"""Generate synthetic asbestos exposure & mesothelioma dataset for SSA. Research-based parameterization: - WHO Africa: Asbestos use continues despite warnings; used in roofing, insulation, cement pipes, brake linings across SSA. - WHO Fact Sheet: Asbestos causes lung/larynx/ovary cancer, mesothelioma, asbestosis. All forms carcinogenic (IARC Group 1). - South Africa: Global leader in asbestos production; crocidolite/amosite/ chrysotile all mined. Wagner (1960) discovered mesothelioma link. - PMC1522094: Social production of asbestos-related disease in SA; asbestosis, lung cancer, mesothelioma since early 1900s. - PMC12573932 (GBD 2021): Eastern SSA saw substantial increases in lung cancer from occupational asbestos exposure. - SA banned asbestos mining in 2002; many SSA countries still use. - Latency period: 20-50 years from exposure to mesothelioma. - Mesothelioma mortality rates lower than expected in SA due to HIV reducing life expectancy (PubMed 21422006). """ 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 = { # Former mining communities (South Africa type) "former_mining_community": { "setting_probs": {"rural_mining": 0.50, "peri_urban": 0.30, "urban": 0.20}, "exposure_probs": {"mining_direct": 0.30, "mining_environmental": 0.25, "construction": 0.15, "roofing_materials": 0.15, "household_exposure": 0.10, "brake_lining": 0.05}, "fibre_type_probs": {"crocidolite": 0.35, "amosite": 0.25, "chrysotile": 0.30, "mixed": 0.10}, "exposure_intensity_mean": 3.5, # fibres/mL "exposure_years_mean": 15, "mesothelioma_rate": 0.008, "asbestosis_prev": 0.12, "lung_cancer_rate": 0.005, "ban_in_place": 0.70, "medical_surveillance": 0.15, }, # Urban construction/demolition (ongoing use) "urban_construction": { "setting_probs": {"urban": 0.45, "peri_urban": 0.35, "industrial": 0.20}, "exposure_probs": {"construction": 0.30, "roofing_materials": 0.25, "demolition": 0.15, "insulation": 0.10, "household_exposure": 0.10, "brake_lining": 0.10}, "fibre_type_probs": {"chrysotile": 0.55, "amosite": 0.15, "crocidolite": 0.10, "mixed": 0.20}, "exposure_intensity_mean": 1.5, "exposure_years_mean": 10, "mesothelioma_rate": 0.003, "asbestosis_prev": 0.06, "lung_cancer_rate": 0.003, "ban_in_place": 0.30, "medical_surveillance": 0.05, }, # Rural asbestos roofing communities "rural_asbestos_roofing": { "setting_probs": {"rural": 0.55, "peri_urban": 0.30, "urban": 0.15}, "exposure_probs": {"roofing_materials": 0.40, "household_exposure": 0.25, "water_pipes": 0.10, "construction": 0.10, "environmental": 0.10, "brake_lining": 0.05}, "fibre_type_probs": {"chrysotile": 0.60, "mixed": 0.20, "amosite": 0.10, "crocidolite": 0.10}, "exposure_intensity_mean": 0.5, "exposure_years_mean": 20, "mesothelioma_rate": 0.002, "asbestosis_prev": 0.03, "lung_cancer_rate": 0.002, "ban_in_place": 0.15, "medical_surveillance": 0.02, }, } SCENARIO_FILES = { "former_mining_community": "asbestos_mining_community.csv", "urban_construction": "asbestos_urban_construction.csv", "rural_asbestos_roofing": "asbestos_rural_roofing.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(45, 15), 18, 80)) sex = rng.choice(["male", "female"], p=[0.65, 0.35]) exposure_type = _choice(rng, params["exposure_probs"]) fibre_type = _choice(rng, params["fibre_type_probs"]) is_occupational = int(exposure_type in ("mining_direct", "construction", "demolition", "brake_lining")) is_environmental = int(exposure_type in ("mining_environmental", "household_exposure", "roofing_materials", "environmental", "water_pipes")) exposure_years = int(np.clip( rng.normal(params["exposure_years_mean"], 