def load_lbwsg_relative_risk(key: str, location: str):
path = paths.lbwsg_data_path('relative_risk', location)
data = pd.read_hdf(path) # type: pd.DataFrame
data['rei_id'] = risk_factors.low_birth_weight_and_short_gestation.gbd_id
data = utilities.convert_affected_entity(data, 'cause_id')
# RRs for all causes are the same.
data = data[data.affected_entity == 'diarrheal_diseases']
data['affected_entity'] = 'all'
# All lbwsg risk is about mortality.
data.loc[:, 'affected_measure'] = 'excess_mortality_rate'
data = data.filter(vi_globals.DEMOGRAPHIC_COLUMNS
+ ['affected_entity', 'affected_measure', 'parameter']
+ vi_globals.DRAW_COLUMNS)
data = (
data
.groupby(['affected_entity', 'parameter'])
.apply(utilities.normalize, fill_value=1)
.reset_index(drop=True)
)
tmrel_cat = utility_data.get_tmrel_category(risk_factors.low_birth_weight_and_short_gestation)
tmrel_mask = data.parameter == tmrel_cat
data.loc[tmrel_mask, vi_globals.DRAW_COLUMNS] = (
data
.loc[tmrel_mask, vi_globals.DRAW_COLUMNS]
.mask(np.isclose(data.loc[tmrel_mask, vi_globals.DRAW_COLUMNS], 1.0), 1.0)
)
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
validation.validate_for_simulation(data, risk_factors.low_birth_weight_and_short_gestation,
'relative_risk', location)
data = utilities.split_interval(data, interval_column='age', split_column_prefix='age')
data = utilities.split_interval(data, interval_column='year', split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def load_lbwsg_exposure(key: str, location: str):
path = paths.lbwsg_data_path('exposure', location)
data = pd.read_hdf(path) # type: pd.DataFrame
data['rei_id'] = risk_factors.low_birth_weight_and_short_gestation.gbd_id
data = data.drop('modelable_entity_id', 'columns')
data = data[data.parameter != 'cat124'] # LBWSG data has an extra residual category added by get_draws.
data = utilities.filter_data_by_restrictions(data, risk_factors.low_birth_weight_and_short_gestation,
'outer', utility_data.get_age_group_ids())
tmrel_cat = utility_data.get_tmrel_category(risk_factors.low_birth_weight_and_short_gestation)
exposed = data[data.parameter != tmrel_cat]
unexposed = data[data.parameter == tmrel_cat]
# FIXME: We fill 1 as exposure of tmrel category, which is not correct.
data = pd.concat([utilities.normalize(exposed, fill_value=0), utilities.normalize(unexposed, fill_value=1)],
ignore_index=True)
# normalize so all categories sum to 1
cols = list(set(data.columns).difference(vi_globals.DRAW_COLUMNS + ['parameter']))
sums = data.groupby(cols)[vi_globals.DRAW_COLUMNS].sum()
data = (data.groupby('parameter')
.apply(lambda df: df.set_index(cols).loc[:, vi_globals.DRAW_COLUMNS].divide(sums))
.reset_index())
data = data.filter(vi_globals.DEMOGRAPHIC_COLUMNS + vi_globals.DRAW_COLUMNS + ['parameter'])
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
validation.validate_for_simulation(data, risk_factors.low_birth_weight_and_short_gestation,
'exposure', location)
data = utilities.split_interval(data, interval_column='age', split_column_prefix='age')
data = utilities.split_interval(data, interval_column='year', split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def validate_and_reshape_gbd_data(
data: pd.DataFrame,
entity: ModelableEntity,
key: EntityKey,
location: str,
gbd_round_id: int,
age_group_ids: List[int] = None) -> pd.DataFrame:
# from vivarium_inputs.core.get_data
data = vi_utils.reshape(data, value_cols=vi_globals.DRAW_COLUMNS)
# from interface.get_measure
data = _scrub_gbd_conventions(data, location, age_group_ids)
estimation_years = get_gbd_estimation_years(gbd_round_id)
validation_years = pd.DataFrame({
'year_start':
range(min(estimation_years),
max(estimation_years) + 1)
})
validation_years['year_end'] = validation_years['year_start'] + 1
# validate_for_simulation(data, entity, key.measure, location, years=validation_years,
# age_bins=get_gbd_age_bins(age_group_ids))
data = vi_utils.split_interval(data,
interval_column='age',
split_column_prefix='age')
data = vi_utils.split_interval(data,
interval_column='year',
split_column_prefix='year')
data = vi_utils.sort_hierarchical_data(data).droplevel('location')
return data
def load_forecast_data(key: EntityKey, location: str):
location_id = extract.get_location_id(location)
path = paths.forecast_data_path(key)
data = extract.load_forecast_from_xarray(path, location_id)
data = data[data.scenario == project_globals.FORECASTING_SCENARIO].drop(
columns='scenario')
if key == EntityKey('etiology.shigellosis.incidence'):
