#!/usr/bin/env python3 """ Literature-Informed Medical Oxygen Supply Dataset ==================================================== Generates realistic synthetic facility-level observations of medical oxygen availability, delivery systems, equipment status, and patient outcomes across three tiers of healthcare in sub-Saharan Africa. Each record represents ONE facility observation for ONE monthly period. Epidemiological Parameterization (web-searched): ------------------------------------------------- [1] Oxygen Hub / ITT (2021). Closing the medical oxygen gap in SSA. Most SSA countries have <10% of volume needed. PSA vs LOX trade-offs. Decentralized hub-and-spoke model proposed. [2] PMC (2022). Oxygen inequity in COVID-19 pandemic and beyond. Ghana, Senegal SPRINT pilot. UNICEF workgroups. PMC9972372. [3] PMC (2022). Comprehensive approach to medical oxygen ecosystem. 165 PSA plants needing repair globally; 151 in SSA. PMC9771461. [4] PMC (2021). Oxygen delivery systems for adults in SSA. Scoping review. Hypoxemia prevalence 11-89%. High mortality among hypoxemic patients. PMC8109278. [5] BMC Health Services Research (2025). Design and maintenance of oxygen concentrators in SSA. WHO distributed >30,000 OCs. Maintenance challenges documented. doi:10.1186/s12913-025-12315-6 [6] PMC (2024). Functional availability of medical oxygen for pneumonia management. ~50% of facilities in resource-limited settings had no or inconsistent oxygen. PMC11082622. [7] WHO (2023). Essential medicines list — medical oxygen included. Pulse oximetry essential for identifying hypoxemia. """ import numpy as np import pandas as pd import argparse import os OXYGEN_SOURCES = [ 'PSA_plant_onsite', 'LOX_bulk_tank', 'oxygen_concentrator', 'cylinder_piped', 'cylinder_portable', 'none' ] PATIENT_CONDITIONS = [ 'pneumonia_child', 'pneumonia_adult', 'neonatal_respiratory', 'COPD_exacerbation', 'COVID19_severe', 'severe_malaria', 'heart_failure', 'surgical_anaesthesia', 'trauma', 'asthma_severe', 'sepsis', 'other_hypoxemia' ] SHORTAGE_CAUSES = [ 'cylinder_delivery_delay', 'PSA_plant_breakdown', 'concentrator_malfunction', 'power_outage', 'empty_cylinders_not_collected', 'funding_for_refill', 'no_oxygen_source', 'demand_surge', 'supplier_stockout', 'piping_system_leak', 'regulator_valve_failure', ] SCENARIOS = { 'referral_hospital': { 'description': ( 'Urban referral/teaching hospital with PSA plant or LOX ' 'bulk tank, piped oxygen system, pulse oximetry, ICU, ' 'biomedical technician. Analogous to Muhimbili (TZ), ' 'Kenyatta (KE), Mulago (UG).' ), 'facility_level': 'referral_hospital', 'has_piped_system': True, 'has_icu': True, 'has_pulse_oximeter': True, 'has_biomedical_tech': True, 'oxygen_source_probs': [0.25, 0.30, 0.20, 0.20, 0.05, 0.00], 'oxygen_available_rate': 0.82, 'sufficient_quantity_rate': 0.65, 'concentrator_functional_rate': 0.70, 'pulse_ox_functional_rate': 0.85, 'cylinder_refill_days': 5, 'mortality_hypoxic_untreated': 0.35, }, 'district_hospital': { 'description': ( 'District hospital with oxygen concentrators and/or ' 'cylinders, limited pulse oximetry, no ICU, clinical ' 'officer manages oxygen. Analogous to district hospitals ' 'in Malawi, Rwanda, Mozambique.' ), 