,
|
||||
"infectious_dose": {
|
||||
"associated_value": "Uniform distribution",
|
||||
"parameters": {"low": 10, "high": 100},
|
||||
},
|
||||
"viable_to_RNA_ratio": {
|
||||
'associated_value': 'Uniform distribution',
|
||||
'parameters': {'low': 0.01, 'high': 0.6},
|
||||
},
|
||||
"transmissibility_factor": 0.2,
|
||||
"infectiousness_days": 14,
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
mask_distributions = {
|
||||
"Type I": {
|
||||
"η_inhale": {
|
||||
"associated_distribution": "Uniform",
|
||||
"associated_value": "Uniform distribution",
|
||||
"parameters": {
|
||||
"low": 0.25,
|
||||
"high": 0.80,
|
||||
},
|
||||
},
|
||||
"Known filtration efficiency of masks when exhaling?": "No",
|
||||
"factor_exhale": 1,
|
||||
},
|
||||
"FFP2": {
|
||||
"η_inhale": {
|
||||
"associated_distribution": "Uniform",
|
||||
"associated_value": "Uniform distribution",
|
||||
"parameters": {
|
||||
"low": 0.83,
|
||||
"high": 0.91,
|
||||
},
|
||||
},
|
||||
"Known filtration efficiency of masks when exhaling?": "No",
|
||||
"factor_exhale": 1,
|
||||
},
|
||||
"Cloth": {
|
||||
"η_inhale": {
|
||||
"associated_distribution": "Uniform",
|
||||
"associated_value": "Uniform distribution",
|
||||
"parameters": {
|
||||
"low": 0.05,
|
||||
"high": 0.40,
|
||||
},
|
||||
},
|
||||
"Known filtration efficiency of masks when exhaling?": "Yes",
|
||||
"η_exhale": {
|
||||
"associated_distribution": "Uniform",
|
||||
"associated_value": "Uniform distribution",
|
||||
"parameters": {
|
||||
"low": 0.20,
|
||||
"high": 0.50,
|
||||
|
|
@ -301,50 +331,15 @@ class DataRegistry:
|
|||
"factor_exhale": 1,
|
||||
},
|
||||
}
|
||||
expiration_BLO_factors = {
|
||||
"Breathing": {
|
||||
"B": 1.0,
|
||||
"L": 0.0,
|
||||
"O": 0.0,
|
||||
},
|
||||
"Speaking": {
|
||||
"B": 1.0,
|
||||
"L": 1.0,
|
||||
"O": 1.0,
|
||||
},
|
||||
"Singing": {
|
||||
"B": 1.0,
|
||||
"L": 5.0,
|
||||
"O": 5.0,
|
||||
},
|
||||
"Shouting": {
|
||||
"B": 1.0,
|
||||
"L": 5.0,
|
||||
"O": 5.0,
|
||||
},
|
||||
}
|
||||
long_range_expiration_distributions = {
|
||||
"minimum_diameter": 0.1,
|
||||
"maximum_diameter": 30,
|
||||
}
|
||||
short_range_expiration_distributions = {
|
||||
"minimum_diameter": 0.1,
|
||||
"maximum_diameter": 100,
|
||||
}
|
||||
short_range_distances = {
|
||||
"minimum_distance": 0.5,
|
||||
"maximum_distance": 2.0,
|
||||
}
|
||||
|
||||
####################################
|
||||
|
||||
room = {
|
||||
"defaults": {
|
||||
"inside_temp": 293,
|
||||
"humidity_with_heating": 0.3,
|
||||
"humidity_without_heating": 0.5,
|
||||
},
|
||||
"inside_temp": 293,
|
||||
"humidity_with_heating": 0.3,
|
||||
"humidity_without_heating": 0.5,
|
||||
}
|
||||
|
||||
ventilation = {
|
||||
"natural": {
|
||||
"discharge_factor": {
|
||||
|
|
@ -353,41 +348,38 @@ class DataRegistry:
|
|||
},
|
||||
"infiltration_ventilation": 0.25,
