"""Generate synthetic One Health climate surveillance 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 = { "zoonotic_hotspot_forest": { "setting_probs": {"rural_forest": 0.45, "peri_urban": 0.30, "urban": 0.25}, "deforestation_rate_mean": 0.03, "temp_mean": 27, "temp_sd": 2, "rainfall_mean": 1600, "rainfall_sd": 350, "wildlife_contact_pct": 0.35, "livestock_density_mean": 120, "zoonotic_spillover_rate": 0.08, "rvf_risk": 0.05, "ebola_risk": 0.02, "lassa_risk": 0.04, "rabies_rate_per100k": 3.5, "amr_livestock_pct": 0.30, "surveillance_coverage_pct": 0.20, "lab_capacity_score_mean": 0.25, "one_health_platform_pct": 0.15, }, "pastoral_livestock_interface": { "setting_probs": {"rural": 0.60, "peri_urban": 0.25, "urban": 0.15}, "deforestation_rate_mean": 0.01, "temp_mean": 32, "temp_sd": 3, "rainfall_mean": 600, "rainfall_sd": 200, "wildlife_contact_pct": 0.25, "livestock_density_mean": 250, "zoonotic_spillover_rate": 0.06, "rvf_risk": 0.10, "ebola_risk": 0.005, "lassa_risk": 0.01, "rabies_rate_per100k": 5.0, "amr_livestock_pct": 0.35, "surveillance_coverage_pct": 0.15, "lab_capacity_score_mean": 0.20, "one_health_platform_pct": 0.10, }, "urban_periurban_market": { "setting_probs": {"urban": 0.50, "peri_urban": 0.35, "rural": 0.15}, "deforestation_rate_mean": 0.005, "temp_mean": 29, "temp_sd": 2.5, "rainfall_mean": 1100, "rainfall_sd": 300, "wildlife_contact_pct": 0.15, "livestock_density_mean": 80, "zoonotic_spillover_rate": 0.04, "rvf_risk": 0.03, "ebola_risk": 0.01, "lassa_risk": 0.02, "rabies_rate_per100k": 2.0, "amr_livestock_pct": 0.25, "surveillance_coverage_pct": 0.35, "lab_capacity_score_mean": 0.45, "one_health_platform_pct": 0.25, }, } SCENARIO_FILES = { "zoonotic_hotspot_forest": "onehealth_zoonotic_forest.csv", "pastoral_livestock_interface": "onehealth_pastoral_livestock.csv", "urban_periurban_market": "onehealth_urban_market.csv", } ANIMAL_RESERVOIRS = {"rodent": 0.25, "bat": 0.20, "primate": 0.15, "livestock": 0.25, "poultry": 0.10, "dog": 0.05} 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)) month = int(rng.choice(range(1, 13))) setting = _choice(rng, params["setting_probs"]) temp = float(np.clip(rng.normal(params["temp_mean"], params["temp_sd"]), 15, 42)) rainfall = float(np.clip(rng.normal(params["rainfall_mean"], params["rainfall_sd"]), 50, 3000)) deforestation_rate = float(np.clip( rng.normal(params["deforestation_rate_mean"], 0.01) + 0.001 * (year - 2010), 0, 0.10, )) land_use_change = rng.choice( ["forest_intact", "deforested", "agricultural_expansion", "urban_expansion"], p=[0.30, 0.20, 0.30, 0.20], ) wildlife_contact = int(rng.random() < params["wildlife_contact_pct"]) bushmeat_consumption = int(wildlife_contact and rng.random() < 0.40) livestock_density = float(np.clip(rng.normal(params["livestock_density_mean"], 50), 10, 500)) animal_reservoir = _choice(rng, ANIMAL_RESERVOIRS) climate_anomaly = float(np.clip(rng.normal(0, 1), -3, 3)) enso_phase = rng.choice(["neutral", "el_nino", "la_nina"], p=[0.50, 0.25, 0.25]) flood_event = int(rainfall > params["rainfall_mean"] + params["rainfall_sd"] * 1.5) risk_mult = ( 1.0 + deforestation_rate * 5 + wildlife_contact * 0.3 + flood_event * 0.25 + (enso_phase == "el_nino") * 0.15 + (temp > 35) * 0.1 ) zoonotic_event = int(rng.random() < np.clip(params["zoonotic_spillover_rate"] * risk_mult, 0, 0.5)) rvf_case = int(rng.random() < params["rvf_risk"] * (1 + flood_event * 1.5)) ebola_signal = int(rng.random() < params["ebola_risk"] * (1 + deforestation_rate * 10)) lassa_case = int(rng.random() < params["lassa_risk"] * risk_mult) rabies_case = int(rng.random() < params["rabies_rate_per100k"] / 100_000 * 50) any_zoonotic = int(zoonotic_event or rvf_case or ebola_signal or lassa_case or rabies_case) amr_detected = int(rng.random() < params["amr_livestock_pct"]) antibiotic_livestock_use = int(rng.random() < 0.45) surveillance_active = int(rng.random() < params["surveillance_coverage_pct"]) event_detected = int(any_zoonotic and surveillance_active and rng.random() < 0.60) detection_delay_days = int(np.clip(rng.exponential(14 if not surveillance_active else 5), 1, 90)) lab_confirmed = int(event_detected and rng.random() < params["lab_capacity_score_mean"] * 1.5) one_health_platform = int(rng.random() < params["one_health_platform_pct"]) joint_investigation = int(event_detected and one_health_platform and rng.random() < 0.40) response_initiated = int(event_detected and rng.random() < 0.50) human_cases = int(np.clip(rng.poisson(2) if any_zoonotic else 0, 0, 50)) animal_cases = int(np.clip(rng.poisson(5) if any_zoonotic else 0, 0, 100)) cfr = float(np.clip(rng.normal(0.10, 0.05) if any_zoonotic else 0, 0, 0.5)) human_deaths = int(human_cases * cfr) record = { "record_id": f"{name[:3].upper()}-{idx:05d}", "scenario": name, "year": year, "month": month, "setting": setting, "temp_c": round(temp, 1), "rainfall_mm": round(rainfall, 0), "deforestation_rate": round(deforestation_rate, 3), "land_use_change": land_use_change, "climate_anomaly": round(climate_anomaly, 1), "enso_phase": enso_phase, "flood_event": flood_event, "wildlife_contact": wildlife_contact, "bushmeat_consumption": bushmeat_consumption, "livestock_density_per_km2": round(livestock_density, 0), "animal_reservoir": animal_reservoir, "zoonotic_spillover_event": zoonotic_event, "rvf_case": rvf_case, "ebola_signal": ebola_signal, "lassa_case": lassa_case, "rabies_case": rabies_case, "any_zoonotic_event": any_zoonotic, "amr_detected_livestock": amr_detected, "antibiotic_livestock_use": antibiotic_livestock_use, "surveillance_active": surveillance_active, "event_detected": event_detected, "detection_delay_days": detection_delay_days, "lab_confirmed": lab_confirmed, "one_health_platform": one_health_platform, "joint_investigation": joint_investigation, "response_initiated": response_initiated, "human_cases": human_cases, "animal_cases": animal_cases, "human_deaths": human_deaths, } 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()