"""Generate synthetic medicine quality testing laboratory dataset for SSA. Research-based parameterization: - BMC Health Services (2020): ISO/IEC 17025 accreditation impact in SSA; nonconformities indicate quality compliance levels of NMRAs. - USP PQM (2018): Only small number of LMIC labs meet ISO 17025 or WHO PQ standards; strengthening QC labs critical. - WHO PQ: Prequalification evaluating laboratories must meet protocol and compliance requirements. - SSA context: Limited HPLC equipment; minilab/TLC used for field screening; reagent stockouts; staff shortages; few accredited labs. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd SEED = 42 N_PER_SCENARIO = 10_000 YEAR_RANGE = np.arange(2010, 2025) YEAR_WEIGHTS = np.linspace(0.85, 1.3, len(YEAR_RANGE)) YEAR_WEIGHTS = YEAR_WEIGHTS / YEAR_WEIGHTS.sum() SCENARIOS = { "national_qc_laboratory": { "setting_probs": {"national_qc_lab": 0.40, "university_lab": 0.25, "regional_qc_lab": 0.20, "private_contract_lab": 0.15}, "test_method_probs": {"HPLC": 0.30, "dissolution": 0.20, "disintegration": 0.15, "UV_spectroscopy": 0.10, "TLC": 0.08, "Karl_Fischer": 0.05, "visual_inspection": 0.05, "microbial_limit": 0.07}, "iso_17025_pct": 0.15, "who_pq_pct": 0.05, "staff_qualified_pct": 0.40, "equipment_functional_pct": 0.55, "reagent_available_pct": 0.50, "samples_per_year_mean": 500, "turnaround_days_mean": 30, }, "field_screening_minilab": { "setting_probs": {"district_pharmacy": 0.30, "port_of_entry": 0.25, "market_surveillance": 0.25, "mobile_screening": 0.20}, "test_method_probs": {"minilab_TLC": 0.35, "visual_inspection": 0.25, "colorimetric": 0.15, "GPHF_minilab": 0.10, "NIR_handheld": 0.08, "Raman_handheld": 0.07}, "iso_17025_pct": 0.01, "who_pq_pct": 0.00, "staff_qualified_pct": 0.15, "equipment_functional_pct": 0.65, "reagent_available_pct": 0.40, "samples_per_year_mean": 200, "turnaround_days_mean": 3, }, "referral_confirmatory": { "setting_probs": {"WHO_PQ_lab": 0.20, "international_referral": 0.20, "national_reference_lab": 0.30, "university_research": 0.30}, "test_method_probs": {"HPLC": 0.35, "LC_MS": 0.15, "dissolution": 0.15, "GC": 0.08, "ICP_MS": 0.05, "microbial_limit": 0.07, "stability_testing": 0.08, "bioequivalence": 0.07}, "iso_17025_pct": 0.40, "who_pq_pct": 0.15, "staff_qualified_pct": 0.65, "equipment_functional_pct": 0.75, "reagent_available_pct": 0.70, "samples_per_year_mean": 300, "turnaround_days_mean": 45, }, } SCENARIO_FILES = { "national_qc_laboratory": "qclab_national.csv", "field_screening_minilab": "qclab_field_minilab.csv", "referral_confirmatory": "qclab_referral.csv", } def _choice(rng, prob_map): keys = list(prob_map.keys()) weights = np.array(list(prob_map.values()), dtype=float) weights = weights / weights.sum() return rng.choice(keys, p=weights) def _simulate_scenario(name, params, seed): rng = np.random.default_rng(seed) records = [] for idx in range(N_PER_SCENARIO): year = int(rng.choice(YEAR_RANGE, p=YEAR_WEIGHTS)) setting = _choice(rng, params["setting_probs"]) test_method = _choice(rng, params["test_method_probs"]) medicine_tested = rng.choice(["antibiotic", "antimalarial", "ARV", "analgesic", "antihypertensive", "anti_TB", "vaccine", "antidiabetic", "other"], p=[0.20, 0.15, 0.12, 0.12, 0.10, 0.08, 0.05, 0.08, 0.10]) sample_source = rng.choice(["post_market", "port_entry", "complaint", "routine", "research", "manufacturer"], p=[0.30, 0.15, 0.10, 0.25, 0.10, 0.10]) # Lab accreditation & capacity iso_17025 = int(rng.random() < params["iso_17025_pct"]) who_pq_lab = int(rng.random() < params["who_pq_pct"]) gmp_compliant = int(iso_17025 or (rng.random() < 0.20)) quality_manual = int(rng.random() < 0.40) proficiency_testing = int(iso_17025 and rng.random() < 0.60) # Equipment equipment_functional = int(rng.random() < params["equipment_functional_pct"]) hplc_available = int(test_method == "HPLC" and equipment_functional) calibrated_current = int(equipment_functional and rng.random() < 0.50) maintenance_contract = int(rng.random() < 0.15) # Reagents & consumables reagent_available = int(rng.random() < params["reagent_available_pct"]) reference_standard_available = int(reagent_available and rng.random() < 0.40) reagent_expired = int(not reagent_available and rng.random() < 0.20) # Staffing staff_qualified = int(rng.random() < params["staff_qualified_pct"]) analyst_trained = int(staff_qualified and rng.random() < 0.70) staff_count = int(np.clip(rng.poisson(8), 1, 40)) vacancies = int(np.clip(rng.poisson(2), 0, staff_count)) # Testing performance samples_year = int(np.clip(rng.poisson(params["samples_per_year_mean"]), 10, 5000)) turnaround_days = int(np.clip( rng.normal(params["turnaround_days_mean"], 15), 1, 180)) test_completed = int(equipment_functional and reagent_available and staff_qualified) result_accurate = int(test_completed and calibrated_current and rng.random() < 0.85) # Findings sf_detected = int(test_completed and rng.random() < 0.15) api_failure = int(sf_detected and rng.random() < 0.60) dissolution_failure = int(sf_detected and rng.random() < 0.40) reported_to_nmra = int(sf_detected and rng.random() < 0.50) # Budget budget_adequate = int(rng.random() < 0.20) donor_funded = int(rng.random() < 0.35) fee_for_service = int(rng.random() < 0.25) record = { "record_id": f"{name[:3].upper()}-{idx:05d}", "scenario": name, "year": year, "setting": setting, "test_method": test_method, "medicine_tested": medicine_tested, "sample_source": sample_source, "iso_17025": iso_17025, "who_pq_lab": who_pq_lab, "quality_manual": quality_manual, "proficiency_testing": proficiency_testing, "equipment_functional": equipment_functional, "hplc_available": hplc_available, "calibrated_current": calibrated_current, "maintenance_contract": maintenance_contract, "reagent_available": reagent_available, "reference_standard_available": reference_standard_available, "staff_qualified": staff_qualified, "analyst_trained": analyst_trained, "staff_count": staff_count, "vacancies": vacancies, "samples_year": samples_year, "turnaround_days": turnaround_days, "test_completed": test_completed, "result_accurate": result_accurate, "sf_detected": sf_detected, "api_failure": api_failure, "reported_to_nmra": reported_to_nmra, "budget_adequate": budget_adequate, "donor_funded": donor_funded, } records.append(record) return pd.DataFrame(records) def main(): output_dir = Path("data") output_dir.mkdir(parents=True, exist_ok=True) for idx, (name, params) in enumerate(SCENARIOS.items()): df = _simulate_scenario(name, params, SEED + idx * 211) df.to_csv(output_dir / SCENARIO_FILES[name], index=False) print(f"Saved {name} -> {SCENARIO_FILES[name]}") if __name__ == "__main__": main()