#!/usr/bin/env python3 """ Literature-Informed Medicine Quality Control Laboratories Dataset ================================================================== Each record = ONE QC laboratory assessment/performance record. Sources (v2.0): [1] WHO (2023). Only 30% of African countries have a functional national QC lab. Many labs lack ISO 17025 accreditation. [2] USP PQM+. Supported 50+ labs globally. Equipment maintenance and reagent supply are critical bottlenecks. [3] WHO Prequalification of QC Labs. Only ~10 WHO-prequalified QC labs in Africa (out of ~60 globally). [4] African Medicines Quality Forum. Lab networking for proficiency testing and harmonized methods. [5] Lancet Commission (2017). QC lab capacity directly impacts SF medicine detection and regulatory enforcement. """ import numpy as np import pandas as pd import argparse import os EQUIPMENT_TYPES = [ 'HPLC', 'UV_Vis_spectrophotometer', 'dissolution_apparatus', 'disintegration_tester', 'Karl_Fischer_titrator', 'IR_spectrophotometer', 'mass_spectrometer', 'GC', 'TLC_equipment', 'pH_meter', 'analytical_balance', 'stability_chamber', ] TEST_TYPES = [ 'identity', 'assay_API_content', 'dissolution', 'disintegration', 'uniformity_content', 'uniformity_mass', 'moisture_content', 'impurity_related_substances', 'microbial_limit', 'sterility', 'endotoxin', 'particle_size', ] SCENARIOS = { 'who_prequalified_lab': { 'lab_tier': 'WHO_prequalified', 'ISO_17025': True, 'staff_analysts': 20, 'equipment_functional_rate': 0.92, 'samples_per_year': 5000, 'turnaround_days': 10, 'proficiency_testing_pass': 0.95, 'reagent_stockout_rate': 0.05, 'budget_usd': 800000, 'accreditation_scope_tests': 10, }, 'national_reference_lab': { 'lab_tier': 'national_reference', 'ISO_17025': False, 'staff_analysts': 8, 'equipment_functional_rate': 0.65, 'samples_per_year': 1500, 'turnaround_days': 25, 'proficiency_testing_pass': 0.70, 'reagent_stockout_rate': 0.25, 'budget_usd': 200000, 'accreditation_scope_tests': 5, }, 'district_basic_lab': { 'lab_tier': 'district_basic', 'ISO_17025': False, 'staff_analysts': 2, 'equipment_functional_rate': 0.30, 'samples_per_year': 200, 'turnaround_days': 45, 'proficiency_testing_pass': 0.35, 'reagent_stockout_rate': 0.55, 'budget_usd': 30000, 'accreditation_scope_tests': 2, }, } def generate_dataset(n=10000, seed=42, scenario='national_reference_lab'): rng = np.random.default_rng(seed) sc = SCENARIOS[scenario] records = [] for idx in range(n): rec = {'id': idx + 1} rec['lab_tier'] = sc['lab_tier'] rec['lab_id'] = f"QCL_{rng.integers(1, 100):03d}" rec['country_id'] = f"C_{rng.integers(1, 55):03d}" rec['region_type'] = rng.choice(['urban', 'peri_urban', 'rural'], p=[0.70, 0.20, 0.10] if scenario == 'who_prequalified_lab' else ([0.50, 0.30, 0.20] if scenario == 'national_reference_lab' else [0.20, 0.30, 0.50])) rec['ISO_17025_accredited'] = 1 if sc['ISO_17025'] else ( 1 if rng.random() < 0.05 else 0) rec['WHO_prequalified_lab'] = 1 if scenario == 'who_prequalified_lab' else 0 rec['staff_analysts'] = max(1, int(rng.poisson(sc['staff_analysts']))) rec['staff_with_MSc_PhD'] = max(0, int(rec['staff_analysts'] * np.clip( rng.normal(0.50 if scenario == 'who_prequalified_lab' else (0.25 if scenario == 'national_reference_lab' else 0.05), 0.10), 0, 0.80))) rec['continuous_training_hours_per_year'] = max(0, int(rng.normal( 80 if