Nurse Shortages

src/scripts/nurses_analyses/nurses_scenario_analyses.py

@@ -0,0 +1,195 @@
+"""
+This scenario file sets up the scenarios for simulating the effects of nursing staffing levels
+The scenario
+0- Baseline scenario
+1-
+2-
+
+
+"""
+from pathlib import Path
+from typing import Dict
+
+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 ImprovedHealthSystemAndCareSeekingScenarioSwitcher
+from tlo.scenario import BaseScenario
+
+
+class StaffingScenario(BaseScenario):
+    def __init__(self):
+        super().__init__()
+        self.seed = 0
+        self.start_date = Date(2010, 1, 1)
+        self.end_date = Date(2030, 1, 1)
+        self.initial_population_size = 200
+        self._scenarios = self._get_scenarios()
+        self.number_of_draws = len(self._scenarios)
+        self.number_of_draws = 2
+        self.runs_per_draw = 2
+
+    def log_configuration(self):
+        return {
+            'filename': 'nurses_scenario_outputs',
+            '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) + [
+            ImprovedHealthSystemAndCareSeekingScenarioSwitcher(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
+
+    def _default_of_all_scenarios(self) -> Dict:
+        return mix_scenarios(
+            get_parameters_for_status_quo(),
+            {
+                'HealthSystem': {
+                    'mode_appt_constraints': 1,
+                    'mode_appt_constraints_postSwitch': 2,
+                    "scale_to_effective_capabilities": True,
+                    # This happens in the year before mode change, as the model calibration is done by that year
+                    "year_mode_switch": 2020,
+                    'cons_availability': 'default',
+                    'cons_availability_postSwitch': "all",
+                    # 'year_cons_availability_switch': 2025,
+                    'yearly_HR_scaling_mode': 'historical_scaling',  # for 5 years of 2020-2024; source data year 2019
+                    "policy_name": 'Naive',
+                    "tclose_overwrite": 1,
+                    "tclose_days_offset_overwrite": 7,
+                },
+                'ImprovedHealthSystemAndCareSeekingScenarioSwitcher': {
+                    'max_healthcare_seeking': [False, False],
+                    'max_healthsystem_function': [False, False],
+                    'year_of_switch': 2025,
+                }
+            },
+        )
+
+    # def _baseline_scenario(self) -> Dict:
+    #     return mix_scenarios(
+    #         self._default_of_all_scenarios(),
+    #         {
+    #             'HealthSystem': {
+    #                 'ResourceFile_HR_scaling_by_level_and_officer_type': "historical_scaling",
+    #                 'mode_appt_constraints_postSwitch': 2,
+    #                 "use_funded_or_actual_staffing": "actual",
+    #             },
+    #         },
+    #     )
+    #
+    # def _improved_staffing_scenario(self) -> Dict:
+    #     return mix_scenarios(
+    #         self._default_of_all_scenarios(),
+    #         {
+    #             'HealthSystem': {
+    #                 'ResourceFile_HR_scaling_by_level_and_officer_type': "historical_scaling",
+    #                 'mode_appt_constraints_postSwitch': 2,
+    #                 "use_funded_or_actual_staffing": "funded_plus",
+    #             },
+    #         },
+    #     )
+    #
+    # def _worst_case_scenario(self) -> Dict:
+    #     return mix_scenarios(
+    #         self._default_of_all_scenarios(),
+    #         {
+    #             'HealthSystem': {
+    #                 'ResourceFile_HR_scaling_by_level_and_officer_type': "historical_scaling",
+    #                 'mode_appt_constraints_postSwitch': 2,
+    #                 "use_funded_or_actual_staffing": "actual",
+    #             },
+    #         },
+    #     )
+    ####################################################################
+    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 {
+            "Baseline":
+                mix_scenarios(
+                    self._default_of_all_scenarios(),
+                    {
+                        "HealthSystem": {
+                            'ResourceFile_HR_scaling_by_level_and_officer_type': "default",
+                            'mode_appt_constraints_postSwitch': 2,
+                            "use_funded_or_actual_staffing": "actual",
+                        },
+                    }
+                ),
+
+            "Improved Staffing":
+                mix_scenarios(
+                    self._default_of_all_scenarios(),
+                    {
+                        "HealthSystem": {
+                            'ResourceFile_HR_scaling_by_level_and_officer_type': "default",
+                            'mode_appt_constraints_postSwitch': 2,
+                            "use_funded_or_actual_staffing": "funded_plus",
+                        },
+                    }
+                ),
+
+            "Improved Staffing Doubled Establishment":
+                mix_scenarios(
+                    self._default_of_all_scenarios(),
+                    {
+                        "HealthSystem": {
+                            'ResourceFile_HR_scaling_by_level_and_officer_type': "custom_doubling",
+                            'mode_appt_constraints_postSwitch': 2,
+                            "use_funded_or_actual_staffing": "funded_plus",
+                        },
+                    }
+                ),
+
+
+            "Worse Case":
+                mix_scenarios(
+                    self._default_of_all_scenarios(),
+                    {
+                        "HealthSystem": {
+                            'ResourceFile_HR_scaling_by_level_and_officer_type': "custom_worse",
+                            'mode_appt_constraints_postSwitch': 2,
+                            "use_funded_or_actual_staffing": "actual",
+                        },
+                    }
+                ),
+        }
+
+
+
+
+    # To be sensitivity analysis
+    # def _baseline_scenario(self) -> Dict:
+    #     return mix_scenarios(
+    #         self._default_of_all_scenarios(),
+    #         {
+    #             'HealthSystem': {
+    #                 'ResourceFile_HR_scaling_by_level_and_officer_type': "historical_scaling",
+    #                 'year_mode_switch': 2020,
+    #                 'mode_appt_constraints_postSwitch': 2,
+    #                 'scale_to_effective_capabilities': True,
+    #                 "use_funded_or_actual_staffing": "actual",
+    #                 "year_cons_availability_switch": 2025,
+    #                 "cons_availability_postSwitch": "all",
+    #             },
+    #         },
+    #     )
+
+
+if __name__ == '__main__':
+    from tlo.cli import scenario_run
+
+    scenario_run([__file__])