#!/usr/bin/env python3 """ Literature-Informed Synthetic Community Health Worker iCCM Triage Dataset ========================================================================= Generates realistic synthetic datasets of sick child assessments by community health workers (CHWs) using integrated Community Case Management (iCCM) protocols. Children aged 2-59 months presenting with acute illness at community level in LMIC settings. iCCM covers three core conditions: - Malaria (fever + RDT) - Pneumonia (cough + fast breathing) - Diarrhoea (loose stools + dehydration assessment) Plus danger sign assessment for referral decisions. DAG (Sampling Order): 1. age_months, sex (roots) 2. presenting_complaint (from prevalence) 3. true_diagnosis (conditional on complaint + scenario) 4. symptoms & signs (conditional on diagnosis) 5. rdt_result (conditional on malaria status + test performance) 6. respiratory_rate, fast_breathing (conditional on pneumonia) 7. muac_cm (age-conditional, used for malnutrition screening) 8. danger_signs (conditional on severity) 9. chw_classification (iCCM algorithm output) 10. chw_action (treat/refer) References: ----------- [1] WHO/UNICEF (2012). Caring for the sick child in the community. Geneva. [2] WHO/UNICEF (2014). Integrated Community Case Management (iCCM): Evidence review. Geneva. [3] Marsh DR, et al. (2012). Introduction to a special supplement on iCCM. Am J Trop Med Hyg, 87(5 Suppl):1-5. [4] Druetz T, et al. (2015). Impact of iCCM on child mortality: a systematic review. Paediatrics & International Child Health, 35(1):18-29. [5] WHO (2023). World Malaria Report 2023. Geneva. [6] UNICEF (2019). Diarrhoea treatment guidelines. New York. [7] WHO (2014). Revised WHO classification and treatment of pneumonia in children at health facilities. Geneva. [8] DHS/MICS Program. Treatment-seeking and CHW utilization data. """ import numpy as np import pandas as pd import argparse import os # ============================================================ # SECTION 1: Literature-Informed Parameters # ============================================================ SCENARIOS = { 'low_burden': { 'description': 'Lower burden LMIC community (e.g., urban peri-urban)', 'malaria_pct': 0.20, # Of presenting children 'pneumonia_pct': 0.15, 'diarrhoea_pct': 0.25, 'mixed_pct': 0.08, # Multiple conditions 'other_febrile_pct': 0.32, # Other/viral 'danger_sign_rate': 0.04, # Need referral 'malnutrition_sam_rate': 0.02, 'malnutrition_mam_rate': 0.05, }, 'moderate_burden': { 'description': 'Average LMIC community (e.g., rural Kenya, Senegal)', 'malaria_pct': 0.35, 'pneumonia_pct': 0.18, 'diarrhoea_pct': 0.22, 'mixed_pct': 0.10, 'other_febrile_pct': 0.15, 'danger_sign_rate': 0.07, 'malnutrition_sam_rate': 0.04, 'malnutrition_mam_rate': 0.08, }, 'high_burden': { 'description': 'High burden / conflict (e.g., Sahel, DRC)', 'malaria_pct': 0.45, 'pneumonia_pct': 0.20, 'diarrhoea_pct': 0.18, 'mixed_pct': 0.12, 'other_febrile_pct': 0.05, 'danger_sign_rate': 0.12, 'malnutrition_sam_rate': 0.08, 'malnutrition_mam_rate': 0.12, }, } # --- RDT Performance (same as malaria dataset) --- RDT_SENSITIVITY = 0.93 RDT_SPECIFICITY = 0.95 # --- Respiratory rate thresholds (WHO/UNICEF iCCM) --- # Fast breathing: ≥50 breaths/min (2-11 months), ≥40 (12-59 months) RR_PNEUMONIA = {'mean': 56, 'sd': 8} # Pneumonia cases RR_NORMAL = {'mean': 32, 'sd': 6} # Non-pneumonia # --- MUAC reference --- MUAC_NORMAL = {'mean': 14.8, 'sd': 1.2} MUAC_MAM = {'mean': 12.0, 'sd': 0.3} # 11.5-12.4 cm MUAC_SAM = {'mean': 10.8, 'sd': 0.5} # <11.5 cm # ============================================================ # SECTION 2: Main Generator # ============================================================ def generate_iccm_dataset(n=10000, seed=42, scenario='moderate_burden'): rng = np.random.default_rng(seed) sc = SCENARIOS[scenario] # ── Step 1: Demographics ── sex = rng.choice(['M', 'F'], size=n, p=[0.512, 0.488]) age_months = rng.integers(2, 60, size=n) # ── Step 2: True diagnosis ── diag_probs = [sc['malaria_pct'], sc['pneumonia_pct'], sc['diarrhoea_pct'], sc['mixed_pct'], sc['other_febrile_pct']] diagnoses = ['malaria', 'pneumonia', 'diarrhoea', 'mixed', 'other_febrile'] true_diagnosis = rng.choice(diagnoses, size=n, p=diag_probs) # Mixed = malaria + pneumonia or malaria + diarrhoea mixed_type = np.array(['none'] * n, dtype=object) for i in range(n): if true_diagnosis[i] == 'mixed': mixed_type[i] = rng.choice(['malaria_pneumonia', 'malaria_diarrhoea'], p=[0.55, 0.45]) # ── Step 3: Fever ── fever = np.zeros(n, dtype=int) temperature = np.zeros(n) for i in range(n): if true_diagnosis[i] in ('malaria', 'other_febrile'): fever[i] = 1 if rng.random() < 0.88 else 0 elif true_diagnosis[i] == 'pneumonia': fever[i] = 1 if rng.random() < 0.65 else 0 elif true_diagnosis[i] == 'diarrhoea': fever[i] = 1 if rng.random() < 0.35 else 0 elif true_diagnosis[i] == 'mixed': fever[i] = 1 if rng.random() < 0.90 else 0 if fever[i]: temperature[i] = rng.normal(38.6, 0.7) else: temperature[i] = rng.normal(37.0, 0.3) temperature = np.clip(np.round(temperature, 1), 35.5, 41.5) fever_duration_days = np.where(fever, np.clip(rng.poisson(3, n), 1, 14), 0) # ── Step 4: Cough & respiratory signs ── cough = np.zeros(n, dtype=int) respiratory_rate = np.zeros(n, dtype=int) for i in range(n): has_resp = true_diagnosis[i] == 'pneumonia' or \ (true_diagnosis[i] == 'mixed' and 'pneumonia' in mixed_type[i]) if has_resp: cough[i] = 1 if rng.random() < 0.90 else 0 respiratory_rate[i] = max(15, int(rng.normal(RR_PNEUMONIA['mean'], RR_PNEUMONIA['sd']))) else: cough[i] = 1 if rng.random() < 0.25 else 0 respiratory_rate[i] = max(15, int(rng.normal(RR_NORMAL['mean'], RR_NORMAL['sd']))) # Fast breathing classification (WHO iCCM) fast_breathing = np.zeros(n, dtype=int) for i in range(n): threshold = 50 if age_months[i] < 12 else 40 fast_breathing[i] = 1 if respiratory_rate[i] >= threshold else 0 # ── Step 5: Diarrhoea signs ── diarrhoea = np.zeros(n, dtype=int) diarrhoea_days = np.zeros(n, dtype=int) blood_in_stool = np.zeros(n, dtype=int) dehydration_status = np.array(['none'] * n, dtype=object) for i in range(n): has_diarr = true_diagnosis[i] == 'diarrhoea' or \ (true_diagnosis[i] == 'mixed' and 'diarrhoea' in mixed_type[i]) if has_diarr: diarrhoea[i] = 1 diarrhoea_days[i] = max(1, rng.poisson(4)) blood_in_stool[i] = 1 if rng.random() < 0.08 else 0 r = rng.random() if r < 0.10: