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tbhallett
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Capabilities expansion scenarios (#1304)
* Include first set of HR scaling scenarios to consider * First version of scenario file, without perfect consumables from 2018 * Add additional scenarios * Final scenarios * Initially only consider first set of runs. Only do 2 runs per scenario, pop at 100000 * Style fixe * Add consumables switcher * Correct default parameters to have no effect in master, and slightly changed name to better reflect parameters that are being changed. * Correct year in which mode switch takes place * Add cons switch to scenario file * Style fix * typo: acceptable value is 'default' not 'Default' * elaborate comments for new parameters * black formatting --------- Co-authored-by: Tim Hallett <[email protected]>
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‎resources/healthsystem/ResourceFile_HealthSystem_parameters.csv

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version https://git-lfs.github.com/spec/v1
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oid sha256:0c73ab0899a29f032dd90629c01f64fddb903510f8ff789b81335f9e5fb97adc
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size 495
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oid sha256:e2e47086a381ec30c0c4229278fe4567f01ca0f24b52808b542667ae50cb816f
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size 585

‎resources/healthsystem/human_resources/scaling_capabilities/ResourceFile_dynamic_HR_scaling.xlsx

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version https://git-lfs.github.com/spec/v1
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oid sha256:75ac6ae6b873d2b6e95090da64da98c5646e21a6c485ee6a10d5b8bc0e352950
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size 10780
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oid sha256:5caca2d50dc998c2512bc4a401507b4b137fe8868fef4892e73221bbdb06c090
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size 24582

‎src/scripts/healthsystem/impact_of_const_capabilities_expansion/scenario_impact_of_const_capabilities_expansion.py

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"""This Scenario file run the model under different assumptions for the HealthSystem Mode in order to estimate the
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impact that is achieved under each (relative to there being no health system).
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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_policy.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_policy.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) #CHECK
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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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# MUST ADD SWITCH TO PERFECT CONSUMABLE AVAILABILITY IN 2018
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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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"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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"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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"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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"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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"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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"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__])

‎src/tlo/methods/healthsystem.py

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@@ -202,8 +202,10 @@ class HealthSystem(Module):
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'policy_name': Parameter(
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Types.STRING, "Name of priority policy adopted"),
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'year_mode_switch': Parameter(
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Types.INT, "Year in which mode switch in enforced"),
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Types.INT, "Year in which mode switch is enforced"),
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'year_cons_availability_switch': Parameter(
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Types.INT, "Year in which consumable availability switch is enforced. The change happens"
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"on 1st January of that year.)"),
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'priority_rank': Parameter(
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Types.DICT, "Data on the priority ranking of each of the Treatment_IDs to be adopted by "
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" the queueing system under different policies, where the lower the number the higher"
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' to the module initialiser.',
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),
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'mode_appt_constraints_postSwitch': Parameter(
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Types.INT, 'Mode considered after a mode switch in year_mode_switch.')
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Types.INT, 'Mode considered after a mode switch in year_mode_switch.'),
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'cons_availability_postSwitch': Parameter(
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Types.STRING, 'Consumables availability after switch in `year_cons_availability_switch`. Acceptable values'
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'are the same as those for Parameter `cons_availability`.')
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}
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PROPERTIES = {
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sim.schedule_event(HealthSystemChangeMode(self),
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Date(self.parameters["year_mode_switch"], 1, 1))
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# Schedule a consumables availability switch
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sim.schedule_event(
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HealthSystemChangeParameters(
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self,
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parameters={
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'cons_availability': self.parameters['cons_availability_postSwitch']
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}
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),
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Date(self.parameters["year_cons_availability_switch"], 1, 1)
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)
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# Schedule a one-off rescaling of _daily_capabilities broken down by officer type and level.
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# This occurs on 1st January of the year specified in the parameters.
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sim.schedule_event(ConstantRescalingHRCapabilities(self),

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