|
||||
}
|
||||
particle = {
|
||||
"evaporation_factor": 0.3,
|
||||
}
|
||||
population_with_virus = {
|
||||
"fraction_of_infectious_virus": 1,
|
||||
}
|
||||
|
||||
concentration_model = {
|
||||
"min_background_concentration": 0.0,
|
||||
"virus_concentration_model": {
|
||||
"min_background_concentration": 0.0,
|
||||
},
|
||||
"CO2_concentration_model": {
|
||||
"CO2_atmosphere_concentration": 440.44,
|
||||
"CO2_fraction_exhaled": 0.042,
|
||||
},
|
||||
}
|
||||
|
||||
short_range_model = {
|
||||
"dilution_factor": {
|
||||
"mouth_diameter": 0.02,
|
||||
"exhalation_coefficient": 2,
|
||||
"tstar": 2,
|
||||
"breathing_cycle": 4,
|
||||
"penetration_coefficients": {
|
||||
"𝛽r1": 0.18,
|
||||
"𝛽r2": 0.2,
|
||||
"𝛽x1": 2.4,
|
||||
},
|
||||
},
|
||||
"conversational_distance": {
|
||||
"minimum_distance": 0.5,
|
||||
"maximum_distance": 2.0,
|
||||
},
|
||||
}
|
||||
exposure_model = {
|
||||
"repeats": 1,
|
||||
|
||||
monte_carlo = {
|
||||
"sample_size": 250000,
|
||||
}
|
||||
conditional_prob_inf_given_viral_load = {
|
||||
"lower_percentile": 0.05,
|
||||
"upper_percentile": 0.95,
|
||||
"min_vl": 2,
|
||||
"max_vl": 10,
|
||||
}
|
||||
monte_carlo_sample_size = 250000
|
||||
|
||||
population_scenario_activity = {
|
||||
"office": {"placeholder": "Office", "activity": "Seated", "expiration": {"Speaking": 1, "Breathing": 2}},
|
||||
"smallmeeting": {
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ class DataService:
|
|||
self._host = host
|
||||
|
||||
@classmethod
|
||||
def create(cls, host: str = "https://caimira-data-api.app.cern.ch"):
|
||||
def create(cls, host: str = "https://caimira-data-api-qa.app.cern.ch"):
|
||||
"""Factory."""
|
||||
return cls(host)
|
||||
|
||||
|
|
|
|||
|
|
@ -186,8 +186,8 @@ def skagit_chorale_mc(data_registry):
|
|||
presence=models.SpecificInterval(((0, 2.5), )),
|
||||
virus=mc.SARSCoV2(
|
||||
viral_load_in_sputum=10**9,
|
||||
infectious_dose=infectious_dose_distribution(data_registry),
|
||||
viable_to_RNA_ratio=viable_to_RNA_ratio_distribution(data_registry),
|
||||
infectious_dose=infectious_dose_distribution(),
|
||||
viable_to_RNA_ratio=viable_to_RNA_ratio_distribution(),
|
||||
transmissibility_factor=1.,
|
||||
),
|
||||
mask=models.Mask.types['No mask'],
|
||||
|
|
@ -230,8 +230,8 @@ def bus_ride_mc(data_registry):
|
|||
presence=models.SpecificInterval(((0, 1.67), )),
|
||||
virus=mc.SARSCoV2(
|
||||
viral_load_in_sputum=5*10**8,
|
||||
infectious_dose=infectious_dose_distribution(data_registry),
|
||||
viable_to_RNA_ratio=viable_to_RNA_ratio_distribution(data_registry),
|
||||
infectious_dose=infectious_dose_distribution(),
|
||||
viable_to_RNA_ratio=viable_to_RNA_ratio_distribution(),
|
||||
transmissibility_factor=1.,
|
||||
),
|
||||
mask=models.Mask.types['No mask'],
|
||||
|
|
|
|||
Loading…
Reference in a new issue