dehydration_status[i] = 'severe' elif r < 0.35: dehydration_status[i] = 'some' else: dehydration_status[i] = 'none' else: diarrhoea[i] = 1 if rng.random() < 0.08 else 0 if diarrhoea[i]: diarrhoea_days[i] = max(1, rng.poisson(2)) diarrhoea_days = np.clip(diarrhoea_days, 0, 21) # ── Step 6: RDT result ── has_malaria = np.array([ true_diagnosis[i] == 'malaria' or (true_diagnosis[i] == 'mixed' and 'malaria' in mixed_type[i]) for i in range(n) ]) rdt_result = np.zeros(n, dtype=int) for i in range(n): if has_malaria[i]: rdt_result[i] = 1 if rng.random() < RDT_SENSITIVITY else 0 else: rdt_result[i] = 1 if rng.random() < (1 - RDT_SPECIFICITY) else 0 # ── Step 7: MUAC ── muac = np.zeros(n) nutrition_status = np.array(['normal'] * n, dtype=object) for i in range(n): r = rng.random() if r < sc['malnutrition_sam_rate']: muac[i] = rng.normal(MUAC_SAM['mean'], MUAC_SAM['sd']) nutrition_status[i] = 'SAM' elif r < sc['malnutrition_sam_rate'] + sc['malnutrition_mam_rate']: muac[i] = rng.normal(MUAC_MAM['mean'], MUAC_MAM['sd']) nutrition_status[i] = 'MAM' else: muac[i] = rng.normal(MUAC_NORMAL['mean'], MUAC_NORMAL['sd']) muac = np.clip(np.round(muac, 1), 7.0, 20.0) # Reconcile MUAC with nutrition status nutrition_status = np.where(muac < 11.5, 'SAM', np.where(muac < 12.5, 'MAM', 'normal')) # ── Step 8: Danger signs (WHO/UNICEF iCCM) ── unable_to_drink = np.zeros(n, dtype=int) vomiting_everything = np.zeros(n, dtype=int) convulsions = np.zeros(n, dtype=int) lethargic_unconscious = np.zeros(n, dtype=int) chest_indrawing = np.zeros(n, dtype=int) for i in range(n): is_severe = rng.random() < sc['danger_sign_rate'] # More likely with younger children, malnourished if age_months[i] < 12: is_severe = is_severe or rng.random() < sc['danger_sign_rate'] * 0.5 if nutrition_status[i] == 'SAM': is_severe = is_severe or rng.random() < 0.15 if is_severe: unable_to_drink[i] = 1 if rng.random() < 0.45 else 0 vomiting_everything[i] = 1 if rng.random() < 0.40 else 0 convulsions[i] = 1 if rng.random() < 0.20 else 0 lethargic_unconscious[i] = 1 if rng.random() < 0.25 else 0 chest_indrawing[i] = 1 if rng.random() < 0.35 else 0 else: # Occasional isolated signs in non-severe vomiting_everything[i] = 1 if rng.random() < 0.02 else 0 chest_indrawing[i] = 1 if rng.random() < 0.01 else 0 any_danger_sign = ((unable_to_drink + vomiting_everything + convulsions + lethargic_unconscious + chest_indrawing) > 0).astype(int) # ── Step 9: CHW classification (iCCM algorithm) ── chw_classification = np.array(['other'] * n, dtype=object) for i in range(n): classifications = [] if rdt_result[i] == 1: classifications.append('malaria') if cough[i] and fast_breathing[i]: classifications.append('pneumonia') if diarrhoea[i]: classifications.append('diarrhoea') if nutrition_status[i] == 'SAM': classifications.append('severe_malnutrition') if len(classifications) == 0: chw_classification[i] = 'other_febrile' if fever[i] else 'well_child' elif len(classifications) == 1: chw_classification[i] = classifications[0] else: chw_classification[i] = '+'.join(sorted(classifications)) # ── Step 10: CHW action ── chw_action = np.array(['treat_at_community'] * n, dtype=object) for i in range(n): if any_danger_sign[i]: chw_action[i] = 'refer_urgently' elif nutrition_status[i] == 'SAM': chw_action[i] = 'refer_urgently' elif dehydration_status[i] == 'severe': chw_action[i] = 'refer_urgently' elif age_months[i] < 2: chw_action[i] = 'refer_urgently' # Very young infant elif rdt_result[i] and fast_breathing[i]: chw_action[i] = 'treat_and_refer' elif blood_in_stool[i]: chw_action[i] = 'refer' # Treatment given act_given = ((rdt_result == 1) & (chw_action != 'refer_urgently')).astype(int) amoxicillin_given = ((cough == 1) & (fast_breathing == 1) & (chw_action != 'refer_urgently')).astype(int) ors_given = ((diarrhoea == 1) & (chw_action != 'refer_urgently')).astype(int) zinc_given = ors_given.copy() # ── Assemble DataFrame ── df = pd.DataFrame({ 'id': np.arange(1, n + 1), 'age_months': age_months, 'sex': sex, 'fever': fever, 'temperature_c': temperature, 'fever_duration_days': fever_duration_days, 'cough': cough, 'respiratory_rate_bpm': respiratory_rate, 'fast_breathing': fast_breathing, 'chest_indrawing': chest_indrawing, 'diarrhoea': diarrhoea, 'diarrhoea_duration_days': diarrhoea_days, 'blood_in_stool': blood_in_stool, 'dehydration_status': dehydration_status, 'rdt_result': rdt_result, 'muac_cm': muac, 'nutrition_status': nutrition_status, 'unable_to_drink': unable_to_drink, 'vomiting_everything': vomiting_everything, 'convulsions': convulsions, 'lethargic_unconscious': lethargic_unconscious, 'any_danger_sign': any_danger_sign, 'true_diagnosis': true_diagnosis, 'chw_classification': chw_classification, 'chw_action': chw_action, 'act_given': act_given, 'amoxicillin_given': amoxicillin_given, 'ors_given': ors_given, 'zinc_given': zinc_given, }) # ── Print summary ── print(f"\n{'='*60}") print(f"iCCM CHW Triage — {scenario} (n={n}, seed={seed})") print(f"{'='*60}") print(f"\nTrue diagnosis:") for d in diagnoses: print(f" {d:20s}: {(true_diagnosis==d).mean()*100:.1f}%") print(f"\nRDT+: {rdt_result.mean()*100:.1f}%") print(f"Fast breathing: {fast_breathing.mean()*100:.1f}%") print(f"Diarrhoea: {(diarrhoea==1).mean()*100:.1f}%") print(f"Any danger sign: {any_danger_sign.mean()*100:.1f}%") print(f"SAM: {(nutrition_status=='SAM').mean()*100:.1f}%") print(f"Referral rate: {np.mean(['refer' in str(a) for a in chw_action])*100:.1f}%") print(f"ACT given: {act_given.mean()*100:.1f}%") print(f"Amoxicillin given: {amoxicillin_given.mean()*100:.1f}%") print(f"ORS given: {ors_given.mean()*100:.1f}%") return df # ============================================================ # SECTION 3: CLI Entry Point # ============================================================ if __name__ == '__main__': parser = argparse.ArgumentParser( description='Generate synthetic iCCM CHW triage dataset') parser.add_argument('--scenario', type=str, default='moderate_burden', 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_iccm_dataset(n=args.n, seed=args.seed, scenario=sc_name) out = os.path.join('data', f'iccm_{sc_name}.csv') df.to_csv(out, index=False) print(f" → Saved to {out}\n") else: df = generate_iccm_dataset(n=args.n, seed=args.seed, scenario=args.scenario) out = args.output or os.path.join('data', f'iccm_{args.scenario}.csv') df.to_csv(out, index=False) print(f" → Saved to {out}")