#!/usr/bin/env python3 """ Literature-Informed Synthetic Snakebite Envenomation Dataset Generator ====================================================================== Generates realistic synthetic records of snakebite envenomation cases presenting to health facilities in sub-Saharan Africa, including snake species, envenomation syndrome, severity grading, clinical features, antivenom treatment, complications, and outcomes. Target population: Snakebite victims aged 1-80 years presenting to health facilities across SSA. DAG (Sampling Order): 1. age, sex, occupation, location (roots) 2. snake_species (conditional on region/habitat) 3. envenomation_syndrome: cytotoxic / hemotoxic / neurotoxic / dry bite 4. severity: mild / moderate / severe 5. clinical_features: swelling, coagulopathy, neurotoxicity, necrosis 6. treatment: antivenom, wound care, fasciotomy 7. complications: compartment syndrome, AKI, necrosis, amputation 8. outcome: survived / died / disability References: ----------- [1] Chippaux JP (Toxicon 2011). SSA: ~315,000 envenomations/yr, ~32,000 deaths, ~7,000 amputations. Case fatality 6-10%. [2] WHO (2019). Snakebite envenoming strategy. NTD category A since 2017. Target: halve deaths/disability by 2030. [3] Habib AG, et al. (PLoS NTD 2015). Echis ocellatus envenoming in Nigeria: coagulopathy in 70-80%, local swelling 90%+. EchiTab antivenom efficacy. [4] Warrell DA (Lancet 2010). Snake bite. Clinical features by species: Bitis arietans: massive local swelling, necrosis, coagulopathy. Naja: necrosis, neurotoxicity. Dendroaspis: rapid neurotoxicity. Echis: coagulopathy, haemorrhage, renal failure. [5] Harrison RA, et al. (PLoS NTD 2009). Snakebite: disease of poverty. Agricultural workers, children at highest risk. [6] Kasturiratne A, et al. (PLoS Med 2008). Global burden: 1.2-5.5M bites/yr, 421,000-1.8M envenomations, 20,000-94,000 deaths. [7] Gutierrez JM, et al. (Nat Rev Dis Primers 2017). Snakebite envenoming comprehensive review. Antivenom sole specific treatment. [8] Schioldann E, et al. (PLoS NTD 2018). Antivenom availability in SSA: <2% of need met. Mean time to treatment 6-24 hours. """ import numpy as np import pandas as pd import argparse import os # ============================================================ # SECTION 1: Literature-Informed Parameters # ============================================================ SCENARIOS = { 'referral_hospital': { 'description': 'Tertiary/referral hospital with antivenom stock, ' 'surgical capacity (e.g., Kaltungo Nigeria, Kilifi Kenya)', # Snake species distribution [1][3][4] 'species_probs': { 'echis_ocellatus': 0.30, # Carpet viper 'bitis_arietans': 0.25, # Puff adder 'naja_nigricollis': 0.15, # Black-necked spitting cobra 'naja_haje': 0.08, # Egyptian cobra 'dendroaspis_polylepis': 0.05, # Black mamba 'dendroaspis_viridis': 0.03, # Green mamba 'causus_rhombeatus': 0.04, # Night adder 'atractaspis_spp': 0.03, # Burrowing asp 'unknown_unidentified': 0.07, }, 'dry_bite_rate': 0.20, # No envenomation 'antivenom_available': 0.70, 'antivenom_given_if_needed': 0.80, 'surgical_capacity': 0.90, 'time_to_facility_hrs_mean': 4, 'case_fatality': 0.04, }, 'district_hospital': { 'description': 'District hospital, intermittent antivenom supply, ' 'basic surgical (e.g., district hospitals Ghana, Tanzania)', 'species_probs': { 'echis_ocellatus': 0.28, 'bitis_arietans': 0.28, 'naja_nigricollis': 0.14, 'naja_haje': 0.06, 'dendroaspis_polylepis': 0.04, 'dendroaspis_viridis': 0.02, 'causus_rhombeatus': 0.06, 'atractaspis_spp': 0.03, 'unknown_unidentified': 