'facility_level': 'district_hospital', 'has_piped_system': False, 'has_icu': False, 'has_pulse_oximeter': False, 'has_biomedical_tech': False, 'oxygen_source_probs': [0.02, 0.05, 0.35, 0.15, 0.30, 0.13], 'oxygen_available_rate': 0.50, 'sufficient_quantity_rate': 0.35, 'concentrator_functional_rate': 0.45, 'pulse_ox_functional_rate': 0.40, 'cylinder_refill_days': 21, 'mortality_hypoxic_untreated': 0.45, }, 'rural_health_centre': { 'description': ( 'Rural health centre with no permanent oxygen source, ' 'occasional cylinder if available, no pulse oximetry, ' 'nurse-managed. Analogous to health centres in Niger, ' 'DRC, South Sudan, rural Ethiopia.' ), 'facility_level': 'rural_health_centre', 'has_piped_system': False, 'has_icu': False, 'has_pulse_oximeter': False, 'has_biomedical_tech': False, 'oxygen_source_probs': [0.00, 0.00, 0.08, 0.02, 0.20, 0.70], 'oxygen_available_rate': 0.12, 'sufficient_quantity_rate': 0.08, 'concentrator_functional_rate': 0.15, 'pulse_ox_functional_rate': 0.08, 'cylinder_refill_days': 45, 'mortality_hypoxic_untreated': 0.55, }, } def generate_dataset(n=10000, seed=42, scenario='district_hospital'): rng = np.random.default_rng(seed) sc = SCENARIOS[scenario] records = [] for idx in range(n): rec = {'id': idx + 1} # ── 1. Facility characteristics ── rec['facility_level'] = sc['facility_level'] rec['facility_id'] = f"O2_{rng.integers(1, 200):04d}" rec['region_type'] = rng.choice( ['urban', 'peri_urban', 'rural'], p=[0.10, 0.15, 0.75] if scenario == 'rural_health_centre' else ([0.55, 0.25, 0.20] if scenario == 'referral_hospital' else [0.20, 0.35, 0.45])) rec['bed_count'] = max(5, int(rng.normal( 250 if scenario == 'referral_hospital' else (60 if scenario == 'district_hospital' else 12), 40))) rec['has_icu'] = 1 if sc['has_icu'] else (1 if rng.random() < 0.02 else 0) rec['has_nicu'] = 1 if sc['has_icu'] else (1 if rng.random() < 0.05 else 0) rec['has_piped_oxygen'] = 1 if sc['has_piped_system'] else ( 1 if rng.random() < 0.05 else 0) rec['has_biomedical_technician'] = 1 if sc['has_biomedical_tech'] else ( 1 if rng.random() < 0.05 else 0) # ── 2. Oxygen source & equipment [1][3][5] ── rec['primary_oxygen_source'] = rng.choice( OXYGEN_SOURCES, p=sc['oxygen_source_probs']) rec['concentrator_count'] = 0 if rec['primary_oxygen_source'] == 'oxygen_concentrator' or rng.random() < 0.20: rec['concentrator_count'] = max(0, int(rng.poisson( 5 if scenario == 'referral_hospital' else (2 if scenario == 'district_hospital' else 0.3)))) rec['concentrator_functional'] = 0 if rec['concentrator_count'] > 0: func_rate = sc['concentrator_functional_rate'] rec['concentrator_functional'] = max(0, min( rec['concentrator_count'], int(rec['concentrator_count'] * rng.normal(func_rate, 0.2)))) rec['concentrator_mean_age_years'] = max(0, round(rng.exponential( 3 if scenario == 'referral_hospital' else (5 if scenario == 'district_hospital' else 8)), 1)) rec['concentrator_maintenance_available'] = 1 if rec['has_biomedical_technician'] else ( 1 if rng.random() < 0.10 else 0) rec['cylinder_count_full'] = max(0, int(rng.poisson( 10 if scenario == 'referral_hospital' else (3 if scenario == 'district_hospital' else 0.5)))) rec['cylinder_count_empty'] = max(0, int(rng.poisson( 