# Only one draw for incidence
data = pd.concat(project_globals.NUM_DRAWS * [
data.set_index(
['location_id', 'age_group_id', 'sex_id', 'year_id']).value
],
axis=1)
else:
data = data.set_index(
['location_id', 'age_group_id', 'sex_id', 'year_id',
'draw']).unstack()
if len(data.columns) == 100: # Not 1000 draws for everything
data = pd.concat([data] * 10, axis=1)
data.columns = pd.Index([f'draw_{i}' for i in range(1000)])
data = data.reset_index()
data = standardize.normalize(data)
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
data = utilities.split_interval(data,
interval_column='age',
split_column_prefix='age')
data = utilities.split_interval(data,
interval_column='year',
split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def load_lbwsg_paf(key: str, location: str):
path = paths.lbwsg_data_path('population_attributable_fraction', location)
data = pd.read_hdf(path) # type: pd.DataFrame
data['rei_id'] = risk_factors.low_birth_weight_and_short_gestation.gbd_id
data = data[data.metric_id == vi_globals.METRICS['Percent']]
# All lbwsg risk is about mortality.
data = data[data.measure_id.isin([vi_globals.MEASURES['YLLs']])]
temp = []
causes_map = {c.gbd_id: c for c in causes}
# We filter paf age groups by cause level restrictions.
for (c_id, measure), df in data.groupby(['cause_id', 'measure_id']):
cause = causes_map[c_id]
measure = 'yll' if measure == vi_globals.MEASURES['YLLs'] else 'yld'
df = utilities.filter_data_by_restrictions(df, cause, measure, utility_data.get_age_group_ids())
temp.append(df)
data = pd.concat(temp, ignore_index=True)
data = utilities.convert_affected_entity(data, 'cause_id')
data.loc[data['measure_id'] == vi_globals.MEASURES['YLLs'], 'affected_measure'] = 'excess_mortality_rate'
data = (data.groupby(['affected_entity', 'affected_measure'])
.apply(utilities.normalize, fill_value=0)
.reset_index(drop=True))
data = data.filter(vi_globals.DEMOGRAPHIC_COLUMNS
+ ['affected_entity', 'affected_measure']
+ vi_globals.DRAW_COLUMNS)
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
validation.validate_for_simulation(data, risk_factors.low_birth_weight_and_short_gestation,
'population_attributable_fraction', location)
data = utilities.split_interval(data, interval_column='age', split_column_prefix='age')
data = utilities.split_interval(data, interval_column='year', split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def get_demographic_dimensions(location: str) -> pd.DataFrame:
"""Pull the full demographic dimensions for GBD data, standardized to the
expected simulation input format, including scrubbing all GBD conventions
to replace IDs with with meaningful values or ranges.
Parameters
----------
location
Location for which to pull demographic dimension data.
Returns
-------
pandas.DataFrame
Dataframe with age and year bins from GBD, sexes, and the given location.
"""
pop = Population()
data = core.get_data(pop, "demographic_dimensions", location)
data = utilities.scrub_gbd_conventions(data, location)
validation.validate_for_simulation(data, pop, "demographic_dimensions",
location)
data = utilities.split_interval(data,
interval_column="age",
split_column_prefix="age")
data = utilities.split_interval(data,
interval_column="year",
split_column_prefix="year")
return utilities.sort_hierarchical_data(data)
def load_shigella_disability_weight(key: EntityKey, location: str):
location_id = extract.get_location_id(location)
data = _get_raw_demographic_dimensions(location)
data = pd.DataFrame(0, columns=vi_globals.DRAW_COLUMNS, index=data.index)
for sequela in causes.diarrheal_diseases.sequelae:
prevalence = _load_prevalence(sequela, location_id, 'sequela')
disability = _load_diarrhea_sequela_disability_weight(
sequela, location_id)
disability.index = disability.index.set_levels([location_id],
'location_id')
data += prevalence * disability
diarrhea_prevalence = _load_prevalence(causes.diarrheal_diseases,
location_id, 'cause')
data = (data / diarrhea_prevalence).fillna(0).reset_index()
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
data = utilities.split_interval(data,
interval_column='age',
split_column_prefix='age')
data = utilities.split_interval(data,
interval_column='year',
split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def get_population_structure(location: str) -> pd.DataFrame:
"""Pull GBD population data for the given location and standardize to the
expected simulation input format, including scrubbing all GBD conventions
to replace IDs with meaningful values or ranges and expanding over all
demographic dimensions.
