"""Generate synthetic herbal & traditional medicine safety dataset for SSA. Research-based parameterization: - Frontiers Pharmacology (2020): Heavy metal contamination in herbal medicines; Pb, Cd, Hg, As detected across multiple countries. - IntechOpen: Adulteration with synthetic drugs, pesticides, microbes, heavy metals; hepatotoxicity, nephrotoxicity major concerns. - BMC Complement Med (2023): Microbial and heavy metal contamination; safety concerns linked to global herbal trade increase. - PubMed 22843016: Contamination/adulteration causes agranulocytosis, meningitis, multi-organ failure, death. - WHO: ~80% of African population uses traditional medicine; regulation inadequate in most SSA countries. """ 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 = { "traditional_healer_practice": { "setting_probs": {"rural_healer": 0.40, "urban_healer": 0.25, "traditional_market": 0.20, "community": 0.15}, "product_probs": {"herbal_decoction": 0.30, "herbal_powder": 0.20, "plant_extract": 0.15, "mixed_herbal": 0.15, "animal_product": 0.10, "mineral_preparation": 0.10}, "heavy_metal_contamination_pct": 0.30, "microbial_contamination_pct": 0.35, "adulteration_pct": 0.15, "hepatotoxicity_pct": 0.08, "regulated_pct": 0.05, "concurrent_conventional_pct": 0.25, }, "herbal_retail_market": { "setting_probs": {"herbal_shop": 0.35, "pharmacy_herbal_section": 0.20, "open_market": 0.25, "online_seller": 0.20}, "product_probs": {"packaged_herbal": 0.30, "herbal_supplement": 0.20, "herbal_tea": 0.15, "herbal_capsule": 0.15, "topical_herbal": 0.10, "imported_herbal": 0.10}, "heavy_metal_contamination_pct": 0.20, "microbial_contamination_pct": 0.25, "adulteration_pct": 0.20, "hepatotoxicity_pct": 0.05, "regulated_pct": 0.15, "concurrent_conventional_pct": 0.35, }, "hospital_herb_drug_interaction": { "setting_probs": {"hospital": 0.35, "primary_care": 0.25, "HIV_clinic": 0.20, "oncology_clinic": 0.20}, "product_probs": {"herbal_decoction": 0.20, "herbal_supplement": 0.20, "herbal_capsule": 0.15, "mixed_herbal": 0.15, "african_potato": 0.10, "sutherlandia": 0.08, "other_traditional": 0.12}, "heavy_metal_contamination_pct": 0.15, "microbial_contamination_pct": 0.15, "adulteration_pct": 0.10, "hepatotoxicity_pct": 0.10, "regulated_pct": 0.10, "concurrent_conventional_pct": 0.70, }, } SCENARIO_FILES = { "traditional_healer_practice": "herbal_traditional_healer.csv", "herbal_retail_market": "herbal_retail_market.csv", "hospital_herb_drug_interaction": "herbal_hospital_interaction.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"]) age = int(np.clip(rng.normal(38, 16), 1, 80)) sex = rng.choice(["male", "female"], p=[0.40, 0.60]) product_type = _choice(rng, params["product_probs"]) indication = rng.choice(["malaria_fever", "stomach_GI", "sexual_enhancement", "HIV_immune_boost", "diabetes", "hypertension", "pain_inflammation", "fertility", "skin_disease", "other"], p=[0.15, 0.12, 0.10, 0.10, 0.08, 0.08, 0.12, 0.08, 0.07, 0.10]) # Contamination heavy_metal = int(rng.random() < params["heavy_metal_contamination_pct"]) lead_detected = int(heavy_metal and rng.random() < 0.50) mercury_detected = int(heavy_metal and rng.random() < 0.25) arsenic_detected = int(heavy_metal and rng.random() < 0.20) microbial = int(rng.random() < params["microbial_contamination_pct"]) aflatoxin = int(microbial and rng.random() < 0.15) ecoli = int(microbial and rng.random() < 0.30) salmonella = int(microbial and rng.random() < 0.10) # Adulteration adulterated = int(rng.random() < params["adulteration_pct"]) synthetic_drug_added = int(adulterated and rng.random() < 0.40) steroid_added = int(adulterated and rng.random() < 0.20) sildenafil_added = int(adulterated and indication == "sexual_enhancement" and rng.random() < 0.30) pesticide_residue = int(rng.random() < 0.10) # Health effects any_contamination = int(heavy_metal or microbial or adulterated) hepatotoxicity = int(rng.random() < params["hepatotoxicity_pct"]) nephrotoxicity = int(heavy_metal and rng.random() < 0.05) gi_adverse = int(rng.random() < 0.08) skin_reaction = int(rng.random() < 0.04) hospitalisation = int((hepatotoxicity or nephrotoxicity) and rng.random() < 0.30) death = int(hospitalisation and rng.random() < 0.05) # Herb-drug interactions concurrent_conventional = int(rng.random() < params["concurrent_conventional_pct"]) disclosed_to_doctor = int(concurrent_conventional and rng.random() < 0.20) interaction_risk = int(concurrent_conventional and rng.random() < 0.15) interaction_type = rng.choice(["CYP_inhibition", "CYP_induction", "pharmacodynamic", "absorption", "unknown"], p=[0.30, 0.20, 0.25, 0.15, 0.10]) if interaction_risk else "none" # Regulation & quality registered_product = int(rng.random() < params["regulated_pct"]) label_present = int(rng.random() < 0.30) dosage_specified = int(label_present and rng.random() < 0.40) quality_tested = int(rng.random() < 0.02) traditional_healer_registered = int(rng.random() < 0.10) any_adverse = int(hepatotoxicity or nephrotoxicity or gi_adverse or interaction_risk) record = { "record_id": f"{name[:3].upper()}-{idx:05d}", "scenario": name, "year": year, "setting": setting, "age": age, "sex": sex, "product_type": product_type, "indication": indication, "heavy_metal_contaminated": heavy_metal, "lead_detected": lead_detected, "mercury_detected": mercury_detected, "microbial_contaminated": microbial, "aflatoxin": aflatoxin, "adulterated": adulterated, "synthetic_drug_added": synthetic_drug_added, "pesticide_residue": pesticide_residue, "hepatotoxicity": hepatotoxicity, "nephrotoxicity": nephrotoxicity, "gi_adverse": gi_adverse, "hospitalisation": hospitalisation, "death": death, "concurrent_conventional": concurrent_conventional, "disclosed_to_doctor": disclosed_to_doctor, "interaction_risk": interaction_risk, "interaction_type": interaction_type, "registered_product": registered_product, "label_present": label_present, "quality_tested": quality_tested, "any_adverse": any_adverse, } 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()