5 if scenario == 'referral_hospital' else (4 if scenario == 'district_hospital' else 1)))) rec['days_since_cylinder_refill'] = max(0, int(rng.exponential( sc['cylinder_refill_days']))) rec['PSA_plant_functional'] = 0 if rec['primary_oxygen_source'] == 'PSA_plant_onsite': rec['PSA_plant_functional'] = 1 if rng.random() < 0.55 else 0 # ── 3. Pulse oximetry [4][6][7] ── rec['pulse_oximeter_available'] = 1 if sc['has_pulse_oximeter'] else ( 1 if rng.random() < (0.35 if scenario == 'district_hospital' else 0.05) else 0) rec['pulse_oximeter_functional'] = 0 if rec['pulse_oximeter_available']: rec['pulse_oximeter_functional'] = 1 if rng.random() < sc['pulse_ox_functional_rate'] else 0 rec['SpO2_screening_routine'] = 0 if rec['pulse_oximeter_functional']: rec['SpO2_screening_routine'] = 1 if rng.random() < ( 0.70 if scenario == 'referral_hospital' else (0.30 if scenario == 'district_hospital' else 0.05)) else 0 # ── 4. Oxygen availability [1][6] ── rec['oxygen_available_today'] = 1 if rng.random() < sc['oxygen_available_rate'] else 0 rec['oxygen_sufficient_for_demand'] = 0 if rec['oxygen_available_today']: rec['oxygen_sufficient_for_demand'] = 1 if rng.random() < sc['sufficient_quantity_rate'] / sc['oxygen_available_rate'] else 0 rec['oxygen_stockout_days_last_month'] = 0 if not rec['oxygen_available_today']: rec['oxygen_stockout_days_last_month'] = max(1, min(30, int(rng.exponential(12)))) elif rng.random() < 0.20: rec['oxygen_stockout_days_last_month'] = max(1, min(15, int(rng.exponential(5)))) rec['shortage_cause'] = 'not_applicable' if not rec['oxygen_available_today'] or rec['oxygen_stockout_days_last_month'] > 0: if scenario == 'rural_health_centre': cause_p = [0.15, 0.02, 0.05, 0.12, 0.05, 0.10, 0.35, 0.08, 0.05, 0.01, 0.02] elif scenario == 'district_hospital': cause_p = [0.25, 0.05, 0.18, 0.15, 0.08, 0.08, 0.05, 0.06, 0.04, 0.03, 0.03] else: cause_p = [0.10, 0.20, 0.15, 0.15, 0.05, 0.05, 0.02, 0.15, 0.05, 0.05, 0.03] rec['shortage_cause'] = rng.choice(SHORTAGE_CAUSES, p=cause_p) # ── 5. Reporting period ── rec['year'] = rng.choice([2021, 2022, 2023, 2024], p=[0.15, 0.25, 0.30, 0.30]) rec['month'] = rng.integers(1, 13) # ── 6. Patient demand & utilisation ── rec['patients_needing_oxygen'] = max(0, int(rng.poisson( 40 if scenario == 'referral_hospital' else (12 if scenario == 'district_hospital' else 3)))) rec['primary_condition'] = rng.choice(PATIENT_CONDITIONS, p=[0.15, 0.12, 0.12, 0.08, 0.10, 0.08, 0.06, 0.08, 0.06, 0.05, 0.05, 0.05]) rec['patients_received_oxygen'] = 0 if rec['oxygen_available_today'] and rec['patients_needing_oxygen'] > 0: coverage = rng.normal( 0.80 if scenario == 'referral_hospital' else (0.45 if scenario == 'district_hospital' else 0.10), 0.15) rec['patients_received_oxygen'] = max(0, min( rec['patients_needing_oxygen'], int(rec['patients_needing_oxygen'] * np.clip(coverage, 0, 1)))) rec['patients_untreated_hypoxemia'] = max(0, rec['patients_needing_oxygen'] - rec['patients_received_oxygen']) rec['flow_rate_adequate'] = 0 if rec['patients_received_oxygen'] > 0: rec['flow_rate_adequate'] = 1 if rng.random() < ( 0.75 if scenario == 'referral_hospital' else (0.40 if scenario == 'district_hospital' else 