0.09, }, 'dry_bite_rate': 0.20, 'antivenom_available': 0.35, 'antivenom_given_if_needed': 0.60, 'surgical_capacity': 0.50, 'time_to_facility_hrs_mean': 8, 'case_fatality': 0.08, }, 'rural_health_centre': { 'description': 'Rural PHC/health centre, no antivenom, referral ' 'needed for severe cases (e.g., rural DRC, Niger, Chad)', 'species_probs': { 'echis_ocellatus': 0.30, 'bitis_arietans': 0.30, 'naja_nigricollis': 0.12, 'naja_haje': 0.05, 'dendroaspis_polylepis': 0.05, 'dendroaspis_viridis': 0.02, 'causus_rhombeatus': 0.05, 'atractaspis_spp': 0.02, 'unknown_unidentified': 0.09, }, 'dry_bite_rate': 0.20, 'antivenom_available': 0.05, 'antivenom_given_if_needed': 0.20, 'surgical_capacity': 0.10, 'time_to_facility_hrs_mean': 16, 'case_fatality': 0.14, }, } # Envenomation syndrome by species [4] SYNDROME_BY_SPECIES = { 'echis_ocellatus': {'hemotoxic': 0.70, 'cytotoxic': 0.25, 'mixed': 0.05}, 'bitis_arietans': {'cytotoxic': 0.60, 'hemotoxic': 0.30, 'mixed': 0.10}, 'naja_nigricollis': {'cytotoxic': 0.55, 'neurotoxic': 0.35, 'mixed': 0.10}, 'naja_haje': {'neurotoxic': 0.60, 'cytotoxic': 0.30, 'mixed': 0.10}, 'dendroaspis_polylepis': {'neurotoxic': 0.85, 'mixed': 0.15}, 'dendroaspis_viridis': {'neurotoxic': 0.80, 'mixed': 0.20}, 'causus_rhombeatus': {'cytotoxic': 0.90, 'hemotoxic': 0.10}, 'atractaspis_spp': {'cytotoxic': 0.70, 'cardiotoxic': 0.30}, 'unknown_unidentified': {'cytotoxic': 0.45, 'hemotoxic': 0.30, 'neurotoxic': 0.15, 'mixed': 0.10}, } def sample_categorical(probs_dict, rng): labels = list(probs_dict.keys()) probs = np.array(list(probs_dict.values())) probs = probs / probs.sum() return rng.choice(labels, p=probs) def generate_dataset(n=10000, seed=42, scenario='district_hospital'): rng = np.random.default_rng(seed) sc = SCENARIOS[scenario] records = [] for idx in range(n): rec = {'id': idx + 1} # ── Step 1: Demographics [5] ── r = rng.random() if r < 0.15: rec['age_years'] = rng.integers(1, 10) elif r < 0.30: rec['age_years'] = rng.integers(10, 18) elif r < 0.65: rec['age_years'] = rng.integers(18, 40) elif r < 0.85: rec['age_years'] = rng.integers(40, 60) else: rec['age_years'] = rng.integers(60, 80) rec['sex'] = rng.choice(['M', 'F'], p=[0.65, 0.35]) # Male predominance [5] if rec['age_years'] >= 18: rec['occupation'] = rng.choice( ['farmer', 'herder', 'hunter', 'trader', 'student', 'other'], p=[0.45, 0.15, 0.10, 0.10, 0.10, 0.10]) else: rec['occupation'] = rng.choice(['student', 'child', 'herder'], p=[0.40, 0.45, 0.15]) rec['bite_location'] = rng.choice( ['foot', 'ankle', 'lower_leg', 'hand', 'forearm', 'other'], p=[0.30, 0.20, 0.15, 0.20, 0.10, 0.05]) rec['activity_at_bite'] = rng.choice( ['farming', 'walking', 'sleeping', 'herding', 'collecting_firewood', 'other'], p=[0.35, 0.25, 0.15, 0.10, 0.10, 0.05]) # Time to facility [8] rec['time_to_facility_hours'] = max(0.5, round( rng.exponential(sc['time_to_facility_hrs_mean']), 1)) rec['time_to_facility_hours'] = min(rec['time_to_facility_hours'], 72) # Traditional treatment before hospital rec['traditional_treatment_first'] = 1 if rng.random() < 0.40 else 0 if rec['traditional_treatment_first']: rec['time_to_facility_hours'] *= 1.5 # ── Step 2: Snake Species [1][4] ── rec['snake_species'] = sample_categorical(sc['species_probs'], rng) # ── Step 3: Envenomation & Syndrome ── rec['dry_bite'] = 1 if rng.random() < sc['dry_bite_rate'] else 0 if rec['dry_bite']: rec['envenomation_syndrome'] = 'none' rec['severity'] = 'none' else: syndrome_probs = SYNDROME_BY_SPECIES.get( rec['snake_species'], {'cytotoxic': 0.50, 'hemotoxic': 