#!/usr/bin/env python3 """ HIV Viral Load & CD4 Testing Dataset ======================================== Each record = ONE HIV viral load or CD4 test request/result. Literature-Grounded Parameterization: [1] WHO (2023). HIV treatment guidelines. - VL preferred monitoring; target <1000 copies/mL - CD4 for staging and OI prophylaxis - VL coverage SSA: ~70% of ART patients [2] UNAIDS (2023). Global HIV statistics. - SSA: 25.6M PLHIV, 21M on ART - VL suppression: 76% of those on ART [3] Lecher SL et al. (2021). Scale-up HIV VL monitoring. MMWR. DOI: 10.15585/mmwr.mm7034a2 - VL coverage 17% to 71% (2015-2019) - Centralized testing: TAT weeks [4] Jani IV et al. (2016). POC CD4 and VL testing. Lancet HIV. DOI: 10.1016/S2352-3018(15)00236-X - POC reduces loss to follow-up - Conventional VL TAT: 2-6 weeks - POC VL TAT: 1-3 hours - DBS for sample transport """ import numpy as np, pandas as pd, argparse, os SCENARIOS = { 'vl_accessible': { 'exemplar': 'South Africa/Botswana/Kenya (urban)', 'vl_coverage': 0.75, 'cd4_available': 0.85, 'poc_vl_available': 0.20, 'dbs_used': 0.15, 'conventional_vl': 0.65, 'vl_suppression_rate': 0.80, 'cd4_median_on_art': 450, 'tat_vl_days': 7, 'tat_cd4_days': 1, 'eac_for_unsuppressed': 0.60, 'result_returned_patient': 0.70, 'reagent_stockout': 0.10, }, 'vl_limited': { 'exemplar': 'Kenya (district)/Ghana/Tanzania/Ethiopia', 'vl_coverage': 0.35, 'cd4_available': 0.50, 'poc_vl_available': 0.05, 'dbs_used': 0.30, 'conventional_vl': 0.25, 'vl_suppression_rate': 0.72, 'cd4_median_on_art': 380, 'tat_vl_days': 21, 'tat_cd4_days': 3, 'eac_for_unsuppressed': 0.30, 'result_returned_patient': 0.40, 'reagent_stockout': 0.30, }, 'no_vl_access': { 'exemplar': 'DRC/CAR/Sierra Leone/Niger (rural)', 'vl_coverage': 0.08, 'cd4_available': 0.15, 'poc_vl_available': 0.01, 'dbs_used': 0.05, 'conventional_vl': 0.05, 'vl_suppression_rate': 0.60, 'cd4_median_on_art': 300, 'tat_vl_days': 45, 'tat_cd4_days': 14, 'eac_for_unsuppressed': 0.10, 'result_returned_patient': 0.15, 'reagent_stockout': 0.55, }, } def generate_dataset(n=10000, seed=42, scenario='vl_limited'): rng = np.random.default_rng(seed) sc = SCENARIOS[scenario] records = [] for idx in range(n): rec = {'id': idx + 1} rec['age'] = int(np.clip(rng.normal(38, 12), 1, 80)) rec['sex'] = rng.choice(['male','female'], p=[0.38, 0.62]) rec['pregnant'] = 1 if (rec['sex']=='female' and 15<=rec['age']<=45 and rng.random()<0.08) else 0 rec['facility_level'] = rng.choice(['tertiary','district','health_centre','clinic'], p=[0.10, 0.25, 0.35, 0.30]) rec['on_art'] = 1 if rng.random() < 0.85 else 0 rec['art_duration_months'] = int(rng.exponential(36)) if rec['on_art'] else 0 rec['art_regimen'] = rng.choice(['TLD','TLE','AZT_based','PI_based','other'], p=[0.40, 0.25, 0.15, 0.10, 0.10]) if rec['on_art'] else 'none' # Test type ordered rec['vl_ordered'] = 1 if rng.random() < sc['vl_coverage'] else 0 rec['cd4_ordered'] = 1 if rng.random() < sc['cd4_available'] else 0 # VL testing tier = 1.3 if rec['facility_level'] in ['tertiary','district'] else 0.7 if rec['vl_ordered']: rec['vl_platform'] = rng.choice(['conventional_central','poc_vl','dbs_referred'], p=[sc['conventional_vl']*tier/(sc['conventional_vl']*tier+sc['poc_vl_available']+sc['dbs_used']+0.01), sc['poc_vl_available']/(sc['conventional_vl']*tier+sc['poc_vl_available']+sc['dbs_used']+0.01), (sc['dbs_used']+0.01)/(sc['conventional_vl']*tier+sc['poc_vl_available']+sc['dbs_used']+0.01)]) if