0.15)) else 0 # ── 7. Outcomes ── rec['deaths_hypoxemia_related'] = 0 if rec['patients_untreated_hypoxemia'] > 0: mort_rate = sc['mortality_hypoxic_untreated'] rec['deaths_hypoxemia_related'] = max(0, int(rng.binomial(rec['patients_untreated_hypoxemia'], mort_rate))) rec['referred_for_oxygen'] = 0 if not rec['oxygen_available_today'] and rec['patients_needing_oxygen'] > 0: rec['referred_for_oxygen'] = max(0, int( rec['patients_needing_oxygen'] * rng.normal(0.30, 0.15))) # ── 8. Cost & logistics ── rec['monthly_oxygen_cost_usd'] = max(0, round(rng.normal( 800 if scenario == 'referral_hospital' else (200 if scenario == 'district_hospital' else 30), 150 if scenario == 'referral_hospital' else (80 if scenario == 'district_hospital' else 20)), 0)) rec['distance_to_refill_km'] = max(0, round(rng.exponential( 15 if scenario == 'referral_hospital' else (50 if scenario == 'district_hospital' else 120)), 0)) rec['transport_available_for_cylinders'] = 1 if rng.random() < ( 0.85 if scenario == 'referral_hospital' else (0.40 if scenario == 'district_hospital' else 0.10)) else 0 # ── 9. Power supply (critical for concentrators/PSA) ── rec['power_source'] = rng.choice( ['grid_reliable', 'grid_unreliable', 'generator_only', 'solar', 'none'], p=[0.40, 0.30, 0.15, 0.10, 0.05] if scenario == 'referral_hospital' else ([0.10, 0.35, 0.15, 0.15, 0.25] if scenario == 'district_hospital' else [0.02, 0.10, 0.05, 0.08, 0.75])) rec['power_outage_hours_last_week'] = max(0, int(rng.exponential( 5 if scenario == 'referral_hospital' else (15 if scenario == 'district_hospital' else 60)))) records.append(rec) df = pd.DataFrame(records) print(f"\n{'='*65}") print(f"Medical Oxygen Supply — {scenario} (n={n}, seed={seed})") print(f"{'='*65}") print(f"\n Oxygen available today: {df['oxygen_available_today'].mean()*100:.1f}%") print(f" Sufficient for demand: {df['oxygen_sufficient_for_demand'].mean()*100:.1f}%") print(f" Pulse oximeter functional: {df['pulse_oximeter_functional'].mean()*100:.1f}%") print(f" Patients needing O2 (mean): {df['patients_needing_oxygen'].mean():.1f}") print(f" Patients received O2 (mean): {df['patients_received_oxygen'].mean():.1f}") print(f" Deaths hypoxemia (mean): {df['deaths_hypoxemia_related'].mean():.2f}") return df if __name__ == '__main__': parser = argparse.ArgumentParser( description='Generate medical oxygen supply dataset') parser.add_argument('--scenario', type=str, default='district_hospital', choices=list(SCENARIOS.keys())) parser.add_argument('--n', type=int, default=10000) parser.add_argument('--seed', type=int, default=42) parser.add_argument('--output', type=str, default=None) parser.add_argument('--all-scenarios', action='store_true') args = parser.parse_args() os.makedirs('data', exist_ok=True) if args.all_scenarios: for sc_name in SCENARIOS: df = generate_dataset(n=args.n, seed=args.seed, scenario=sc_name) out = os.path.join('data', f'oxygen_{sc_name}.csv') df.to_csv(out, index=False) print(f" -> Saved to {out}\n") else: df = generate_dataset(n=args.n, seed=args.seed, scenario=args.scenario) out = args.output or os.path.join('data', f'oxygen_{args.scenario}.csv') df.to_csv(out, index=False) print(f" -> Saved to {out}")