"""Generate synthetic wildfire smoke & respiratory outcomes dataset for SSA.""" 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 = { "savanna_fire_belt": { "fire_season_months": [11, 12, 1, 2, 3], "pm25_smoke_mean": 180, "pm25_smoke_sd": 90, "pm25_baseline": 25, "fire_frequency_mean": 3.5, "fire_proximity_km_mean": 12, "fire_proximity_km_sd": 8, "smoke_days_mean": 45, "smoke_days_sd": 18, "asthma_prev": 0.08, "copd_prev": 0.04, "ari_rate_per1k": 85, "er_visit_rate": 0.12, "mortality_rate_per100k": 4.5, "setting_probs": {"rural": 0.60, "peri_urban": 0.25, "urban": 0.15}, "mask_access": 0.05, }, "forest_clearing_burn": { "fire_season_months": [6, 7, 8, 9], "pm25_smoke_mean": 250, "pm25_smoke_sd": 120, "pm25_baseline": 20, "fire_frequency_mean": 2.0, "fire_proximity_km_mean": 8, "fire_proximity_km_sd": 5, "smoke_days_mean": 60, "smoke_days_sd": 25, "asthma_prev": 0.07, "copd_prev": 0.03, "ari_rate_per1k": 95, "er_visit_rate": 0.10, "mortality_rate_per100k": 5.0, "setting_probs": {"rural": 0.70, "peri_urban": 0.20, "urban": 0.10}, "mask_access": 0.03, }, "urban_peri_urban_haze": { "fire_season_months": [12, 1, 2, 3], "pm25_smoke_mean": 120, "pm25_smoke_sd": 55, "pm25_baseline": 40, "fire_frequency_mean": 1.5, "fire_proximity_km_mean": 25, "fire_proximity_km_sd": 15, "smoke_days_mean": 30, "smoke_days_sd": 12, "asthma_prev": 0.10, "copd_prev": 0.05, "ari_rate_per1k": 70, "er_visit_rate": 0.18, "mortality_rate_per100k": 3.5, "setting_probs": {"urban": 0.50, "peri_urban": 0.35, "rural": 0.15}, "mask_access": 0.15, }, } SCENARIO_FILES = { "savanna_fire_belt": "wildfire_savanna_fire_belt.csv", "forest_clearing_burn": "wildfire_forest_clearing_burn.csv", "urban_peri_urban_haze": "wildfire_urban_peri_urban_haze.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)) age = int(np.clip(rng.normal(32, 18), 1, 85)) sex = rng.choice(["male", "female"], p=[0.48, 0.52]) setting = _choice(rng, params["setting_probs"]) is_child = int(age < 5) is_elderly = int(age >= 60) pre_existing_asthma = int(rng.random() < params["asthma_prev"]) pre_existing_copd = int(age > 35 and rng.random() < params["copd_prev"]) smoker = int(age > 15 and rng.random() < 0.12) month = int(rng.choice(range(1, 13))) in_fire_season = int(month in params["fire_season_months"]) if in_fire_season: pm25_smoke = float(np.clip(rng.normal(params["pm25_smoke_mean"], params["pm25_smoke_sd"]), 20, 800)) else: pm25_smoke = float(np.clip(rng.normal(params["pm25_baseline"], 10), 5, 60)) pm25_total = float(pm25_smoke + rng.normal(params["pm25_baseline"], 8)) pm25_total = float(np.clip(pm25_total, 10, 900)) fire_proximity_km = float(np.clip( rng.normal(params["fire_proximity_km_mean"], params["fire_proximity_km_sd"]), 0.5, 100 )) smoke_days = int(np.clip( rng.normal(params["smoke_days_mean"], params["smoke_days_sd"]) * (1.2 if in_fire_season else 0.3), 0, 120, )) fire_count = int(np.clip(rng.poisson(params["fire_frequency_mean"]), 0, 15)) aqi_category = ( "hazardous" if pm25_total > 250 else "very_unhealthy" if pm25_total > 150 else "unhealthy" if pm25_total > 55 else "moderate" if pm25_total > 35 else "good" ) mask_use = int(rng.random() < params["mask_access"] * (1.5 if in_fire_season else 0.5)) stayed_indoors = int(in_fire_season and rng.random() < 0.20) vulnerability = ( 0.15 + is_child * 0.25 + is_elderly * 0.20 + pre_existing_asthma * 0.15 + pre_existing_copd * 0.15 + smoker * 0.10 ) vulnerability = float(np.clip(vulnerability, 0, 1)) exposure_risk = float(np.clip( (pm25_total / 300) * (1 - mask_use * 0.3) * (1 - stayed_indoors * 0.4) * vulnerability * 2, 0, 1, )) cough = int(rng.random() < 0.15 + exposure_risk * 0.35) wheeze = int(rng.random() < 0.08 + exposure_risk * 0.25) dyspnoea = int(rng.random() < 0.05 + exposure_risk * 0.20) eye_irritation = int(in_fire_season and rng.random() < 0.20 + exposure_risk * 0.25) ari_episode = int(rng.random() < params["ari_rate_per1k"] / 1000 * (1 + exposure_risk * 2)) asthma_exacerbation = int(pre_existing_asthma and rng.random() < 0.15 + exposure_risk * 0.3) copd_exacerbation = int(pre_existing_copd and rng.random() < 0.10 + exposure_risk * 0.25) er_visit = int( (ari_episode or asthma_exacerbation or copd_exacerbation) and rng.random() < params["er_visit_rate"] ) hospitalised = int(er_visit and rng.random() < 0.25) mortality = int( hospitalised and rng.random() < params["mortality_rate_per100k"] / 100_000 * 500 ) climate_fire_trend = float(np.clip(0.015 * (year - 2010) + rng.normal(0, 0.005), -0.02, 0.1)) record = { "record_id": f"{name[:3].upper()}-{idx:05d}", "scenario": name, "year": year, "month": month, "in_fire_season": in_fire_season, "age": age, "sex": sex, "setting": setting, "is_child_u5": is_child, "is_elderly_60plus": is_elderly, "pre_existing_asthma": pre_existing_asthma, "pre_existing_copd": pre_existing_copd, "smoker": smoker, "pm25_smoke_ugm3": round(pm25_smoke, 1), "pm25_total_ugm3": round(pm25_total, 1), "aqi_category": aqi_category, "fire_proximity_km": round(fire_proximity_km, 1), "smoke_days_per_season": smoke_days, "fire_count": fire_count, "mask_use": mask_use, "stayed_indoors": stayed_indoors, "vulnerability_index": round(vulnerability, 2), "exposure_risk_score": round(exposure_risk, 3), "cough": cough, "wheeze": wheeze, "dyspnoea": dyspnoea, "eye_irritation": eye_irritation, "ari_episode": ari_episode, "asthma_exacerbation": asthma_exacerbation, "copd_exacerbation": copd_exacerbation, "er_visit": er_visit, "hospitalised": hospitalised, "mortality": mortality, "climate_fire_trend": round(climate_fire_trend, 4), } 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()