Parameters
----------
location
Location for which to pull population data.
Returns
-------
pandas.DataFrame
Dataframe of population data for `location`, standardized to the format
expected by `vivarium` simulations.
"""
pop = Population()
data = core.get_data(pop, "structure", location)
data = utilities.scrub_gbd_conventions(data, location)
validation.validate_for_simulation(data, pop, "structure", location)
data = utilities.split_interval(data,
interval_column="age",
split_column_prefix="age")
data = utilities.split_interval(data,
interval_column="year",
split_column_prefix="year")
return utilities.sort_hierarchical_data(data)
def write_utilization_rate(artifact, location):
key = 'healthcare_entity.outpatient_visits.utilization_rate'
from vivarium_csu_hypertension_sdc import external_data
data_dir = Path(external_data.__file__).parent
data = pd.read_csv(data_dir / f'outpatient_utilization.csv')
loc_id = utility_data.get_location_id(location)
data = data[data.location_id == loc_id].reset_index(drop=True)
data['log_mean'] = np.log(data['outpatient_visits_per_cap_mean'])
data['log_sd'] = (
np.log(data['outpatient_visits_per_cap_95_upper']) -
np.log(data['outpatient_visits_per_cap_95_lower'])) / 1.96
draws = np.exp(
np.random.normal(loc=data['log_mean'],
scale=data['log_sd'],
size=(1000, len(data)))).T
draws = pd.DataFrame(data=draws, columns=globals.DRAW_COLUMNS)
data = pd.concat(
[data[['location_id', 'sex_id', 'age_group_id', 'year_id']], draws],
axis=1)
data = utilities.normalize(data, fill_value=0)
data = data.filter(globals.DEMOGRAPHIC_COLUMNS + globals.DRAW_COLUMNS)
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
data = split_interval(data,
interval_column='age',
split_column_prefix='age')
data = split_interval(data,
interval_column='year',
split_column_prefix='year')
data = utilities.sort_hierarchical_data(data)
artifact.write(key, data)
def get_theoretical_minimum_risk_life_expectancy() -> pd.DataFrame:
"""Pull GBD theoretical minimum risk life expectancy data and standardize
to the expected simulation input format, including binning age parameters
as expected by simulations.
Returns
-------
pandas.DataFrame
Dataframe of theoretical minimum risk life expectancy data, standardized
to the format expected by `vivarium` simulations with binned age parameters.
"""
pop = Population()
data = core.get_data(pop, "theoretical_minimum_risk_life_expectancy",
"Global")
data = utilities.set_age_interval(data)
validation.validate_for_simulation(
data, pop, "theoretical_minimum_risk_life_expectancy", "Global")
data = utilities.split_interval(data,
interval_column="age",
split_column_prefix="age")
data = utilities.split_interval(data,
interval_column="year",
split_column_prefix="year")
return utilities.sort_hierarchical_data(data)
def write_ckd_data(artifact, location):
load = get_load(location)
# Metadata
key = f'cause.chronic_kidney_disease.restrictions'
artifact.write(key, load(key))
# Measures for Disease Model
key = f'cause.chronic_kidney_disease.cause_specific_mortality_rate'
csmr = load(key)
artifact.write(key, csmr.copy())
# Measures for Disease States
key = 'cause.chronic_kidney_disease.prevalence'
prevalence = load(key)
artifact.write(key, prevalence.copy())
key = 'cause.chronic_kidney_disease.disability_weight'
df = gbd.get_incidence_prevalence(causes.chronic_kidney_disease.gbd_id,
utility_data.get_location_id(location))
ylds = df[df.measure_id == globals.MEASURES['YLDs']]
ylds = utilities.filter_data_by_restrictions(
ylds, causes.chronic_kidney_disease, 'yld',
utility_data.get_age_group_ids())
ylds = utilities.normalize(ylds, fill_value=0)
ylds = ylds.filter(globals.DEMOGRAPHIC_COLUMNS + globals.DRAW_COLUMNS)
ylds = utilities.reshape(ylds, value_cols=globals.DRAW_COLUMNS)
ylds = utilities.scrub_gbd_conventions(ylds, location)
ylds = split_interval(ylds,
interval_column='age',
split_column_prefix='age')
ylds = split_interval(ylds,
interval_column='year',
split_column_prefix='year')
ylds = utilities.sort_hierarchical_data(ylds)
dw = (ylds / prevalence).fillna(0).replace([np.inf, -np.inf], 0)
artifact.write(key, dw)
key = 'cause.chronic_kidney_disease.excess_mortality_rate'
emr = (csmr / prevalence).fillna(0).replace([np.inf, -np.inf], 0)
artifact.write(key, emr)
# Measures for Transitions
key = 'cause.chronic_kidney_disease.incidence_rate'
data = core.get_data(causes.chronic_kidney_disease, 'incidence_rate',
location)
data = utilities.scrub_gbd_conventions(data, location)
data = utilities.split_interval(data,
interval_column='age',
split_column_prefix='age')
data = utilities.split_interval(data,
interval_column='year',
split_column_prefix='year')
data = utilities.sort_hierarchical_data(data)
data[
data >
50] = 50 # Russia has absurdly high values in some of the data and it breaks validation.