scenario == 'who_prequalified_lab' else (30 if scenario == 'national_reference_lab' else 5), 15))) rec['annual_budget_usd'] = max(5000, int(rng.lognormal( np.log(sc['budget_usd']), 0.3))) rec['budget_from_government_pct'] = round(np.clip( rng.normal(60 if scenario == 'who_prequalified_lab' else (40 if scenario == 'national_reference_lab' else 20), 15), 5, 95), 1) rec['budget_from_donors_pct'] = round(100 - rec['budget_from_government_pct'] - np.clip(rng.normal(10, 5), 0, 30), 1) # Equipment rec['total_equipment_items'] = max(3, int(rng.poisson( len(EQUIPMENT_TYPES) if scenario == 'who_prequalified_lab' else (8 if scenario == 'national_reference_lab' else 4)))) rec['equipment_functional_pct'] = round(np.clip( rng.normal(sc['equipment_functional_rate'] * 100, 8), 10, 100), 1) rec['HPLC_available'] = 1 if scenario != 'district_basic_lab' or rng.random() < 0.10 else 0 rec['dissolution_available'] = 1 if scenario == 'who_prequalified_lab' or ( scenario == 'national_reference_lab' and rng.random() < 0.60) else 0 rec['mass_spec_available'] = 1 if scenario == 'who_prequalified_lab' and rng.random() < 0.70 else 0 rec['reagent_stockout_days_per_year'] = max(0, int(rng.exponential( sc['reagent_stockout_rate'] * 365 * 0.3))) rec['reference_standards_available'] = 1 if rng.random() < ( 0.95 if scenario == 'who_prequalified_lab' else (0.55 if scenario == 'national_reference_lab' else 0.15)) else 0 # Performance rec['samples_tested_per_year'] = max(10, int(rng.poisson(sc['samples_per_year']))) rec['turnaround_days_median'] = max(1, int(rng.exponential(sc['turnaround_days'] * 0.6))) rec['test_types_offered'] = min(len(TEST_TYPES), max(1, int(rng.poisson( sc['accreditation_scope_tests'])))) rec['proficiency_testing_enrolled'] = 1 if rng.random() < ( 0.98 if scenario == 'who_prequalified_lab' else (0.50 if scenario == 'national_reference_lab' else 0.08)) else 0 rec['proficiency_testing_pass_rate'] = round(np.clip( rng.normal(sc['proficiency_testing_pass'] * 100, 8), 0, 100), 1) if rec['proficiency_testing_enrolled'] else 0 rec['internal_audit_conducted'] = 1 if rng.random() < ( 0.95 if scenario == 'who_prequalified_lab' else (0.35 if scenario == 'national_reference_lab' else 0.05)) else 0 rec['CAPA_system_functional'] = 1 if rng.random() < ( 0.90 if scenario == 'who_prequalified_lab' else (0.25 if scenario == 'national_reference_lab' else 0.03)) else 0 rec['sf_detection_rate_pct'] = round(np.clip( rng.normal(18 if scenario == 'who_prequalified_lab' else (22 if scenario == 'national_reference_lab' else 35), 5), 2, 60), 1) rec['confirmatory_testing_capacity'] = 1 if scenario == 'who_prequalified_lab' or ( scenario == 'national_reference_lab' and rng.random() < 0.40) else 0 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"QC Labs — {scenario} (n={n}, seed={seed})") print(f"{'='*65}") print(f" Lab tier: {sc['lab_tier']}") print(f" Avg staff: {df['staff_analysts'].mean():.0f}") print(f" Equipment functional: {df['equipment_functional_pct'].mean():.0f}%") print(f" Samples/year: {df['samples_tested_per_year'].mean():.0f}") print(f" Proficiency pass: {df[df['proficiency_testing_enrolled']==1]['proficiency_testing_pass_rate'].mean():.0f}%") 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'qc_lab_{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', 'qc_lab_national_reference_lab.csv'), index=False)