#!/usr/bin/env python3 """ Literature-Informed Online Pharmacy & E-Pharmacy Regulation Dataset ===================================================================== Each record = ONE online medicine purchase/listing assessed. Sources (v2.0): [1] WHO (2024). SF products increasingly sold online. Rise of unauthorized e-pharmacy sites exacerbates SF problem. [2] NABP (2023). 95% of online pharmacies globally do not comply with pharmacy laws and practice standards. [3] Interpol Operation Pangea. Annual crackdown on illicit online pharmacies. 2022: 4,000+ websites shut down. [4] LegitScript. ~30,000+ rogue pharmacy websites identified. Most sell prescription medicines without prescriptions. [5] WHO Africa (2023). E-pharmacy regulation nascent in SSA. Only 5-10 countries have specific e-pharmacy legislation. """ import numpy as np import pandas as pd import argparse import os PRODUCT_CATEGORIES = [ ('erectile_dysfunction', 'lifestyle', 0.15), ('weight_loss', 'lifestyle', 0.10), ('antibiotic', 'prescription', 0.12), ('opioid_analgesic', 'controlled', 0.08), ('benzodiazepine', 'controlled', 0.05), ('antimalarial', 'essential', 0.08), ('ARV_HIV', 'essential', 0.04), ('antihypertensive', 'NCD', 0.06), ('antidiabetic', 'NCD', 0.05), ('contraceptive', 'reproductive', 0.04), ('skin_lightening', 'cosmetic', 0.06), ('COVID_treatment', 'pandemic', 0.03), ('traditional_herbal', 'unregulated', 0.05), ('supplement_vitamin', 'OTC', 0.05), ('cancer_medicine', 'specialty', 0.04), ] SCENARIOS = { 'licensed_e_pharmacy': { 'platform_type': 'licensed_e_pharmacy', 'sf_rate': 0.05, 'falsified_rate': 0.01, 'no_prescription_rate': 0.15, 'unregistered_product_rate': 0.08, 'regulatory_compliant': 0.85, 'pharmacist_available': 0.90, 'verified_supplier': 0.88, 'delivery_cold_chain': 0.70, 'consumer_complaint_rate': 0.03, }, 'social_media_marketplace': { 'platform_type': 'social_media', 'sf_rate': 0.35, 'falsified_rate': 0.15, 'no_prescription_rate': 0.92, 'unregistered_product_rate': 0.55, 'regulatory_compliant': 0.02, 'pharmacist_available': 0.05, 'verified_supplier': 0.08, 'delivery_cold_chain': 0.05, 'consumer_complaint_rate': 0.20, }, 'rogue_website_darknet': { 'platform_type': 'rogue_website', 'sf_rate': 0.65, 'falsified_rate': 0.40, 'no_prescription_rate': 0.99, 'unregistered_product_rate': 0.85, 'regulatory_compliant': 0.00, 'pharmacist_available': 0.01, 'verified_supplier': 0.02, 'delivery_cold_chain': 0.01, 'consumer_complaint_rate': 0.45, }, } def generate_dataset(n=10000, seed=42, scenario='social_media_marketplace'): rng = np.random.default_rng(seed) sc = SCENARIOS[scenario] records = [] n_prod = len(PRODUCT_CATEGORIES) for idx in range(n): rec = {'id': idx + 1} rec['platform_type'] = sc['platform_type'] rec['seller_id'] = f"EPHARM_{rng.integers(1, 1000):05d}" rec['platform_name'] = rng.choice( ['WhatsApp', 'Facebook', 'Instagram', 'Telegram', 'TikTok', 'dedicated_website', 'marketplace_app', 'darknet_market'], p=[0.10, 0.15, 0.10, 0.08, 0.05, 0.25, 0.15, 0.12] if scenario == 'social_media_marketplace' else ([0.02, 0.03, 0.02, 0.01, 0.01, 0.80, 0.10, 0.01] if scenario == 'licensed_e_pharmacy' else [0.03, 0.05, 0.03, 0.10, 0.02, 0.30, 0.07, 0.40])) prod = PRODUCT_CATEGORIES[rng.choice(n_prod, p=[p[2] for p in PRODUCT_CATEGORIES])] rec['product_name'] = prod[0] rec['product_category'] = prod[1] rec['prescription_required_legally'] = 1 if