rec['on_art']: suppressed = rng.random() < sc['vl_suppression_rate'] if suppressed: rec['vl_result'] = int(np.clip(rng.exponential(50), 0, 999)) else: rec['vl_result'] = int(np.clip(rng.lognormal(np.log(5000), 1.5), 1000, 500000)) else: rec['vl_result'] = int(np.clip(rng.lognormal(np.log(50000), 1.2), 1000, 1000000)) rec['vl_suppressed'] = 1 if rec['vl_result'] < 1000 else 0 rec['vl_tat_days'] = round(max(0.1, rng.lognormal( np.log(0.1 if rec['vl_platform']=='poc_vl' else sc['tat_vl_days']), 0.5)), 1) else: rec['vl_platform'] = 'not_done' rec['vl_result'] = np.nan rec['vl_suppressed'] = np.nan rec['vl_tat_days'] = np.nan # CD4 testing if rec['cd4_ordered']: rec['cd4_platform'] = rng.choice(['flow_cytometry','poc_cd4','manual'], p=[0.40, 0.30, 0.30] if sc['cd4_available']>0.5 else [0.15, 0.25, 0.60]) if rec['on_art']: rec['cd4_result'] = int(np.clip(rng.normal(sc['cd4_median_on_art'], 180), 10, 1500)) else: rec['cd4_result'] = int(np.clip(rng.lognormal(np.log(200), 0.7), 5, 800)) rec['cd4_below_200'] = 1 if rec['cd4_result'] < 200 else 0 rec['cd4_tat_days'] = round(max(0.1, rng.lognormal(np.log(sc['tat_cd4_days']), 0.5)), 1) else: rec['cd4_platform'] = 'not_done' rec['cd4_result'] = np.nan rec['cd4_below_200'] = np.nan rec['cd4_tat_days'] = np.nan # Clinical actions rec['result_returned'] = 1 if rng.random() < sc['result_returned_patient'] else 0 rec['eac_provided'] = 1 if (rec['vl_ordered'] and rec.get('vl_suppressed')==0 and rng.random() < sc['eac_for_unsuppressed']) else 0 rec['regimen_switch'] = 1 if (rec['vl_ordered'] and rec.get('vl_suppressed')==0 and rng.random() < 0.15) else 0 rec['oi_prophylaxis'] = 1 if (rec['cd4_ordered'] and rec.get('cd4_below_200')==1 and rng.random() < 0.65) else 0 # Quality rec['reagent_stockout'] = 1 if rng.random() < sc['reagent_stockout'] else 0 rec['sample_rejected'] = 1 if rng.random() < 0.04 else 0 rec['test_not_done_stockout'] = 1 if (rec['reagent_stockout'] and rng.random() < 0.40) else 0 rec['specimen_transport_issue'] = 1 if (rec.get('vl_platform') in ['dbs_referred','conventional_central'] and rng.random() < 0.12) else 0 rec['year'] = rng.choice([2019,2020,2021,2022,2023], p=[0.12,0.18,0.20,0.25,0.25]) records.append(rec) df = pd.DataFrame(records) print(f"\n{'='*60}\nHIV Viral Load & CD4 — {scenario} ({sc['exemplar']})") print(f" VL ordered: {df['vl_ordered'].mean()*100:.1f}% | CD4 ordered: {df['cd4_ordered'].mean()*100:.1f}%") vl_done = df[df['vl_ordered']==1] if len(vl_done)>0: print(f" VL suppressed: {vl_done['vl_suppressed'].mean()*100:.1f}% | VL TAT median: {vl_done['vl_tat_days'].median():.1f}d") print(f" Result returned: {df['result_returned'].mean()*100:.1f}% | Stockout: {df['reagent_stockout'].mean()*100:.1f}%") return df if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--all-scenarios', action='store_true') parser.add_argument('--n', type=int, default=10000) parser.add_argument('--seed', type=int, default=42) args = parser.parse_args() os.makedirs('data', exist_ok=True) if args.all_scenarios: for sc in SCENARIOS: df = generate_dataset(n=args.n, seed=args.seed, scenario=sc) df.to_csv(os.path.join('data', f'hiv_vl_cd4_{sc}.csv'), index=False) print(f" -> Saved\n") else: df = generate_dataset(n=args.n, seed=args.seed) df.to_csv(os.path.join('data', 'hiv_vl_cd4_vl_limited.csv'), index=False)