Create new scenario file to test a few scenarios with scaling to effe…

…ctive capabilities

src/scripts/healthsystem/impact_of_const_capabilities_expansion/scenario_impact_of_capabilities_expansion_test_scaling.py

@@ -0,0 +1,152 @@
+"""This Scenario file run the model under different assumptions for HR capabilities expansion in order to estimate the
+impact that is achieved under each.
+
+Run on the batch system using:
+```
+tlo batch-submit src/scripts/healthsystem/impact_of_policy/scenario_impact_of_const_capabilities_expansion.py
+```
+
+or locally using:
+```
+tlo scenario-run src/scripts/healthsystem/impact_of_policy/scenario_impact_of_const_capabilities_expansion.py
+```
+
+"""
+from pathlib import Path
+from typing import Dict
+
+import pandas as pd
+
+from tlo import Date, logging
+from tlo.analysis.utils import get_parameters_for_status_quo, mix_scenarios
+from tlo.methods.fullmodel import fullmodel
+from tlo.methods.scenario_switcher import ScenarioSwitcher
+from tlo.scenario import BaseScenario
+
+
+class ImpactOfHealthSystemMode(BaseScenario):
+    def __init__(self):
+        super().__init__()
+        self.seed = 0
+        self.start_date = Date(2010, 1, 1)
+        self.end_date = self.start_date + pd.DateOffset(years=31)
+        self.pop_size = 100_000
+        self._scenarios = self._get_scenarios()
+        self.number_of_draws = len(self._scenarios)
+        self.runs_per_draw = 2
+
+    def log_configuration(self):
+        return {
+            'filename': 'effect_of_policy',
+            'directory': Path('./outputs'),  # <- (specified only for local running)
+            'custom_levels': {
+                '*': logging.WARNING,
+                'tlo.methods.demography': logging.INFO,
+                'tlo.methods.demography.detail': logging.WARNING,
+                'tlo.methods.healthburden': logging.INFO,
+                'tlo.methods.healthsystem.summary': logging.INFO,
+            }
+        }
+
+    def modules(self):
+        return fullmodel(resourcefilepath=self.resources) + [ScenarioSwitcher(resourcefilepath=self.resources)]
+
+    def draw_parameters(self, draw_number, rng):
+        if draw_number < self.number_of_draws:
+            return list(self._scenarios.values())[draw_number]
+        else:
+            return
+
+    # case 1: gfHE = -0.030, factor = 1.01074
+    # case 2: gfHE = -0.020, factor = 1.02116
+    # case 3: gfHE = -0.015, factor = 1.02637
+    # case 4: gfHE =  0.015, factor = 1.05763
+    # case 5: gfHE =  0.020, factor = 1.06284
+    # case 6: gfHE =  0.030, factor = 1.07326
+
+    def _get_scenarios(self) -> Dict[str, Dict]:
+        """Return the Dict with values for the parameters that are changed, keyed by a name for the scenario.
+        """
+        return {
+            "No growth":
+                mix_scenarios(
+                    get_parameters_for_status_quo(),
+                    {
+                     "HealthSystem": {
+                        "yearly_HR_scaling_mode": "no_scaling",
+                        "year_mode_switch": 2019,
+                        "mode_appt_constraints_postSwitch": 2,
+                        "scale_to_effective_capabilities": True,
+                        "policy_name": "Naive",
+                        "tclose_overwrite": 1,
+                        "tclose_days_offset_overwrite": 7,
+                        "use_funded_or_actual_staffing": "actual",
+                        "year_cons_availability_switch": 2019,
+                        "cons_availability_postSwitch": "all",
+                      },
+                    }
+                ),
+
+            "GDP growth":
+                mix_scenarios(
+                    get_parameters_for_status_quo(),
+                    {
+                     "HealthSystem": {
+                        "yearly_HR_scaling_mode": "GDP_growth",
+                        "year_mode_switch": 2019,
+                        "mode_appt_constraints_postSwitch": 2,
+                        "scale_to_effective_capabilities": True,
+                        "policy_name": "Naive",
+                        "tclose_overwrite": 1,
+                        "tclose_days_offset_overwrite": 7,
+                        "use_funded_or_actual_staffing": "actual",
+                        "year_cons_availability_switch": 2019,
+                        "cons_availability_postSwitch": "all",
+                      },
+                    }
+                ),
+
+            "GDP growth fHE growth case 1":
+                mix_scenarios(
+                    get_parameters_for_status_quo(),
+                    {
+                     "HealthSystem": {
+                        "yearly_HR_scaling_mode": "GDP_growth_fHE_case1",
+                        "year_mode_switch": 2019,
+                        "mode_appt_constraints_postSwitch": 2,
+                        "scale_to_effective_capabilities": True,
+                        "policy_name": "Naive",
+                        "tclose_overwrite": 1,
+                        "tclose_days_offset_overwrite": 7,
+                        "use_funded_or_actual_staffing": "actual",
+                        "year_cons_availability_switch": 2019,
+                        "cons_availability_postSwitch": "all",
+                      },
+                    }
+                ),
+
+            "GDP growth fHE growth case 6":
+                mix_scenarios(
+                    get_parameters_for_status_quo(),
+                    {
+                     "HealthSystem": {
+                        "yearly_HR_scaling_mode": "GDP_growth_fHE_case6",
+                        "year_mode_switch": 2019,
+                        "mode_appt_constraints_postSwitch": 2,
+                        "scale_to_effective_capabilities": True,
+                        "policy_name": "Naive",
+                        "tclose_overwrite": 1,
+                        "tclose_days_offset_overwrite": 7,
+                        "use_funded_or_actual_staffing": "actual",
+                        "year_cons_availability_switch": 2019,
+                        "cons_availability_postSwitch": "all",
+                      },
+                    }
+                ),
+
+        }
+
+if __name__ == '__main__':
+    from tlo.cli import scenario_run
+
+    scenario_run([__file__])