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Option to rescale to "effective capabilities" before switching to mode 2 (#1308)
* Rescale capabilities before switching in mode 2 to capture effective capabilities * Include test for rescaling of capabilities before mode switching * Create scenario file to test when rescaling to effective capabilities, for all scenarios considered * Create new scenario file to test a few scenarios with scaling to effective capabilities * Update src/tlo/methods/healthsystem.py Co-authored-by: Matt Graham <[email protected]> * Decorating scale_to_effective_capabilities and year_mode_switch methods as properties Co-authored-by: Matt Graham <[email protected]> * Call definition methods Co-authored-by: Matt Graham <[email protected]> * Prefix with underscore Co-authored-by: Matt Graham <[email protected]> * Avoid accessing attribute of another object prefixed with an underscore * Replace _frac_time_used_by_officer_type_and_level with _sum_of_daily_frac_time_used_by_officer_type_and_level * Cosmetic corrections to test * Remove commented-out line Co-authored-by: Tim Hallett <[email protected]> * Move scaling inside HealthSystem module * Fix to new HealthSystem function (outdated use of module to access) * Add scenario file * Removed outdated logic * Ensure only rescale if rescaling factor is > 1 and not infinity. Some of officers would have had 0 capability * Style fixes * Remove outdated lines * Style fixes * Remove reference to healthsystem module Co-authored-by: Matt Graham <[email protected]> * Remove reference to module under new design Co-authored-by: Matt Graham <[email protected]> * roll back incidental changes * roll back adding kwargs --------- Co-authored-by: Matt Graham <[email protected]> Co-authored-by: Tim Hallett <[email protected]>
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‎resources/healthsystem/ResourceFile_HealthSystem_parameters.csv

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size 585
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oid sha256:e7e56da62d26488f05ec1a5a10f3790ec9416c029b040f1fed629f314a14816b
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size 622

‎src/scripts/healthsystem/impact_of_const_capabilities_expansion/scenario_impact_of_capabilities_expansion_combined.py