artifact.write(key, data)
def write_hypertension_medication_data(artifact, location):
external_data_specification = {
'adherence': {
'seed_columns':
['location'], # all adherence will use the same seeds
'distribution': 'beta',
},
'medication_probabilities': {
'seed_columns': [
'location', 'measure', 'thiazide_type_diuretics',
'beta_blockers', 'ace_inhibitors', 'angiotensin_ii_blockers',
'calcium_channel_blockers'
],
'distribution':
'beta',
},
'therapy_category': {
'seed_columns': ['location', 'therapy_category'],
'distribution': 'beta',
},
'treatment_coverage': {
'seed_columns': ['location', 'measure'],
'distribution': 'beta',
},
'drug_efficacy': {
'seed_columns': [
'location', 'drug'
], # don't include dosage so all dosages of same drug will use same seeds
'distribution': 'normal',
},
}
for k, spec in external_data_specification.items():
data = load_external_data(k, location)
data = generate_draws(data, spec['seed_columns'], spec['distribution'])
if set(data.location) == {'Global'}:
# do this post draw generation so all locations use the same draws if data is global
data.location = location
if k == 'medication_probabilities': # drop ACE + ARB single pill because not used
data = data.loc[~((data.ace_inhibitors == 1) &
(data.angiotensin_ii_blockers == 1))]
data = utilities.sort_hierarchical_data(utilities.reshape(data))
if k == 'therapy_category': # normalize so that sum of all categories = 1
data = data.divide(data.sum(axis=0), axis=1)
key = f'health_technology.hypertension_medication.{k}'
data = split_interval(data,
interval_column='age',
split_column_prefix='age')
data = split_interval(data,
interval_column='year',
split_column_prefix='year')
artifact.write(key, data)
def load_demographic_dimensions(key: EntityKey, location: str):
data = _get_raw_demographic_dimensions(location)
data = utilities.scrub_gbd_conventions(data, location)
data = utilities.split_interval(data,
interval_column='age',
split_column_prefix='age')
data = utilities.split_interval(data,
interval_column='year',
split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def get_measure(entity: ModelableEntity, measure: str,
location: str) -> pd.DataFrame:
"""Pull GBD data for measure and entity and prep for simulation input,
including scrubbing all GBD conventions to replace IDs with meaningful
values or ranges and expanding over all demographic dimensions. To pull data
using this function, please have at least 50GB of memory available.
Available measures:
For entity kind 'sequela':
incidence_rate, prevalence, birth_prevalence, disability_weight
For entity kind 'cause':
incidence_rate, prevalence, birth_prevalence, disability_weight,
remission_rate, cause_specific_mortality_rate, excess_mortality_rate
For entity kind 'risk_factor':
exposure, exposure_standard_deviation, exposure_distribution_weights,
relative_risk, population_attributable_fraction, mediation_factors
For entity kind 'etiology':
population_attributable_fraction
For entity kind 'alternative_risk_factor':
exposure, exposure_standard_deviation, exposure_distribution_weights
For entity kind 'covariate':
estimate
Parameters
----------
entity
Entity for which to pull `measure`.
measure
Measure for which to pull data, should be a measure available for the
kind of entity which `entity` is.
location
Location for which to pull data.
Returns
-------
pandas.DataFrame
Dataframe standardized to the format expected by `vivarium` simulations.
