#!/usr/bin/env python3 """ Literature-Informed HIV Test Kit & ARV Supply Dataset ======================================================= Generates realistic synthetic facility-level observations of HIV test kit and antiretroviral (ARV) commodity availability, stockout dynamics, and patient impact across three tiers of healthcare in SSA. Each record = ONE HIV commodity observation at ONE facility for ONE quarterly reporting period. Epidemiological Parameterization (web-searched): ------------------------------------------------- [1] GHSC-PSM (2023). FY23 Q1 report. Over 20.5M patient-years of ARV treatment delivered. 602K in Q1. 79M bottles TLD delivered. [2] GHSC-PSM. Crisis to Confidence. TLD availability increased from 84% to 100% (June 2020–2021), remained 98-100%. Pediatric ARVs (nevirapine suspension) lower availability. [3] UNAIDS (2025). HIV commodity availability fact sheet. 39% of countries low risk of ARV stockout; 61% at moderate-high risk. Complex donor interdependencies. [4] Global Fund (2023). TLD price reduced to <$45/year per patient. [5] PMC (2020). Monitoring pharmacy and test kit stocks in rural Mozambique. PEPFAR-supported. Stockouts documented in rural Zambézia Province. PMC7232670. [6] Nation Africa (2024). Kenya among six countries on brink of running out of HIV drugs. Nevirapine, dolutegravir shortages. """ import numpy as np import pandas as pd import argparse import os HIV_COMMODITIES = [ # (name, category, formulation, pepfar_funded, unit_cost_usd) ('TLD_adult', 'ARV_first_line', 'tablet_FDC', True, 0.12), ('TLE_adult', 'ARV_first_line', 'tablet_FDC', True, 0.16), ('DTG_50mg', 'ARV_component', 'tablet', True, 0.04), ('ATV_r_adult', 'ARV_second_line', 'tablet_FDC', True, 0.30), ('LPV_r_paediatric', 'ARV_paediatric', 'granules', True, 0.50), ('nevirapine_susp', 'ARV_paediatric', 'suspension', True, 0.08), ('ABC_3TC_paediatric', 'ARV_paediatric', 'tablet_dispersible', True, 0.10), ('DTG_10mg_dispersible', 'ARV_paediatric', 'tablet_dispersible', True, 0.05), ('AZT_syrup_PMTCT', 'ARV_PMTCT', 'syrup', True, 0.15), ('HIV_RDT_determine', 'test_kit_screening', 'rapid_test', True, 0.60), ('HIV_RDT_confirmatory', 'test_kit_confirmatory', 'rapid_test', True, 1.20), ('HIV_self_test', 'test_kit_self', 'self_test', True, 2.00), ('viral_load_reagent', 'lab_reagent_VL', 'reagent_cartridge', True, 12.00), ('EID_reagent', 'lab_reagent_EID', 'reagent_cartridge', True, 15.00), ('CD4_reagent', 'lab_reagent_CD4', 'reagent_cartridge', False, 5.00), ('cotrimoxazole_prophylaxis', 'OI_prophylaxis', 'tablet', False, 0.01), ('fluconazole_200mg', 'OI_treatment', 'tablet', False, 0.05), ('isoniazid_TPT', 'TB_preventive', 'tablet', True, 0.02), ] STOCKOUT_CAUSES = [ 'national_procurement_delay', 'PEPFAR_shipment_delay', 'global_fund_disbursement_gap', 'quantification_error', 'district_distribution_delay', 'facility_ordering_late', 'demand_surge_new_patients', 'expired_stock', 'donor_transition_gap', 'regulatory_clearance_delay', 'supply_chain_data_gap', 'theft_diversion', ] SCENARIOS = { 'pepfar_supported_urban': { 'description': ( 'PEPFAR-supported urban ART clinic with electronic LMIS, ' 'dedicated HIV pharmacist, multi-month dispensing (MMD), ' 'direct supply from GHSC-PSM. Analogous to high-volume ' 'ART sites in Kenya, Tanzania, Uganda, South Africa.' ), 'facility_level': 'urban_ART_clinic', 'pepfar_supported': True, 'has_hiv_pharmacist': True, 'lmis_type': 'electronic', 'mmd_available': True, 'art_patients_on_file': 3000, 'base_arv_availability': 0.92, 'base_test_kit_availability': 0.88, 'base_vl_reagent_availability': 0.70, 'paediatric_arv_availability': 0.75, 'stockout_ever_6m_rate': 0.15, 'mean_stockout_days': 8, }, 'district_hospital_art': { 'description': ( 'District hospital ART department with paper LMIS, ' 'clinical officer managing