#!/usr/bin/env python3 """ Literature-Informed Supply Chain Integrity & Track-and-Trace Dataset ===================================================================== Each record = ONE medicine shipment/consignment assessed for integrity. Sources (v2.0): [1] WHO (2024). Complex supply chains with multiple intermediaries increase risk of product tampering and substitution. [2] GS1 Healthcare. Global standards for pharmaceutical serialization and track-and-trace. GTIN, SSCC, 2D barcodes. [3] African Union/AMRH. Track-and-trace pilots in East Africa, Nigeria, Ghana. Mobile verification systems (mPedigree, Sproxil). [4] EU Falsified Medicines Directive. End-to-end serialization model. Only ~5 African countries have mandatory serialization. [5] USAID GHSC-PSM. Supply chain visibility tools for donor-funded medicines. Temperature monitoring, GPS tracking. """ import numpy as np import pandas as pd import argparse import os SUPPLY_CHAIN_STAGES = [ 'manufacturer', 'primary_wholesaler', 'regional_distributor', 'clearing_agent_port', 'central_medical_store', 'regional_store', 'district_store', 'health_facility', 'retail_pharmacy', ] PRODUCT_TYPES = [ ('essential_medicine', 'tablet', 0.35), ('antimalarial_ACT', 'tablet', 0.15), ('antibiotic', 'capsule', 0.12), ('ARV', 'tablet', 0.08), ('vaccine', 'vial', 0.08), ('injectable', 'ampoule', 0.07), ('oral_liquid', 'bottle', 0.05), ('controlled_substance', 'tablet', 0.03), ('medical_device', 'unit', 0.04), ('biological', 'vial', 0.03), ] SCENARIOS = { 'serialized_track_trace': { 'system': 'full_serialization', 'serialization_coverage': 0.90, 'authentication_rate': 0.85, 'diversion_rate': 0.02, 'tampering_detected': 0.01, 'temperature_monitoring': 0.80, 'GPS_tracking': 0.70, 'sf_entry_rate': 0.03, 'transit_days_mean': 15, 'intermediary_count_mean': 3, }, 'partial_visibility': { 'system': 'partial_visibility', 'serialization_coverage': 0.15, 'authentication_rate': 0.20, 'diversion_rate': 0.08, 'tampering_detected': 0.04, 'temperature_monitoring': 0.25, 'GPS_tracking': 0.15, 'sf_entry_rate': 0.12, 'transit_days_mean': 30, 'intermediary_count_mean': 5, }, 'opaque_uncontrolled': { 'system': 'opaque', 'serialization_coverage': 0.01, 'authentication_rate': 0.02, 'diversion_rate': 0.20, 'tampering_detected': 0.10, 'temperature_monitoring': 0.03, 'GPS_tracking': 0.02, 'sf_entry_rate': 0.30, 'transit_days_mean': 60, 'intermediary_count_mean': 8, }, } def generate_dataset(n=10000, seed=42, scenario='partial_visibility'): rng = np.random.default_rng(seed) sc = SCENARIOS[scenario] records = [] n_prod = len(PRODUCT_TYPES) for idx in range(n): rec = {'id': idx + 1} rec['system'] = sc['system'] rec['shipment_id'] = f"SCT_{rng.integers(1, 5000):05d}" prod = PRODUCT_TYPES[rng.choice(n_prod, p=[p[2] for p in PRODUCT_TYPES])] rec['product_type'] = prod[0] rec['dosage_form'] = prod[1] rec['origin_country'] = rng.choice( ['India', 'China', 'Europe', 'local_SSA', 'USA', 'South_Africa'], p=[0.40, 0.18, 0.10, 0.12, 0.05, 0.15]) rec['destination_country'] = rng.choice( ['Nigeria', 'Kenya', 'Tanzania', 'Ghana', 'Ethiopia', 'DRC', 'Uganda', 'Cameroon', 'Senegal', 'Mozambique'], p=[0.15, 0.12, 0.10, 0.10, 0.10, 0.08, 0.08, 0.08, 0.10, 0.09]) rec['intermediary_count'] = max(1, int(rng.poisson(sc['intermediary_count_mean']))) rec['transit_days'] = max(1, int(rng.exponential(sc['transit_days_mean'] * 0.6))) rec['storage_handoffs'] = max(1, min(rec['intermediary_count'], int(rng.poisson(3)))) rec['serialized'] = 1 if rng.random() < sc['serialization_coverage'] else 0 rec['barcode_2D'] = 