"""
data = core.get_data(entity, measure, location)
data = utilities.scrub_gbd_conventions(data, location)
validation.validate_for_simulation(data, entity, measure, location)
data = utilities.split_interval(data,
interval_column="age",
split_column_prefix="age")
data = utilities.split_interval(data,
interval_column="year",
split_column_prefix="year")
return utilities.sort_hierarchical_data(data)
def _load_em_from_meid(location, meid, measure):
location_id = utility_data.get_location_id(location)
data = gbd.get_modelable_entity_draws(meid, location_id)
data = data[data.measure_id == vi_globals.MEASURES[measure]]
data = vi_utils.normalize(data, fill_value=0)
data = data.filter(vi_globals.DEMOGRAPHIC_COLUMNS + vi_globals.DRAW_COLUMNS)
data = vi_utils.reshape(data)
data = vi_utils.scrub_gbd_conventions(data, location)
data = vi_utils.split_interval(data, interval_column='age', split_column_prefix='age')
data = vi_utils.split_interval(data, interval_column='year', split_column_prefix='year')
return vi_utils.sort_hierarchical_data(data)
def write_proportion_hypertensive(artifact, location):
location = location.replace(' ', '_').replace("'", "-").lower()
data = pd.read_hdf(HYPERTENSION_DATA_FOLDER / f'{location}.hdf',
HYPERTENSION_HDF_KEY)
key = f'risk_factor.high_systolic_blood_pressure.{HYPERTENSION_HDF_KEY}'
data = split_interval(data,
interval_column='age',
split_column_prefix='age')
data = split_interval(data,
interval_column='year',
split_column_prefix='year')
artifact.write(key, data)
def load_ikf_paf(key: str, location: str) -> pd.DataFrame:
key = EntityKey(key)
entity = get_entity(key)
value_cols = vi_globals.DRAW_COLUMNS
location_id = utility_data.get_location_id(location)
data = extract.extract_data(entity, 'population_attributable_fraction', location_id, validate=False)
relative_risk = extract.extract_data(entity, 'relative_risk', location_id, validate=False)
yll_only_causes = set([c.gbd_id for c in causes if c.restrictions.yll_only])
data = data[~data.cause_id.isin(yll_only_causes)]
relative_risk = relative_risk[~relative_risk.cause_id.isin(yll_only_causes)]
data = (data.groupby('cause_id', as_index=False)
.apply(core.filter_by_relative_risk, relative_risk)
.reset_index(drop=True))
causes_map = {c.gbd_id: c for c in causes}
temp = []
# We filter paf age groups by cause level restrictions.
for (c_id, measure), df in data.groupby(['cause_id', 'measure_id']):
cause = causes_map[c_id]
measure = 'yll' if measure == vi_globals.MEASURES['YLLs'] else 'yld'
df = utilities.filter_data_by_restrictions(df, cause, measure, utility_data.get_age_group_ids())
temp.append(df)
data = pd.concat(temp, ignore_index=True)
data = utilities.convert_affected_entity(data, 'cause_id')
data.loc[data['measure_id'] == vi_globals.MEASURES['YLLs'], 'affected_measure'] = 'excess_mortality_rate'
data.loc[data['measure_id'] == vi_globals.MEASURES['YLDs'], 'affected_measure'] = 'incidence_rate'
data = (data.groupby(['affected_entity', 'affected_measure'])
.apply(utilities.normalize, fill_value=0)
.reset_index(drop=True))
data = data.filter(vi_globals.DEMOGRAPHIC_COLUMNS + ['affected_entity', 'affected_measure']
+ vi_globals.DRAW_COLUMNS)
data = utilities.reshape(data, value_cols=value_cols)
data = utilities.scrub_gbd_conventions(data, location)
sim_validation.validate_for_simulation(data, entity, key.measure, location)
data = utilities.split_interval(data, interval_column='age', split_column_prefix='age')
data = utilities.split_interval(data, interval_column='year', split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def load_healthcare_utilization(key: str, location: str) -> pd.DataFrame:
data = pd.read_csv(paths.HEALTHCARE_UTILIZATION,
dtype={'location_id': np.int64, 'sex_id': np.int64, 'age_group_id': np.int64,
'year_id': np.int64, 'outpatient_visits_per_cap_mean': np.float64,