prod[1] in ( 'prescription', 'controlled', 'essential', 'NCD', 'specialty') else 0 rec['prescription_verified'] = 0 if rec['prescription_required_legally']: rec['prescription_verified'] = 0 if rng.random() < sc['no_prescription_rate'] else 1 rec['product_registered'] = 0 if rng.random() < sc['unregistered_product_rate'] else 1 rec['seller_licensed'] = 1 if rng.random() < sc['regulatory_compliant'] else 0 rec['pharmacist_consultation'] = 1 if rng.random() < sc['pharmacist_available'] else 0 rec['verified_supply_chain'] = 1 if rng.random() < sc['verified_supplier'] else 0 rec['cold_chain_maintained'] = 1 if rng.random() < sc['delivery_cold_chain'] else 0 rec['manufacturer_origin'] = rng.choice( ['India', 'China', 'Europe', 'local_SSA', 'unknown', 'USA'], p=[0.30, 0.20, 0.08, 0.07, 0.30, 0.05] if scenario != 'licensed_e_pharmacy' else [0.35, 0.10, 0.15, 0.15, 0.10, 0.15]) # SF determination base_sf = sc['sf_rate'] if not rec['product_registered']: base_sf *= 1.4 if not rec['verified_supply_chain']: base_sf *= 1.3 if rec['manufacturer_origin'] == 'unknown': base_sf *= 1.5 if prod[1] == 'controlled': base_sf *= 1.3 if prod[1] == 'lifestyle': base_sf *= 1.2 base_sf = np.clip(base_sf, 0.01, 0.90) is_sf = rng.random() < base_sf rec['quality_test_result'] = 'fail' if is_sf else 'pass' rec['sf_classification'] = 'compliant' if is_sf: fals_prob = sc['falsified_rate'] / max(sc['sf_rate'], 0.001) rec['sf_classification'] = 'falsified' if rng.random() < fals_prob else 'substandard' rec['API_content_adequate'] = 1 if is_sf: rec['API_content_adequate'] = 0 if rng.random() < 0.65 else 1 rec['price_vs_reference'] = round(np.clip( rng.normal(0.40 if scenario == 'rogue_website_darknet' else (0.70 if scenario == 'social_media_marketplace' else 0.90), 0.20), 0.10, 2.0), 2) rec['payment_method'] = rng.choice( ['mobile_money', 'bank_transfer', 'cash_on_delivery', 'cryptocurrency', 'credit_card'], p=[0.35, 0.15, 0.30, 0.05, 0.15] if scenario != 'rogue_website_darknet' else [0.10, 0.10, 0.05, 0.50, 0.25]) rec['consumer_age_group'] = rng.choice( ['18_24', '25_34', '35_49', '50_plus'], p=[0.25, 0.35, 0.25, 0.15]) rec['consumer_education'] = rng.choice( ['primary', 'secondary', 'tertiary'], p=[0.15, 0.40, 0.45] if scenario != 'rogue_website_darknet' else [0.05, 0.30, 0.65]) rec['consumer_complaint_filed'] = 1 if rng.random() < sc['consumer_complaint_rate'] else 0 rec['adverse_event_reported'] = 0 if is_sf: rec['adverse_event_reported'] = 1 if rng.random() < 0.05 else 0 rec['website_takedown'] = 0 if scenario == 'rogue_website_darknet' and rng.random() < 0.03: rec['website_takedown'] = 1 elif scenario == 'social_media_marketplace' and rng.random() < 0.01: rec['website_takedown'] = 1 rec['year'] = rng.choice([2020, 2021, 2022, 2023, 2024], p=[0.08, 0.12, 0.18, 0.28, 0.34]) records.append(rec) df = pd.DataFrame(records) sf_rate = df['quality_test_result'].eq('fail').mean() * 100 print(f"\n{'='*65}") print(f"Online Pharmacy — {scenario} (n={n}, seed={seed})") print(f"{'='*65}") print(f" SF rate: {sf_rate:.1f}% (target ~{sc['sf_rate']*100:.0f}%)") print(f" No prescription: {(1-df['prescription_verified'].mean())*100:.1f}%") print(f" Licensed seller: {df['seller_licensed'].mean()*100:.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'epharmacy_{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', 'epharmacy_social_media_marketplace.csv'), index=False)