"""Generate synthetic radon & indoor radiation exposure dataset for SSA. Research-based parameterization: - WHO Fact Sheet: Radon causes 3-14% of lung cancers; reference level 100 Bq/m³, action level 300 Bq/m³. - PMC12277776: Indoor radon exposure in Africa critical review; growing concern; classified IARC Group 1 carcinogen. - PMC12081354: Radon = ~50% of human radiation exposure; originates from granite, brick, sand, cement, gypsum. - PubMed 40334468: Nigerian buildings (homes, schools, workplaces) monitored; significant public health concern. - PMC12331818: SA community near granite geology; alpha particles from radon daughters damage lung cells. - Second leading cause of lung cancer globally after smoking. """ 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 = { "granite_geology_rural": { "setting_probs": {"rural_granite": 0.50, "peri_urban": 0.30, "mining_town": 0.20}, "building_probs": {"mud_brick": 0.30, "concrete_block": 0.25, "stone_granite": 0.25, "corrugated_iron": 0.15, "modern": 0.05}, "radon_gm": 120, "radon_gsd": 2.2, "ventilation_poor_pct": 0.55, "smoking_prev": 0.15, "lung_cancer_base": 0.003, "measurement_pct": 0.02, }, "urban_residential": { "setting_probs": {"urban_formal": 0.40, "urban_informal": 0.35, "peri_urban": 0.25}, "building_probs": {"concrete_block": 0.40, "brick": 0.25, "corrugated_iron": 0.15, "modern": 0.15, "mud_brick": 0.05}, "radon_gm": 60, "radon_gsd": 2.0, "ventilation_poor_pct": 0.35, "smoking_prev": 0.20, "lung_cancer_base": 0.002, "measurement_pct": 0.05, }, "occupational_underground": { "setting_probs": {"underground_mine": 0.45, "tunnel_cave": 0.20, "basement_building": 0.20, "industrial": 0.15}, "building_probs": {"underground": 0.45, "concrete_block": 0.25, "stone_granite": 0.15, "modern": 0.10, "other": 0.05}, "radon_gm": 250, "radon_gsd": 2.5, "ventilation_poor_pct": 0.45, "smoking_prev": 0.20, "lung_cancer_base": 0.005, "measurement_pct": 0.08, }, } SCENARIO_FILES = { "granite_geology_rural": "radon_granite_rural.csv", "urban_residential": "radon_urban_residential.csv", "occupational_underground": "radon_occupational.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(40, 15), 18, 80)) sex = rng.choice(["male", "female"], p=[0.50, 0.50]) building_type = _choice(rng, params["building_probs"]) # Ventilation (poor ventilation increases radon accumulation) ventilation_poor = int(rng.random() < params["ventilation_poor_pct"]) floor_level = rng.choice(["ground_basement", "first", "upper"], p=[0.60, 0.25, 0.15]) # Radon concentration (Bq/m³) vent_mult = 1.5 if ventilation_poor else 1.0 floor_mult = 1.3 if floor_level == "ground_basement" else 0.8 if floor_level == "upper" else 1.0 geology_mult = 1.3 if building_type in ("stone_granite", "underground") else 1.0 radon_bqm3 = float(np.clip( rng.lognormal(np.log(params["radon_gm"] * vent_mult * floor_mult * geology_mult), np.log(params["radon_gsd"])), 5, 3000)) above_who_100 = int(radon_bqm3 > 100) above_action_300 = int(radon_bqm3 > 300) # Exposure hours_indoors_day = float(np.clip(rng.normal(16, 3), 8, 23)) exposure_years = int(np.clip(rng.normal(15, 8), 1, 50)) occupancy_factor = hours_indoors_day / 24 cumulative_exposure = float(radon_bqm3 * occupancy_factor * exposure_years) smoking = int(rng.random() < params["smoking_prev"]) # Smoking + radon synergy: multiplicative risk (WHO) risk_mult = (cumulative_exposure / 500) * (5.0 if smoking else 1.0) # Lung cancer (WHO: radon = second leading cause) lung_cancer = int(age >= 40 and rng.random() < np.clip( params["lung_cancer_base"] * risk_mult, 0, 0.05)) # Respiratory symptoms chronic_cough = int(rng.random() < np.clip(0.05 + risk_mult * 0.02, 0, 0.20)) dyspnoea = int(rng.random() < np.clip(0.04 + risk_mult * 0.01, 0, 0.15)) # Measurement & mitigation radon_measured = int(rng.random() < params["measurement_pct"]) aware_of_radon = int(rng.random() < 0.05) mitigation_installed = int(above_action_300 and radon_measured and rng.random() < 0.10) ventilation_improved = int(radon_measured and rng.random() < 0.15) sub_slab_depressurization = int(mitigation_installed and rng.random() < 0.20) # Building characteristics sealed_floor = int(rng.random() < 0.30) cracks_in_floor = int(rng.random() < 0.40) uranium_geology = int(setting in ("underground_mine", "rural_granite", "mining_town") and rng.random() < 0.30) any_health_effect = int(lung_cancer or chronic_cough or dyspnoea) record = { "record_id": f"{name[:3].upper()}-{idx:05d}", "scenario": name, "year": year, "setting": setting, "age": age, "sex": sex, "building_type": building_type, "floor_level": floor_level, "ventilation_poor": ventilation_poor, "radon_bqm3": round(radon_bqm3, 1), "above_who_100": above_who_100, "above_action_300": above_action_300, "hours_indoors_day": round(hours_indoors_day, 1), "exposure_years": exposure_years, "cumulative_exposure": round(cumulative_exposure, 0), "smoking": smoking, "lung_cancer": lung_cancer, "chronic_cough": chronic_cough, "dyspnoea": dyspnoea, "radon_measured": radon_measured, "aware_of_radon": aware_of_radon, "mitigation_installed": mitigation_installed, "ventilation_improved": ventilation_improved, "sealed_floor": sealed_floor, "cracks_in_floor": cracks_in_floor, "uranium_geology": uranium_geology, "any_health_effect": any_health_effect, } 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()