0.30, 'neurotoxic': 0.20}) rec['envenomation_syndrome'] = sample_categorical(syndrome_probs, rng) # Severity grading sev_roll = rng.random() if sev_roll < 0.30: rec['severity'] = 'mild' elif sev_roll < 0.70: rec['severity'] = 'moderate' else: rec['severity'] = 'severe' # Mamba bites more often severe if rec['snake_species'].startswith('dendroaspis'): if rng.random() < 0.4: rec['severity'] = 'severe' # ── Step 4: Clinical Features [3][4] ── rec['local_swelling'] = 0 rec['local_necrosis'] = 0 rec['coagulopathy'] = 0 rec['inr_elevated'] = 0 rec['bleeding'] = 0 rec['neurotoxicity'] = 0 rec['ptosis'] = 0 rec['respiratory_failure'] = 0 rec['acute_kidney_injury'] = 0 rec['compartment_syndrome'] = 0 if not rec['dry_bite']: # Local effects if rec['envenomation_syndrome'] in ('cytotoxic', 'mixed'): rec['local_swelling'] = 1 if rng.random() < 0.92 else 0 if rec['severity'] == 'severe': rec['local_necrosis'] = 1 if rng.random() < 0.45 else 0 rec['compartment_syndrome'] = 1 if rng.random() < 0.15 else 0 elif rec['severity'] == 'moderate': rec['local_necrosis'] = 1 if rng.random() < 0.15 else 0 elif rec['envenomation_syndrome'] == 'hemotoxic': rec['local_swelling'] = 1 if rng.random() < 0.85 else 0 # Hemotoxic effects [3] if rec['envenomation_syndrome'] in ('hemotoxic', 'mixed'): rec['coagulopathy'] = 1 if rng.random() < 0.75 else 0 if rec['coagulopathy']: rec['inr_elevated'] = 1 rec['bleeding'] = 1 if rng.random() < 0.40 else 0 rec['acute_kidney_injury'] = 1 if rng.random() < 0.20 else 0 # Neurotoxic effects [4] if rec['envenomation_syndrome'] in ('neurotoxic', 'mixed'): rec['neurotoxicity'] = 1 if rng.random() < 0.80 else 0 if rec['neurotoxicity']: rec['ptosis'] = 1 if rng.random() < 0.70 else 0 if rec['severity'] == 'severe': rec['respiratory_failure'] = 1 if rng.random() < 0.35 else 0 # Delayed presentation worsens everything if rec['time_to_facility_hours'] > 12: if not rec['compartment_syndrome'] and rec['local_swelling']: rec['compartment_syndrome'] = 1 if rng.random() < 0.10 else 0 if not rec['acute_kidney_injury'] and rec['coagulopathy']: rec['acute_kidney_injury'] = 1 if rng.random() < 0.15 else 0 # ── Step 5: Treatment [7][8] ── rec['antivenom_given'] = 0 rec['antivenom_vials'] = 0 rec['antivenom_reaction'] = 0 rec['wound_care'] = 0 rec['fasciotomy'] = 0 rec['blood_transfusion'] = 0 rec['mechanical_ventilation'] = 0 rec['dialysis'] = 0 rec['amputation'] = 0 rec['antibiotics_given'] = 0 if not rec['dry_bite']: # Antivenom needs_antivenom = rec['severity'] in ('moderate', 'severe') if needs_antivenom: av_available = rng.random() < sc['antivenom_available'] if av_available: rec['antivenom_given'] = 1 if rng.random() < sc['antivenom_given_if_needed'] else 0 if rec['antivenom_given']: if rec['severity'] == 'severe': rec['antivenom_vials'] = rng.integers(3, 10) else: rec['antivenom_vials'] = rng.integers(1, 5) rec['antivenom_reaction'] = 1 if rng.random() < 0.15 else 0 # Wound care rec['wound_care'] = 1 if rng.random() < 0.85 else 0 rec['antibiotics_given'] = 1 if rng.random() < 0.70 else 0 # Surgery if rec['compartment_syndrome'] and rng.random() < sc['surgical_capacity']: rec['fasciotomy'] = 1 if rec['local_necrosis'] and rec['severity'] == 'severe': if rec['time_to_facility_hours'] > 24 or not rec['antivenom_given']: rec['amputation'] = 1 if rng.random() < 0.12 else 0 # Supportive if rec['bleeding'] and rng.random() < 0.30: rec['blood_transfusion'] = 1 if rec['respiratory_failure'] and rng.random() < sc['surgical_capacity']: rec['mechanical_ventilation'] = 1 if rec['acute_kidney_injury'] and