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"""This Scenario file run the model under different assumptions for HR capabilities expansion in order to estimate the
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impact that is achieved under each.
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Run on the batch system using:
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```
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tlo batch-submit src/scripts/healthsystem/impact_of_policy/scenario_impact_of_const_capabilities_expansion.py
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```
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or locally using:
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```
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tlo scenario-run src/scripts/healthsystem/impact_of_policy/scenario_impact_of_const_capabilities_expansion.py
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```
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"""
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from pathlib import Path
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from typing import Dict
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import pandas as pd
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from tlo import Date, logging
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from tlo.analysis.utils import get_parameters_for_status_quo, mix_scenarios
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from tlo.methods.fullmodel import fullmodel
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from tlo.methods.scenario_switcher import ScenarioSwitcher
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from tlo.scenario import BaseScenario
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class ImpactOfHealthSystemMode(BaseScenario):
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def __init__(self):
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super().__init__()
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self.seed = 0
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self.start_date = Date(2010, 1, 1)
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self.end_date = self.start_date + pd.DateOffset(years=31)
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self.pop_size = 100_000
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self._scenarios = self._get_scenarios()
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self.number_of_draws = len(self._scenarios)
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self.runs_per_draw = 2
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def log_configuration(self):
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return {
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'filename': 'effect_of_policy',
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'directory': Path('./outputs'), # <- (specified only for local running)
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'custom_levels': {
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'*': logging.WARNING,
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'tlo.methods.demography': logging.INFO,
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'tlo.methods.demography.detail': logging.WARNING,
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'tlo.methods.healthburden': logging.INFO,
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'tlo.methods.healthsystem.summary': logging.INFO,
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}
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}
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def modules(self):
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return fullmodel(resourcefilepath=self.resources) + [ScenarioSwitcher(resourcefilepath=self.resources)]
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def draw_parameters(self, draw_number, rng):
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if draw_number < self.number_of_draws:
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return list(self._scenarios.values())[draw_number]
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else:
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return
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# case 1: gfHE = -0.030, factor = 1.01074
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# case 2: gfHE = -0.020, factor = 1.02116
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# case 3: gfHE = -0.015, factor = 1.02637
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# case 4: gfHE = 0.015, factor = 1.05763
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# case 5: gfHE = 0.020, factor = 1.06284
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# case 6: gfHE = 0.030, factor = 1.07326
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def _get_scenarios(self) -> Dict[str, Dict]:
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"""Return the Dict with values for the parameters that are changed, keyed by a name for the scenario.
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"""
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return {
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"No growth":
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mix_scenarios(
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get_parameters_for_status_quo(),
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{
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"HealthSystem": {
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"yearly_HR_scaling_mode": "no_scaling",
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"year_mode_switch": 2019,
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"mode_appt_constraints_postSwitch": 2,
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"scale_to_effective_capabilities": True,
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"policy_name": "Naive",
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"tclose_overwrite": 1,
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"tclose_days_offset_overwrite": 7,
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"use_funded_or_actual_staffing": "actual",
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"year_cons_availability_switch": 2019,
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"cons_availability_postSwitch": "all",
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},
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}
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),
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"GDP growth":
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mix_scenarios(
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get_parameters_for_status_quo(),
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{
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"HealthSystem": {
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"yearly_HR_scaling_mode": "GDP_growth",
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"year_mode_switch": 2019,
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"mode_appt_constraints_postSwitch": 2,
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"scale_to_effective_capabilities": True,
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"policy_name": "Naive",
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"tclose_overwrite": 1,
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"tclose_days_offset_overwrite": 7,
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"use_funded_or_actual_staffing": "actual",
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"year_cons_availability_switch": 2019,
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"cons_availability_postSwitch": "all",
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},
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}
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),
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"GDP growth fHE growth case 1":
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mix_scenarios(
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get_parameters_for_status_quo(),
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{
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"HealthSystem": {
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"yearly_HR_scaling_mode": "GDP_growth_fHE_case1",
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"year_mode_switch": 2019,
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"mode_appt_constraints_postSwitch": 2,
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"scale_to_effective_capabilities": True,
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"policy_name": "Naive",
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"tclose_overwrite": 1,
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"tclose_days_offset_overwrite": 7,
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"use_funded_or_actual_staffing": "actual",
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"year_cons_availability_switch": 2019,
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"cons_availability_postSwitch": "all",
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},
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}
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),
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"GDP growth fHE growth case 3":
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mix_scenarios(
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get_parameters_for_status_quo(),
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{
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"HealthSystem": {
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"yearly_HR_scaling_mode": "GDP_growth_fHE_case3",
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"year_mode_switch": 2019,
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"mode_appt_constraints_postSwitch": 2,
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"scale_to_effective_capabilities": True,
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"policy_name": "Naive",
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"tclose_overwrite": 1,
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"tclose_days_offset_overwrite": 7,
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"use_funded_or_actual_staffing": "actual",
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"year_cons_availability_switch": 2019,
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"cons_availability_postSwitch": "all",
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},
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}
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),
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"GDP growth fHE growth case 4":
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mix_scenarios(
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get_parameters_for_status_quo(),
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{
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"HealthSystem": {
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"yearly_HR_scaling_mode": "GDP_growth_fHE_case4",
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"year_mode_switch": 2019,
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"mode_appt_constraints_postSwitch": 2,
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"scale_to_effective_capabilities": True,
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"policy_name": "Naive",
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"tclose_overwrite": 1,
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"tclose_days_offset_overwrite": 7,
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"use_funded_or_actual_staffing": "actual",
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"year_cons_availability_switch": 2019,
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"cons_availability_postSwitch": "all",
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},
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}
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),
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"GDP growth fHE growth case 6":
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mix_scenarios(
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get_parameters_for_status_quo(),
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{
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"HealthSystem": {
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"yearly_HR_scaling_mode": "GDP_growth_fHE_case6",
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"year_mode_switch": 2019,
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"mode_appt_constraints_postSwitch": 2,
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"scale_to_effective_capabilities": True,
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"policy_name": "Naive",
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"tclose_overwrite": 1,
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"tclose_days_offset_overwrite": 7,
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"use_funded_or_actual_staffing": "actual",
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"year_cons_availability_switch": 2019,
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"cons_availability_postSwitch": "all",
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},
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}
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),
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"GDP growth FL case 1 const i":
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mix_scenarios(
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get_parameters_for_status_quo(),
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{
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"HealthSystem": {
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"yearly_HR_scaling_mode": "GDP_growth_FL_case1_const_tot_i",
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"year_mode_switch": 2019,
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"mode_appt_constraints_postSwitch": 2,
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"scale_to_effective_capabilities": True,
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"policy_name": "Naive",
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"tclose_overwrite": 1,
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"tclose_days_offset_overwrite": 7,
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"use_funded_or_actual_staffing": "actual",
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"year_cons_availability_switch": 2019,
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"cons_availability_postSwitch": "all",
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},
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}
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),
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"GDP growth FL case 2 const i":
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mix_scenarios(
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get_parameters_for_status_quo(),
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{
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"HealthSystem": {
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"yearly_HR_scaling_mode": "GDP_growth_FL_case2_const_tot_i",
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"year_mode_switch": 2019,
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"mode_appt_constraints_postSwitch": 2,
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"scale_to_effective_capabilities": True,
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"policy_name": "Naive",
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"tclose_overwrite": 1,
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"tclose_days_offset_overwrite": 7,
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"use_funded_or_actual_staffing": "actual",
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"year_cons_availability_switch": 2019,
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"cons_availability_postSwitch": "all",
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},
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}
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),
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"GDP growth FL case 1 vary i":
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mix_scenarios(
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get_parameters_for_status_quo(),
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{
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"HealthSystem": {
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"yearly_HR_scaling_mode": "GDP_growth_FL_case1_vary_tot_i",
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"year_mode_switch": 2019,
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"mode_appt_constraints_postSwitch": 2,
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"scale_to_effective_capabilities": True,
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"policy_name": "Naive",
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"tclose_overwrite": 1,
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"tclose_days_offset_overwrite": 7,
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"use_funded_or_actual_staffing": "actual",
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"year_cons_availability_switch": 2019,
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"cons_availability_postSwitch": "all",
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},
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}
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),
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"GDP growth FL case 2 vary i":
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mix_scenarios(
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get_parameters_for_status_quo(),
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{
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"HealthSystem": {
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"yearly_HR_scaling_mode": "GDP_growth_FL_case2_vary_tot_i",
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"year_mode_switch": 2019,
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"mode_appt_constraints_postSwitch": 2,
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"scale_to_effective_capabilities": True,
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"policy_name": "Naive",
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"tclose_overwrite": 1,
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"tclose_days_offset_overwrite": 7,
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"use_funded_or_actual_staffing": "actual",
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"year_cons_availability_switch": 2019,
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"cons_availability_postSwitch": "all",
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},
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}
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),
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}
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if __name__ == '__main__':
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from tlo.cli import scenario_run
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scenario_run([__file__])

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