| """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() |
|
|