ART, quarterly supply from ' 'district pharmacy. Analogous to district ART sites ' 'in Malawi, Mozambique, Rwanda, Zambia.' ), 'facility_level': 'district_hospital', 'pepfar_supported': True, 'has_hiv_pharmacist': False, 'lmis_type': 'paper', 'mmd_available': False, 'art_patients_on_file': 800, 'base_arv_availability': 0.72, 'base_test_kit_availability': 0.65, 'base_vl_reagent_availability': 0.35, 'paediatric_arv_availability': 0.50, 'stockout_ever_6m_rate': 0.40, 'mean_stockout_days': 22, }, 'rural_health_centre_art': { 'description': ( 'Rural health centre with nurse-led ART, no LMIS, ' 'irregular supply, limited test kits, no viral load ' 'on-site. Analogous to primary ART sites in DRC, ' 'Niger, South Sudan, rural Mozambique.' ), 'facility_level': 'rural_health_centre', 'pepfar_supported': False, 'has_hiv_pharmacist': False, 'lmis_type': 'none', 'mmd_available': False, 'art_patients_on_file': 150, 'base_arv_availability': 0.45, 'base_test_kit_availability': 0.35, 'base_vl_reagent_availability': 0.05, 'paediatric_arv_availability': 0.25, 'stockout_ever_6m_rate': 0.70, 'mean_stockout_days': 45, }, } def generate_dataset(n=10000, seed=42, scenario='district_hospital_art'): rng = np.random.default_rng(seed) sc = SCENARIOS[scenario] records = [] n_commodities = len(HIV_COMMODITIES) for idx in range(n): rec = {'id': idx + 1} # ── 1. Facility ── rec['facility_level'] = sc['facility_level'] rec['facility_id'] = f"ART_{rng.integers(1, 200):04d}" rec['pepfar_supported'] = 1 if sc['pepfar_supported'] else ( 1 if rng.random() < 0.15 else 0) rec['global_fund_supported'] = 1 if rng.random() < ( 0.60 if not sc['pepfar_supported'] else 0.30) else 0 rec['has_hiv_pharmacist'] = 1 if sc['has_hiv_pharmacist'] else ( 1 if rng.random() < 0.05 else 0) rec['lmis_type'] = sc['lmis_type'] rec['lmis_functional'] = 1 if sc['lmis_type'] == 'electronic' else ( 1 if sc['lmis_type'] == 'paper' and rng.random() < 0.55 else 0) rec['mmd_available'] = 1 if sc['mmd_available'] else ( 1 if rng.random() < 0.10 else 0) rec['art_patients_active'] = max(10, int(rng.normal( sc['art_patients_on_file'], sc['art_patients_on_file'] * 0.3))) rec['new_art_initiations_quarter'] = max(0, int(rng.poisson( rec['art_patients_active'] * 0.03))) # ── 2. Commodity ── com_idx = idx % n_commodities com = HIV_COMMODITIES[com_idx] rec['commodity_name'] = com[0] rec['commodity_category'] = com[1] rec['formulation'] = com[2] rec['pepfar_funded_commodity'] = 1 if com[3] else 0 rec['unit_cost_usd'] = com[4] # ── 3. Reporting period ── rec['year'] = rng.choice([2021, 2022, 2023, 2024], p=[0.15, 0.25, 0.30, 0.30]) rec['quarter'] = rng.choice([1, 2, 3, 4]) # ── 4. Availability [2][3] ── if 'paediatric' in rec['commodity_category'] or 'PMTCT' in rec['commodity_category']: base_avail = sc['paediatric_arv_availability'] elif 'test_kit' in rec['commodity_category']: base_avail = sc['base_test_kit_availability'] elif 'lab_reagent' in rec['commodity_category']: base_avail = sc['base_vl_reagent_availability'] elif 'OI' in rec['commodity_category'] or 'TB' in rec['commodity_category']: base_avail = sc['base_arv_availability'] - 0.10 else: base_avail = sc['base_arv_availability'] if rec['pepfar_supported'] and rec['pepfar_funded_commodity']: base_avail = min(0.98, base_avail + 0.10) rec['available_on_survey_day'] = 1 if rng.random() < np.clip(base_avail, 0.05, 0.99) else 0 # ── 5. Stockout metrics [5][6] ── so_mod = 1.0 - base_avail rec['stocked_out_in_last_6m'] = 1 if rng.random() < np.clip( sc['stockout_ever_6m_rate'] * (1 + so_mod), 0.02, 0.95) else 0 rec['stockout_days_last_6m'] = 0 if rec['stocked_out_in_last_6m']: rec['stockout_days_last_6m'] = max(1, min(180, int(rng.exponential(sc['mean_stockout_days'] * 0.7)))) rec['stockout_episodes'] = 0 if rec['stocked_out_in_last_6m']: