Health Impact of Realistic Consumable Availability Scenarios

src/scripts/consumables_analyses/scenario_impact_of_consumable_scenarios.py

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+"""
+This file defines a batch run to calculate the health effect of updated consumable availability estimates
+as a result of a supply chain intervention. The following scenarios are considered:
+1. 'default' : this is the benchmark scenario with 2018 levels of consumable availability
+2. 'scenario1' : [Level 1a + 1b] All items perform as well as consumables other than drugs/diagnostic tests
+3. 'scenario2' : [Level 1a + 1b] 1 + All items perform as well as consumables classified as 'Vital' in the Essential Medicines List
+4. 'scenario3' : [Level 1a + 1b] 2 + All facilities perform as well as those in which consumables stock is managed by pharmacists
+5. 'scenario6' : [Level 1a + 1b] All facilities have the same probability of consumable availability as the 75th percentile best performing facility for each individual item
+6. 'scenario7' : [Level 1a + 1b] All facilities have the same probability of consumable availability as the 90th percentile best performing facility for each individual item
+7. 'scenario8' : [Level 1a + 1b] All facilities have the same probability of consumable availability as the 99th percentile best performing facility for each individual item
+8. 'scenario9' : [Level 1a + 1b + 2] All facilities have the same probability of consumable availability as the 99th percentile best performing facility for each individual item
+9. 'scenario10' : [Level 1a + 1b] All programs perform as well as HIV
+10. 'scenario11' : [Level 1a + 1b] All programs perform as well as EPI
+12. 'all': all consumable are always available - provides the theoretical maximum health gains which can be made through improving consumable supply
+
+The batch runs are for a large population for a long time with all disease modules and full use of HSIs.
+Run on the batch system using:
+```tlo batch-submit src/scripts/consumables_analyses/scenario_impact_of_consumable_scenarios.py```
+or locally using:
+    ```tlo scenario-run src/scripts/consumables_analyses/scenario_impact_of_consumable_scenarios.py```
+"""
+
+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.scenario import BaseScenario
+
+class ImpactOfConsumablesScenarios(BaseScenario):
+    def __init__(self):
+        super().__init__()
+        self.seed = 99
+        self.start_date = Date(2010, 1, 1)
+        self.end_date = Date(2019, 12, 31)
+        self.pop_size = 100_000 # large population size for final simulation - 100,000
+        self.number_of_draws = 11  # <- 11 scenarios
+        self.runs_per_draw = 10  # <- repeated this many times
+
+    def log_configuration(self):
+        return {
+            'filename': 'impact_of_consumables_scenarios',
+            'directory': './outputs',  # <- (specified only for local running)
+            'custom_levels': {
+                '*': logging.WARNING,
+                'tlo.methods.demography': logging.INFO,
+                'tlo.methods.healthburden': logging.INFO,
+                "tlo.methods.healthsystem.summary": logging.INFO,
+            }
+        }
+
+    def modules(self):
+        return fullmodel(resourcefilepath=self.resources)
+
+    def draw_parameters(self, draw_number, rng):
+        return {
+            'HealthSystem': {
+                'cons_availability': ['default',
+                                      'scenario1', 'scenario2', 'scenario3',
+                                      'scenario6', 'scenario7', 'scenario8',
+                                      'scenario9', 'scenario10', 'scenario11',
+                                      'all'][draw_number]
+               }
+        }
+
+if __name__ == '__main__':
+    from tlo.cli import scenario_run
+
+    scenario_run([__file__])