1 if rec['serialized'] and rng.random() < 0.85 else 0 rec['GTIN_assigned'] = 1 if rec['serialized'] else (1 if rng.random() < 0.10 else 0) rec['authentication_scanned'] = 1 if rng.random() < sc['authentication_rate'] else 0 rec['mobile_verification_used'] = 1 if rng.random() < ( sc['authentication_rate'] * 0.5) else 0 rec['temperature_monitored'] = 1 if rng.random() < sc['temperature_monitoring'] else 0 rec['temperature_excursion'] = 0 if not rec['temperature_monitored'] or rng.random() < (0.05 if scenario == 'serialized_track_trace' else (0.15 if scenario == 'partial_visibility' else 0.40)): rec['temperature_excursion'] = 1 if rng.random() < 0.30 else 0 rec['GPS_tracked'] = 1 if rng.random() < sc['GPS_tracking'] else 0 rec['customs_cleared'] = 1 if rng.random() < (0.98 if scenario == 'serialized_track_trace' else (0.85 if scenario == 'partial_visibility' else 0.60)) else 0 rec['import_permit_valid'] = 1 if rng.random() < (0.95 if scenario == 'serialized_track_trace' else (0.70 if scenario == 'partial_visibility' else 0.30)) else 0 rec['WHO_prequalified_product'] = 1 if rng.random() < (0.55 if scenario == 'serialized_track_trace' else (0.25 if scenario == 'partial_visibility' else 0.05)) else 0 # Integrity issues rec['diversion_detected'] = 1 if rng.random() < sc['diversion_rate'] else 0 rec['tampering_evidence'] = 1 if rng.random() < sc['tampering_detected'] else 0 rec['packaging_breach'] = 1 if rng.random() < (sc['tampering_detected'] * 1.5) else 0 # SF entry into supply chain base_sf = sc['sf_entry_rate'] if not rec['serialized']: base_sf *= 1.5 if rec['intermediary_count'] > 5: base_sf *= 1.3 if not rec['customs_cleared']: base_sf *= 2.0 if rec['diversion_detected']: base_sf *= 2.0 base_sf = np.clip(base_sf, 0.005, 0.80) rec['sf_product_detected'] = 1 if rng.random() < base_sf else 0 rec['sf_entry_point'] = 'none' if rec['sf_product_detected']: rec['sf_entry_point'] = rng.choice( ['manufacturer_level', 'wholesaler', 'port_customs', 'regional_distribution', 'last_mile', 'retail'], p=[0.15, 0.20, 0.15, 0.20, 0.15, 0.15]) rec['recall_initiated'] = 0 if rec['sf_product_detected'] and rng.random() < ( 0.60 if scenario == 'serialized_track_trace' else (0.15 if scenario == 'partial_visibility' else 0.02)): rec['recall_initiated'] = 1 rec['batch_quantity'] = max(100, int(rng.lognormal(8, 1))) rec['value_usd'] = round(rec['batch_quantity'] * max(0.01, rng.lognormal( np.log(0.50), 0.8)), 2) rec['year'] = rng.choice([2020, 2021, 2022, 2023, 2024], p=[0.10, 0.15, 0.20, 0.25, 0.30]) records.append(rec) df = pd.DataFrame(records) print(f"\n{'='*65}") print(f"Supply Chain — {scenario} (n={n}, seed={seed})") print(f"{'='*65}") print(f" System: {sc['system']}") print(f" Serialized: {df['serialized'].mean()*100:.1f}%") print(f" SF detected: {df['sf_product_detected'].mean()*100:.1f}%") print(f" Diversion: {df['diversion_detected'].mean()*100:.1f}%") print(f" Avg intermediaries: {df['intermediary_count'].mean():.1f}") return df if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--all-scenarios', action='store_true') parser.add_argument('--n', type=int, default=10000) parser.add_argument('--seed', type=int, default=42) args = parser.parse_args() os.makedirs('data', exist_ok=True) if args.all_scenarios: for sc in SCENARIOS: df = generate_dataset(n=args.n, seed=args.seed, scenario=sc) df.to_csv(os.path.join('data', f'sct_{sc}.csv'), index=False) print(f" -> Saved\n") else: df = generate_dataset(n=args.n, seed=args.seed) df.to_csv(os.path.join('data', 'sct_partial_visibility.csv'), index=False)