rec['stockout_episodes'] = max(1, min(6, int(rng.poisson(1.5)))) rec['stockout_cause'] = 'not_applicable' if rec['stocked_out_in_last_6m']: if sc['pepfar_supported']: cause_p = [0.10, 0.15, 0.08, 0.12, 0.15, 0.10, 0.08, 0.05, 0.05, 0.05, 0.05, 0.02] else: cause_p = [0.15, 0.05, 0.12, 0.10, 0.18, 0.08, 0.06, 0.06, 0.08, 0.05, 0.05, 0.02] rec['stockout_cause'] = rng.choice(STOCKOUT_CAUSES, p=cause_p) # ── 6. Supply chain ── rec['months_of_stock_on_hand'] = max(0, round(rng.normal( 4.0 if scenario == 'pepfar_supported_urban' else (2.0 if scenario == 'district_hospital_art' else 0.8), 1.5), 1)) rec['last_resupply_days_ago'] = max(1, int(rng.exponential( 20 if scenario == 'pepfar_supported_urban' else (45 if scenario == 'district_hospital_art' else 75)))) rec['order_fill_rate_pct'] = round(np.clip(rng.normal( 85 if scenario == 'pepfar_supported_urban' else (60 if scenario == 'district_hospital_art' else 35), 15), 5, 100), 1) rec['emergency_order_placed'] = 0 if rec['months_of_stock_on_hand'] < 1: rec['emergency_order_placed'] = 1 if rng.random() < ( 0.60 if scenario == 'pepfar_supported_urban' else (0.25 if scenario == 'district_hospital_art' else 0.05)) else 0 # ── 7. Patient impact ── rec['patients_turned_away'] = 0 if not rec['available_on_survey_day']: rec['patients_turned_away'] = max(0, int(rng.exponential( 15 if scenario == 'pepfar_supported_urban' else (6 if scenario == 'district_hospital_art' else 2)))) rec['art_interruption_due_to_stockout'] = 0 if not rec['available_on_survey_day'] and 'ARV' in rec['commodity_category']: rec['art_interruption_due_to_stockout'] = 1 if rng.random() < ( 0.15 if scenario == 'pepfar_supported_urban' else (0.35 if scenario == 'district_hospital_art' else 0.55)) else 0 rec['regimen_substitution'] = 0 if not rec['available_on_survey_day'] and 'ARV' in rec['commodity_category']: rec['regimen_substitution'] = 1 if rng.random() < 0.25 else 0 rec['testing_not_done_no_kits'] = 0 if not rec['available_on_survey_day'] and 'test_kit' in rec['commodity_category']: rec['testing_not_done_no_kits'] = 1 if rng.random() < 0.70 else 0 rec['viral_load_not_done'] = 0 if not rec['available_on_survey_day'] and 'lab_reagent_VL' in rec['commodity_category']: rec['viral_load_not_done'] = 1 if rng.random() < 0.80 else 0 # ── 8. Data quality ── rec['stock_card_up_to_date'] = 1 if rng.random() < ( 0.85 if scenario == 'pepfar_supported_urban' else (0.45 if scenario == 'district_hospital_art' else 0.10)) else 0 rec['report_submitted_to_district'] = 1 if rng.random() < ( 0.92 if scenario == 'pepfar_supported_urban' else (0.60 if scenario == 'district_hospital_art' else 0.20)) else 0 rec['report_timely'] = 0 if rec['report_submitted_to_district']: rec['report_timely'] = 1 if rng.random() < ( 0.80 if scenario == 'pepfar_supported_urban' else (0.40 if scenario == 'district_hospital_art' else 0.15)) else 0 records.append(rec) df = pd.DataFrame(records) print(f"\n{'='*65}") print(f"HIV Test Kit & ARV Supply — {scenario} (n={n}, seed={seed})") print(f"{'='*65}") print(f"\n Available on survey day: {df['available_on_survey_day'].mean()*100:.1f}%") print(f" Stocked out in 6m: {df['stocked_out_in_last_6m'].mean()*100:.1f}%") print(f" Mean stockout days: {df[df['stocked_out_in_last_6m']==1]['stockout_days_last_6m'].mean():.1f}") print(f" Order fill rate: {df['order_fill_rate_pct'].mean():.1f}%") print(f" ART interruption: {df['art_interruption_due_to_stockout'].mean()*100:.1f}%") return df if __name__ == '__main__': parser = argparse.ArgumentParser( description='Generate HIV test kit & ARV supply dataset') parser.add_argument('--scenario', type=str, default='district_hospital_art', 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'hiv_supply_{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'hiv_supply_{args.scenario}.csv') df.to_csv(out, index=False) print(f" -> Saved to {out}")