rec['severity'] == 'severe': if rng.random() < 0.15: rec['dialysis'] = 1 # ── Step 6: Outcome ── mort = 0.005 # Dry bite / mild if not rec['dry_bite']: if rec['severity'] == 'severe': mort = sc['case_fatality'] * 2.5 elif rec['severity'] == 'moderate': mort = sc['case_fatality'] else: mort = sc['case_fatality'] * 0.3 # Antivenom protective if rec['antivenom_given']: mort *= 0.40 # Delayed presentation increases mortality if rec['time_to_facility_hours'] > 12: mort *= 1.5 if rec['time_to_facility_hours'] > 24: mort *= 1.5 # Specific high-risk features if rec['respiratory_failure']: if not rec['mechanical_ventilation']: mort = min(mort * 3, 0.80) if rec['acute_kidney_injury'] and rec['severity'] == 'severe': mort *= 1.5 # Children more vulnerable if rec['age_years'] < 10: mort *= 1.5 if rng.random() < min(mort, 0.70): rec['outcome'] = 'died' elif rec['amputation']: rec['outcome'] = 'survived_with_disability' elif rec['local_necrosis'] or rec['compartment_syndrome']: rec['outcome'] = 'survived_with_sequelae' else: rec['outcome'] = 'survived_full_recovery' # Hospital days if rec['outcome'] == 'died': rec['hospital_days'] = max(0, rng.integers(0, 7)) elif rec['dry_bite'] or rec['severity'] in ('none', 'mild'): rec['hospital_days'] = rng.integers(1, 3) elif rec['severity'] == 'moderate': rec['hospital_days'] = rng.integers(2, 8) else: rec['hospital_days'] = rng.integers(5, 21) records.append(rec) df = pd.DataFrame(records) # ── Print Summary ── print(f"\n{'='*65}") print(f"Snakebite Envenomation — {scenario} (n={n}, seed={seed})") print(f"{'='*65}") print(f"\n Dry bites: {df['dry_bite'].sum()} ({df['dry_bite'].mean()*100:.1f}%)") env = df[df['dry_bite'] == 0] print(f" Envenomated: {len(env)}") print(f"\n Top species:") for sp, ct in df['snake_species'].value_counts().head(5).items(): print(f" {sp}: {ct} ({ct/n*100:.1f}%)") print(f"\n Syndrome (envenomated):") for syn, ct in env['envenomation_syndrome'].value_counts().items(): print(f" {syn}: {ct} ({ct/len(env)*100:.1f}%)") print(f"\n Severity (envenomated):") for sev in ['mild', 'moderate', 'severe']: ct = (env['severity'] == sev).sum() print(f" {sev}: {ct} ({ct/len(env)*100:.1f}%)") print(f"\n Antivenom given: {df['antivenom_given'].sum()} " f"({df['antivenom_given'].mean()*100:.1f}%)") print(f" Mean time to facility: {df['time_to_facility_hours'].mean():.1f} hrs") print(f" Traditional treatment first: {df['traditional_treatment_first'].mean()*100:.1f}%") died = (df['outcome'] == 'died').sum() amp = df['amputation'].sum() print(f"\n Deaths: {died} ({died/n*100:.1f}%)") print(f" Amputations: {amp}") return df if __name__ == '__main__': parser = argparse.ArgumentParser( description='Generate synthetic snakebite envenomation dataset') parser.add_argument('--scenario', type=str, default='district_hospital', choices=list(SCENARIOS.keys())) parser.add_argument('--n', type=int, default=10000) parser.add_argument('--seed', type=int, default=42) parser.add_argument('--output', type=str, default=None) parser.add_argument('--all-scenarios', action='store_true') args = parser.parse_args() os.makedirs('data', exist_ok=True) if args.all_scenarios: for sc_name in SCENARIOS: df = generate_dataset(n=args.n, seed=args.seed, scenario=sc_name) out = os.path.join('data', f'snakebite_{sc_name}.csv') df.to_csv(out, index=False) print(f" → Saved to {out}\n") else: df = generate_dataset(n=args.n, seed=args.seed, scenario=args.scenario) out = args.output or os.path.join('data', f'snakebite_{args.scenario}.csv') df.to_csv(out, index=False) print(f" → Saved to {out}")