#!/usr/bin/env python3 """ Literature-Informed Epilepsy & Neurological Disorders Dataset ============================================================== Generates realistic synthetic records of epilepsy patients in sub-Saharan Africa, including seizure type, diagnosis, AED access, treatment gap, stigma, and outcomes. References (web-searched): ----------- [1] PMC 2012. Epilepsy treatment SSA closing the gap. Prevalence highest in LMIC. >80% not receiving care. [2] PMC 2024. Epilepsy in Africa multifaceted perspective. Phenobarbital only drug widely available. Treatment gap. [3] Lancet Neurology. <0.1 neurologists per 100K in SSA. [4] WHO 2024. Epilepsy fact sheet. 50M worldwide. 80% LMIC. """ import numpy as np import pandas as pd import argparse import os SCENARIOS = { 'neurology_clinic': { 'description': 'Urban neurology clinic with neurologist, EEG, ' 'CT/MRI, multiple AEDs, surgical evaluation ' '(e.g., university hospitals SA, Kenya, Nigeria)', 'neurologist_available': True, 'eeg_available': True, 'ct_mri_available': True, 'aed_variety': True, 'surgery_available': True, 'treatment_gap': 0.25, }, 'district_hospital': { 'description': 'District hospital with clinical officer, ' 'phenobarbital only, no EEG, no imaging ' '(e.g., district hospitals Malawi, Uganda)', 'neurologist_available': False, 'eeg_available': False, 'ct_mri_available': False, 'aed_variety': False, 'surgery_available': False, 'treatment_gap': 0.55, }, 'rural_health_centre': { 'description': 'Rural health centre, nurse-led, phenobarbital ' 'often stocked out, traditional healers primary ' '(e.g., rural DRC, Niger, CAR)', 'neurologist_available': False, 'eeg_available': False, 'ct_mri_available': False, 'aed_variety': False, 'surgery_available': False, 'treatment_gap': 0.80, }, } 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} # ── 1. Demographics ── rec['age'] = max(1, min(85, int(rng.exponential(22) + 5))) rec['sex'] = rng.choice(['M', 'F'], p=[0.52, 0.48]) rec['child'] = 1 if rec['age'] < 15 else 0 rec['urban'] = 1 if rng.random() < 0.30 else 0 rec['education'] = rng.choice( ['none', 'primary', 'secondary', 'tertiary'], p=[0.30, 0.30, 0.28, 0.12]) rec['employed'] = 1 if rng.random() < 0.30 else 0 rec['hiv_positive'] = 1 if rng.random() < 0.06 else 0 # ── 2. Epilepsy presentation [1][2] ── rec['seizure_type'] = rng.choice( ['generalised_tonic_clonic', 'focal', 'focal_to_bilateral', 'absence', 'myoclonic', 'unknown'], p=[0.40, 0.20, 0.15, 0.08, 0.05, 0.12]) if rec['child']: rec['seizure_type'] = rng.choice( ['generalised_tonic_clonic', 'focal', 'absence', 'myoclonic', 'febrile_seizure', 'unknown'], p=[0.30, 0.15, 0.12, 0.08, 0.20, 0.15]) rec['seizure_frequency_month'] = max(0, int(rng.exponential(3))) rec['age_at_onset'] = max(0, min(rec['age'], int(rng.exponential(12) + 2))) rec['duration_years'] = max(0, rec['age'] - rec['age_at_onset']) rec['status_epilepticus_history'] = 1 if rng.random() < 0.08 else 0 rec['etiology'] = rng.choice( ['unknown', 'perinatal_injury', 'cns_infection', 'traumatic_brain_injury', 'cerebrovascular', 'brain_tumor', 'genetic', 'febrile'], p=[0.35, 0.15, 0.12, 0.10, 0.08, 0.05, 0.05, 0.10]) if rec['child']: rec['etiology'] = rng.choice( ['unknown', 'perinatal_injury', 'cns_infection', 'febrile', 'genetic'], p=[0.30, 0.25, 0.15, 0.20, 0.10]) # ── 3. Diagnosis & investigation ── rec['clinical_diagnosis_only'] = 1 rec['eeg_done'] = 0 if sc['eeg_available']: rec['eeg_done'] = 1 if rng.random() < 0.45 else 0 rec['ct_done'] = 0 rec['mri_done'] = 0 if sc['ct_mri_available']: rec['ct_done'] = 1 if rng.random() < 0.35 else 0 rec['mri_done'] = 1 if rng.random() < 0.15 else 0 if rec['eeg_done'] or rec['ct_done'] or rec['mri_done']: rec['clinical_diagnosis_only'] = 0 rec['neurologist_seen'] = 0 if sc['neurologist_available']: rec['neurologist_seen'] = 1 if rng.random() < 0.55 else 0 rec['misdiagnosed_as_spiritual'] = 1 if rng.random() < 0.25 else 0 # ── 4. Treatment [1][2] ── rec['in_treatment_gap'] = 1 if