'outpatient_visits_per_cap_95_upper': np.float64,
'outpatient_visits_per_cap_95_lower': np.float64})
loc_id = utility_data.get_location_id(location)
data = data[data.location_id == loc_id].reset_index(drop=True)
data['log_mean'] = np.log(data['outpatient_visits_per_cap_mean'])
data['log_sd'] = (np.log(data['outpatient_visits_per_cap_95_upper'])
- np.log(data['outpatient_visits_per_cap_95_lower'])) / 1.96
draws = np.exp(np.random.normal(loc=data['log_mean'], scale=data['log_sd'], size=(1000, len(data)))).T
draws = pd.DataFrame(data=draws, columns=vi_globals.DRAW_COLUMNS)
data = pd.concat([data[['location_id', 'sex_id', 'age_group_id', 'year_id']], draws], axis=1)
data = utilities.normalize(data, fill_value=0)
data = data.filter(vi_globals.DEMOGRAPHIC_COLUMNS + vi_globals.DRAW_COLUMNS)
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
data = utilities.split_interval(data, interval_column='age', split_column_prefix='age')
data = utilities.split_interval(data, interval_column='year', split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def load_shigella_remission_rate(key: EntityKey, location: str):
location_id = extract.get_location_id(location)
data = extract.get_modelable_entity_draws(
causes.diarrheal_diseases.dismod_id, location_id)
data = data[data.measure_id == vi_globals.MEASURES['Remission rate']]
data = utilities.filter_data_by_restrictions(
data, causes.diarrheal_diseases, 'yld',
utility_data.get_age_group_ids())
data = data[data.year_id == 2016].drop(
columns='year_id') # Use latest GBD results for all data
data = standardize.normalize(data, fill_value=0)
data = data.filter(vi_globals.DEMOGRAPHIC_COLUMNS +
vi_globals.DRAW_COLUMNS)
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
data = utilities.split_interval(data,
interval_column='age',
split_column_prefix='age')
data = utilities.split_interval(data,
interval_column='year',
split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def load_ikf_exposure(key: str, location: str) -> pd.DataFrame:
key = EntityKey(key)
entity = get_entity(key)
location_id = utility_data.get_location_id(location) if isinstance(location, str) else location
measure = 'exposure'
raw_validation.check_metadata(entity, measure)
data = gbd.get_exposure(entity.gbd_id, location_id)
data = normalize_ikf_exposure_distribution(data)
raw_validation.validate_raw_data(data, entity, measure, location_id)
data = data.drop('modelable_entity_id', 'columns')
data = utilities.filter_data_by_restrictions(data, entity, 'outer', utility_data.get_age_group_ids())
tmrel_cat = utility_data.get_tmrel_category(entity)
exposed = data[data.parameter != tmrel_cat]
unexposed = data[data.parameter == tmrel_cat]
# FIXME: We fill 1 as exposure of tmrel category, which is not correct.
data = pd.concat([utilities.normalize(exposed, fill_value=0), utilities.normalize(unexposed, fill_value=1)],
ignore_index=True)
# normalize so all categories sum to 1
cols = list(set(data.columns).difference(vi_globals.DRAW_COLUMNS + ['parameter']))
sums = data.groupby(cols)[vi_globals.DRAW_COLUMNS].sum()
data = (data
.groupby('parameter')
.apply(lambda df: df.set_index(cols).loc[:, vi_globals.DRAW_COLUMNS].divide(sums))
.reset_index())
data = data.filter(vi_globals.DEMOGRAPHIC_COLUMNS + vi_globals.DRAW_COLUMNS + ['parameter'])
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
sim_validation.validate_for_simulation(data, entity, key.measure, location)
data = utilities.split_interval(data, interval_column='age', split_column_prefix='age')
data = utilities.split_interval(data, interval_column='year', split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def get_age_bins() -> pd.DataFrame:
"""Pull GBD age bin data and standardize to the expected simulation input
format.
Returns
-------
pandas.DataFrame
Dataframe of age bin data, with bin start and end values as well as bin
names.