8), 0, 45)) exposure_intensity = float(np.clip( rng.lognormal(np.log(max(params["exposure_intensity_mean"], 0.1)), 0.8), 0.01, 50)) if not is_occupational: exposure_intensity *= 0.2 cumulative_exposure = float(exposure_intensity * exposure_years) latency_years = int(np.clip(rng.normal(30, 10), 10, 50)) time_since_first_exposure = int(np.clip(rng.normal(20, 10), 0, 50)) ppe_use = int(is_occupational and rng.random() < 0.10) if ppe_use: exposure_intensity *= 0.3 # Fibre potency (crocidolite > amosite > chrysotile) potency = {"crocidolite": 2.0, "amosite": 1.5, "chrysotile": 1.0, "mixed": 1.3} risk_mult = cumulative_exposure * potency.get(fibre_type, 1.0) / 20 # Health outcomes # Mesothelioma (latency 20-50 yrs; crocidolite highest risk) mesothelioma = int(time_since_first_exposure >= 15 and rng.random() < np.clip( params["mesothelioma_rate"] * risk_mult, 0, 0.05)) mesothelioma_type = rng.choice(["pleural", "peritoneal"], p=[0.85, 0.15]) if mesothelioma else "none" # Asbestosis (PMC1522094: progressive fibrotic lung disease) asbestosis = int(exposure_years >= 5 and rng.random() < np.clip( params["asbestosis_prev"] * risk_mult, 0, 0.30)) # Lung cancer lung_cancer = int(age >= 40 and rng.random() < np.clip( params["lung_cancer_rate"] * risk_mult, 0, 0.03)) smoking = int(rng.random() < 0.15) if smoking: lung_cancer = int(rng.random() < np.clip( params["lung_cancer_rate"] * risk_mult * 5, 0, 0.10)) # synergy # Pleural plaques (early marker) pleural_plaques = int(exposure_years >= 10 and rng.random() < np.clip( 0.10 * risk_mult, 0, 0.40)) pleural_effusion = int(pleural_plaques and rng.random() < 0.10) # Respiratory symptoms dyspnoea = int(rng.random() < np.clip(0.10 + risk_mult * 0.05, 0, 0.35)) cough_chronic = int(rng.random() < np.clip(0.08 + risk_mult * 0.04, 0, 0.30)) reduced_fvc = int(asbestosis or rng.random() < np.clip(risk_mult * 0.03, 0, 0.15)) any_asbestos_disease = int(mesothelioma or asbestosis or lung_cancer or pleural_plaques) # Compensation & regulation ban_in_place = int(rng.random() < params["ban_in_place"]) medical_surveillance = int(rng.random() < params["medical_surveillance"]) compensation_claimed = int(any_asbestos_disease and rng.random() < 0.05) chest_xray_done = int(rng.random() < 0.10) # HIV co-morbidity (SA context: reduces life expectancy) hiv_positive = int(rng.random() < 0.12) died = int((mesothelioma and rng.random() < 0.85) or (lung_cancer and rng.random() < 0.70)) record = { "record_id": f"{name[:3].upper()}-{idx:05d}", "scenario": name, "year": year, "setting": setting, "age": age, "sex": sex, "exposure_type": exposure_type, "fibre_type": fibre_type, "is_occupational": is_occupational, "is_environmental": is_environmental, "exposure_years": exposure_years, "exposure_intensity_f_mL": round(exposure_intensity, 2), "cumulative_exposure": round(cumulative_exposure, 1), "latency_years": latency_years, "time_since_first_exposure": time_since_first_exposure, "ppe_use": ppe_use, "smoking": smoking, "mesothelioma": mesothelioma, "mesothelioma_type": mesothelioma_type, "asbestosis": asbestosis, "lung_cancer": lung_cancer, "pleural_plaques": pleural_plaques, "pleural_effusion": pleural_effusion, "dyspnoea": dyspnoea, "cough_chronic": cough_chronic, "reduced_fvc": reduced_fvc, "any_asbestos_disease": any_asbestos_disease, "ban_in_place": ban_in_place, "medical_surveillance": medical_surveillance, "compensation_claimed": compensation_claimed, "chest_xray_done": chest_xray_done, "hiv_positive": hiv_positive, "died": died, } 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()