rng.random() < sc['treatment_gap'] else 0 rec['aed_prescribed'] = 'none' if not rec['in_treatment_gap']: if sc['aed_variety']: rec['aed_prescribed'] = rng.choice( ['phenobarbital', 'carbamazepine', 'valproate', 'phenytoin', 'levetiracetam', 'lamotrigine'], p=[0.20, 0.25, 0.20, 0.10, 0.15, 0.10]) else: rec['aed_prescribed'] = rng.choice( ['phenobarbital', 'carbamazepine', 'none'], p=[0.65, 0.15, 0.20]) rec['aed_affordable'] = 0 if rec['aed_prescribed'] != 'none': rec['aed_affordable'] = 1 if rng.random() < 0.50 else 0 rec['aed_adherent'] = 0 if rec['aed_prescribed'] != 'none' and rec['aed_affordable']: rec['aed_adherent'] = 1 if rng.random() < 0.55 else 0 rec['aed_stock_out_experienced'] = 0 if rec['aed_prescribed'] != 'none': stockout_p = 0.10 if sc['aed_variety'] else (0.25 if scenario == 'district_hospital' else 0.45) rec['aed_stock_out_experienced'] = 1 if rng.random() < stockout_p else 0 rec['traditional_healer_consulted'] = 1 if rng.random() < 0.40 else 0 rec['faith_healing_sought'] = 1 if rng.random() < 0.30 else 0 rec['surgery_evaluated'] = 0 rec['surgery_performed'] = 0 if sc['surgery_available'] and rec['seizure_frequency_month'] > 4: rec['surgery_evaluated'] = 1 if rng.random() < 0.10 else 0 if rec['surgery_evaluated']: rec['surgery_performed'] = 1 if rng.random() < 0.20 else 0 rec['referred_specialist'] = 0 if not sc['neurologist_available'] and rec['seizure_frequency_month'] > 4: rec['referred_specialist'] = 1 if rng.random() < 0.15 else 0 rec['referral_completed'] = 0 if rec['referred_specialist']: rec['referral_completed'] = 1 if rng.random() < 0.20 else 0 # ── 5. Stigma & psychosocial [2] ── rec['stigma_experienced'] = 1 if rng.random() < 0.50 else 0 rec['school_exclusion'] = 0 if rec['child'] and rec['stigma_experienced']: rec['school_exclusion'] = 1 if rng.random() < 0.30 else 0 rec['employment_discrimination'] = 0 if not rec['child'] and rec['stigma_experienced']: rec['employment_discrimination'] = 1 if rng.random() < 0.35 else 0 rec['marriage_affected'] = 0 if rec['age'] >= 15 and rec['stigma_experienced']: rec['marriage_affected'] = 1 if rng.random() < 0.25 else 0 rec['depression_comorbid'] = 1 if rng.random() < 0.25 else 0 rec['anxiety_comorbid'] = 1 if rng.random() < 0.20 else 0 rec['conceals_diagnosis'] = 1 if rng.random() < 0.45 else 0 # ── 6. Outcomes ── rec['seizure_free_12m'] = 0 if rec['aed_adherent']: rec['seizure_free_12m'] = 1 if rng.random() < 0.45 else 0 elif rec['aed_prescribed'] != 'none' and not rec['aed_adherent']: rec['seizure_free_12m'] = 1 if rng.random() < 0.10 else 0 rec['seizure_related_injury'] = 1 if rng.random() < 0.15 else 0 rec['seizure_related_burn'] = 1 if rec['seizure_related_injury'] and rng.random() < 0.30 else 0 rec['seizure_related_drowning'] = 1 if rec['seizure_related_injury'] and rng.random() < 0.10 else 0 rec['died'] = 0 if rec['status_epilepticus_history'] and rng.random() < 0.05: rec['died'] = 1 elif rec['in_treatment_gap'] and rng.random() < 0.02: rec['died'] = 1 rec['quality_of_life_impaired'] = 0 if rec['seizure_frequency_month'] > 2 or rec['stigma_experienced'] or rec['depression_comorbid']: rec['quality_of_life_impaired'] = 1 if rng.random() < 0.65 else 0 records.append(rec) df = pd.DataFrame(records) print(f"\n{'='*65}") print(f"Epilepsy — {scenario} (n={n}, seed={seed})") print(f"{'='*65}") print(f"\n Treatment gap: {df['in_treatment_gap'].mean()*100:.1f}%") print(f" AED prescribed: {(df['aed_prescribed']!='none').mean()*100:.1f}%") print(f" Seizure-free 12m: {df['seizure_free_12m'].mean()*100:.1f}%") print(f" Stigma: {df['stigma_experienced'].mean()*100:.1f}%") print(f" Traditional healer: {df['traditional_healer_consulted'].mean()*100:.1f}%") return df if __name__ == '__main__': parser = argparse.ArgumentParser( description='Generate epilepsy 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'epilepsy_{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'epilepsy_{args.scenario}.csv') df.to_csv(out, index=False) print(f" -> Saved to {out}")