"""
pop = Population()
data = core.get_data(pop, "age_bins", "Global")
data = utilities.set_age_interval(data)
validation.validate_for_simulation(data, pop, "age_bins", "Global")
data = utilities.split_interval(data,
interval_column="age",
split_column_prefix="age")
data = utilities.split_interval(data,
interval_column="year",
split_column_prefix="year")
return utilities.sort_hierarchical_data(data)
def load_ikf_relative_risk(key: str, location: str) -> pd.DataFrame:
key = EntityKey(key)
entity = get_entity(key)
value_cols = vi_globals.DRAW_COLUMNS
location_id = utility_data.get_location_id(location)
data = extract.extract_data(entity, 'relative_risk', location_id, validate=False)
yll_only_causes = set([c.gbd_id for c in causes if c.restrictions.yll_only])
data = data[~data.cause_id.isin(yll_only_causes)]
data = utilities.convert_affected_entity(data, 'cause_id')
data = data[data['affected_entity'].isin(project_globals.DISEASE_MODELS)]
morbidity = data.morbidity == 1
mortality = data.mortality == 1
data.loc[morbidity & mortality, 'affected_measure'] = 'incidence_rate'
data.loc[morbidity & ~mortality, 'affected_measure'] = 'incidence_rate'
data.loc[~morbidity & mortality, 'affected_measure'] = 'excess_mortality_rate'
data = core.filter_relative_risk_to_cause_restrictions(data)
data = (data.groupby(['affected_entity', 'parameter'])
.apply(utilities.normalize, fill_value=1)
.reset_index(drop=True))
data = data.filter(vi_globals.DEMOGRAPHIC_COLUMNS + ['affected_entity', 'affected_measure', 'parameter']
+ vi_globals.DRAW_COLUMNS)
tmrel_cat = utility_data.get_tmrel_category(entity)
tmrel_mask = data.parameter == tmrel_cat
data.loc[tmrel_mask, value_cols] = (
data.loc[tmrel_mask, value_cols].mask(np.isclose(data.loc[tmrel_mask, value_cols], 1.0), 1.0)
)
data = utilities.reshape(data, value_cols=value_cols)
data = utilities.scrub_gbd_conventions(data, location)
sim_validation.validate_for_simulation(data, entity, key.measure, location)
data = utilities.split_interval(data, interval_column='age', split_column_prefix='age')
data = utilities.split_interval(data, interval_column='year', split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def load_location_specific_life_expectancy(key: EntityKey, location: str):
location_id = extract.get_location_id(location)
data = extract.get_location_specific_life_expectancy(location_id)
data = data.rename(columns={'age': 'age_start'})
data['age_end'] = data.age_start.shift(-1).fillna(5.01)
earliest_year = data[data.year_id == 2025]
out = []
for year in range(project_globals.MIN_YEAR, 2025):
df = earliest_year.copy()
df['year_id'] = year
out.append(df)
data = pd.concat(out + [data], ignore_index=True)
data = utilities.normalize_sex(data, None, ['value'])
data = standardize.normalize_year(data)
data = utilities.reshape(data, value_cols=['value'])
data = utilities.scrub_gbd_conventions(data, location)
data = utilities.split_interval(data,
interval_column='year',
split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def load_lri_birth_prevalence_from_meid(_, location):
"""Ignore the first argument to fit in to the get_data model. """
location_id = utility_data.get_location_id(location)
data = get_draws('modelable_entity_id', project_globals.LRI_BIRTH_PREVALENCE_MEID,
source=project_globals.LRI_BIRTH_PREVALENCE_DRAW_SOURCE,
age_group_id=project_globals.LRI_BIRTH_PREVALENCE_AGE_ID,
measure_id=vi_globals.MEASURES['Prevalence'],
gbd_round_id=project_globals.LRI_BIRTH_PREVALENCE_GBD_ROUND,
location_id=location_id)
data = data[data.measure_id == vi_globals.MEASURES['Prevalence']]
data = utilities.normalize(data, fill_value=0)
idx_columns = list(vi_globals.DEMOGRAPHIC_COLUMNS)
idx_columns.remove('age_group_id')
data = data.filter(idx_columns + vi_globals.DRAW_COLUMNS)
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
data = utilities.split_interval(data, interval_column='year', split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def load_live_births_by_year(key: EntityKey, location: str):
location_id = extract.get_location_id(location)
asfr_key = EntityKey('covariate.age_specific_fertility_rate.estimate')
pop_key = EntityKey(project_globals.POPULATION_STRUCTURE)
asfr_data = extract.load_forecast_from_xarray(
paths.forecast_data_path(asfr_key), location_id)
asfr_data = asfr_data[
(asfr_data.scenario == project_globals.FORECASTING_SCENARIO)
& (asfr_data.year_id >= project_globals.MIN_YEAR)].drop(
columns='scenario')
asfr_data = asfr_data.set_index(
['location_id', 'age_group_id', 'sex_id', 'year_id',
'draw']).unstack()
asfr_data.columns = pd.Index([f'draw_{i}' for i in range(1000)])
pop_data = extract.load_forecast_from_xarray(
paths.forecast_data_path(pop_key), location_id)
pop_data = pop_data[(
pop_data.scenario == project_globals.FORECASTING_SCENARIO)].drop(
columns='scenario')
pop_data = pop_data.set_index(
['location_id', 'age_group_id', 'sex_id', 'year_id',
'draw']).unstack()
pop_data.columns = pd.Index([f'draw_{i}' for i in range(1000)])
pop_data = pop_data.loc[asfr_data.index]
live_births = asfr_data * pop_data
live_births = (live_births.reset_index().drop(
columns=['sex_id', 'age_group_id']).groupby(['location_id', 'year_id'
]).sum().reset_index())
data = standardize.normalize(live_births)
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
data = utilities.split_interval(data,
interval_column='year',
split_column_prefix='year')
return utilities.sort_hierarchical_data(data)
def write_sbp_data(artifact, location):
load = get_load(location)
affected_entity_map = {
'ischemic_heart_disease': 'acute_myocardial_infarction',
'ischemic_stroke': 'acute_ischemic_stroke',
'intracerebral_hemorrhage': 'acute_intracerebral_hemorrhage',
'subarachnoid_hemorrhage': 'acute_subarachnoid_hemorrhage',
'chronic_kidney_disease': 'chronic_kidney_disease'
}
prefix = 'risk_factor.high_systolic_blood_pressure.'
measures = [
"restrictions", "distribution", "tmred", "exposure",
"exposure_standard_deviation", "relative_risk_scalar",
"exposure_distribution_weights"
]
for m in measures:
key = prefix + m
artifact.write(key, load(key))
sbp = risk_factors.high_systolic_blood_pressure
data = gbd.get_paf(sbp.gbd_id, utility_data.get_location_id(location))
data = data[data.metric_id == globals.METRICS['Percent']]
data = data[data.measure_id == globals.MEASURES['YLDs']]
data = utilities.convert_affected_entity(data, 'cause_id')
data.loc[data['measure_id'] == globals.MEASURES['YLDs'],
'affected_measure'] = 'incidence_rate'
data = (data.groupby(['affected_entity', 'affected_measure'
]).apply(utilities.normalize,
fill_value=0).reset_index(drop=True))
data = data.loc[data.affected_entity.isin(affected_entity_map.keys())]
data.affected_entity.replace(to_replace=affected_entity_map, inplace=True)
data = data.filter(globals.DEMOGRAPHIC_COLUMNS +
['affected_entity', 'affected_measure'] +
globals.DRAW_COLUMNS)
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
data = split_interval(data,
interval_column='age',
split_column_prefix='age')
data = split_interval(data,
interval_column='year',
split_column_prefix='year')
data = utilities.sort_hierarchical_data(data)
key = prefix + 'population_attributable_fraction'
artifact.write(key, data)
data = gbd.get_relative_risk(sbp.gbd_id,
utility_data.get_location_id(location))
data = utilities.convert_affected_entity(data, 'cause_id')
morbidity = data.morbidity == 1
mortality = data.mortality == 1
data.loc[morbidity & mortality, 'affected_measure'] = 'incidence_rate'
data.loc[morbidity & ~mortality, 'affected_measure'] = 'incidence_rate'
data.loc[~morbidity & mortality, 'affected_measure'] = 'excess_mortality'
data = data.loc[data.affected_entity.isin(affected_entity_map.keys())]
data = core.filter_relative_risk_to_cause_restrictions(data)
data.affected_entity.replace(to_replace=affected_entity_map, inplace=True)
data = data.filter(globals.DEMOGRAPHIC_COLUMNS +
['affected_entity', 'affected_measure', 'parameter'] +
globals.DRAW_COLUMNS)
data = (data.groupby(['affected_entity', 'parameter'
]).apply(utilities.normalize,
fill_value=1).reset_index(drop=True))
data = utilities.reshape(data)
data = utilities.scrub_gbd_conventions(data, location)
data = utilities.sort_hierarchical_data(data)
data = split_interval(data,
interval_column='age',
split_column_prefix='age')
data = split_interval(data,
interval_column='year',
split_column_prefix='year')
loc = location.lower().replace(' ', '_')
ckd_rr = pd.read_hdf(
f'/share/costeffectiveness/artifacts/vivarium_csu_hypertension_sdc/ckd_rr/{loc}.hdf'
)
ckd_rr = ckd_rr.reset_index()
ckd_rr['parameter'] = 'per unit'
ckd_rr['affected_entity'] = 'chronic_kidney_disease'
ckd_rr['affected_measure'] = 'incidence_rate'
ckd_rr = ckd_rr.set_index([
'location', 'sex', 'age_start', 'year_start', 'affected_entity',
'affected_measure', 'parameter', 'age_end', 'year_end'
])
data = pd.concat([data, ckd_rr])
key = prefix + 'relative_risk'
artifact.write(key, data)
