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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+language:
+  - en
+tags:
+  - synthetic
+  - healthcare
+  - immunisation
+  - vaccination
+  - epi
+  - zero-dose
+  - dropout
+  - equity
+  - who-unicef
+  - wuenic
+  - dhs
+  - lmic
+  - gavi
+pretty_name: "Synthetic Childhood Immunisation Coverage & Dropout Dataset (0-23 months)"
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: high_coverage
+    data_files: data/immunisation_high_coverage.csv
+  - config_name: moderate_coverage
+    data_files: data/immunisation_moderate_coverage.csv
+    default: true
+  - config_name: low_coverage
+    data_files: data/immunisation_low_coverage.csv
+---
+
+# Synthetic Childhood Immunisation Coverage & Dropout Dataset (0–23 months)
+
+## Abstract
+
+This dataset provides **30,000 synthetic records** (10,000 per scenario) of childhood immunisation status for children aged 0-23 months in LMIC settings. Each record contains 28 variables: demographics, socioeconomic determinants (wealth quintile, maternal education, urban/rural, distance to facility), individual vaccine doses (BCG, OPV0-3, Penta1-3, PCV1-3, Rota1-2, IPV1, MCV1-2), and derived indicators (fully immunised, zero-dose, dropout). Coverage and equity gradients are parameterized from WHO/UNICEF WUENIC estimates, Gavi zero-dose analytics, and DHS vaccination equity analyses. Three scenarios (high, moderate, low coverage) capture the spectrum from well-performing programmes to fragile/conflict-affected settings.
+
+## 1. Introduction
+
+Globally, 14.3 million children received no routine vaccines ("zero-dose") in 2022 (WUENIC 2023). Immunisation coverage inequities by wealth, geography, and education remain a central challenge for the Immunization Agenda 2030. Open-access individual-level vaccination datasets from LMICs are scarce—DHS microdata requires registration and is survey-weighted, making it unsuitable for direct ML training.
+
+This synthetic dataset addresses this gap for:
+
+- Training ML models for zero-dose identification and dropout prediction
+- Equity analysis and coverage gap modelling
+- Prototyping immunisation programme dashboards
+- Educational use in vaccinology and public health informatics
+
+**This dataset is entirely synthetic. It must not be used for clinical decision-making or programme evaluation.**
+
+## 2. Methodology
+
+### 2.1 Vaccine Schedule
+
+Based on WHO Expanded Programme on Immunization (EPI) recommendations:
+
+| Vaccine | Doses | Schedule | Disease Target |
+| --- | --- | --- | --- |
+| BCG | 1 | Birth | Tuberculosis |
+| OPV | 4 (0-3) | Birth, 6, 10, 14 weeks | Poliomyelitis |
+| Penta (DTP-HepB-Hib) | 3 | 6, 10, 14 weeks | Diphtheria, tetanus, pertussis, hepatitis B, Hib |
+| PCV | 3 | 6, 10, 14 weeks | Pneumococcal disease |
+| Rotavirus | 2 | 6, 10 weeks | Rotavirus diarrhoea |
+| IPV | 1 | 14 weeks | Poliomyelitis |
+| Measles (MCV) | 2 | 9, 15 months | Measles |
+
+### 2.2 Equity Determinants
+
+Individual coverage probability is modulated by five equity determinants, each with literature-grounded multipliers:
+
+| Determinant | Effect | Source |
+| --- | --- | --- |
+| Wealth quintile (1-5) | 0.65x (Q1) to 1.30x (Q5) | Restrepo-Méndez et al., Bull WHO 2016 |
+| Urban/Rural | 1.10x urban, 0.90x rural | DHS pooled estimates |
+| Maternal education | 0.70x (none) to 1.20x (tertiary) | Arsenault et al., Lancet Global Health 2017 |
+| Distance to facility | -1.2% per km | DHS access analyses |
+| Individual random effect | N(1.0, 0.08) | Unobserved heterogeneity |
+
+### 2.3 Scenario Design
+
+| Scenario | Context | BCG | Penta3 | MCV1 | MCV2 | Zero-dose |
+| --- | --- | --- | --- | --- | --- | --- |
+| High coverage | Well-performing LMIC | 75.5% | 34.8% | 64.1% | 40.1% | 4.8% |
+| Moderate coverage | Average LMIC | 66.5% | 27.8% | 55.4% | 33.4% | 11.6% |
+| Low coverage | Fragile/conflict | 40.5% | 10.7% | 33.1% | 15.8% | 33.9% |
+
+## 3. Dataset Description
+
+### 3.1 Schema
+
+| Column | Type | Description |
+| --- | --- | --- |
+| id | int | Unique identifier |
+| sex | categorical (M/F) | Biological sex |
+| age_months | float | Age in months (0-23.9) |
+| region_type | categorical | Urban or rural |
+| ses_quintile | int (1-5) | Socioeconomic status quintile (1=poorest) |
+| maternal_education | categorical | None, primary, secondary, tertiary |
+| distance_to_facility_km | float | Distance to nearest health facility |
+| bcg, opv0-3, penta1-3, pcv1-3, rota1-2, ipv1, mcv1-2 | binary (0/1) | Vaccine dose received |
+| total_basic_doses | int | Sum of BCG+Penta1-3+OPV1-3+MCV1 (max 8) |
+| fully_immunised | binary | All age-appropriate vaccines received |
+| dropout_penta1_penta3 | binary | Received Penta1 but not Penta3 |
+| dropout_penta1_mcv1 | binary | Received Penta1 but not MCV1 |
+| zero_dose | binary | No vaccines received at all |
+| immunisation_status | categorical | fully_immunised / partially_immunised / zero_dose |
+
+## 4. Validation
+
+### 4.1 Diagnostic Plots
+
+<p align="center">
+  <img src="validation_report.png" alt="Validation Report" width="100%">
+</p>
+
+## 5. Usage
+
+### 5.1 Loading with HuggingFace `datasets`
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("electricsheepafrica/synthetic-childhood-immunisation-coverage-dropout-WUENIC", "moderate_coverage")
+df = dataset["train"].to_pandas()
+```
+
+### 5.2 Regenerating
+
+```bash
+pip install numpy pandas matplotlib
+python generate_dataset.py --all-scenarios --n 10000 --seed 42
+python validate_dataset.py
+```
+
+## 6. Limitations
+
+- **Synthetic**: Not real programme data. Not for programme evaluation.
+- **No campaign vaccines**: Only routine EPI; does not model supplementary immunisation activities (SIAs).
+- **Cross-sectional**: Single snapshot; does not capture timeliness or catch-up dynamics.
+- **Simplified equity model**: Real equity determinants are more complex and context-specific.
+
+## 7. References
+
+1. WHO/UNICEF (2023). WUENIC Estimates of National Immunization Coverage.
+2. Gavi (2023). Zero-dose children: Key data and analytics.
+3. Restrepo-Méndez MC, et al. (2016). Inequalities in full immunization coverage. *Bull WHO*, 94:794-805.
+4. Arsenault C, et al. (2017). Equity in antenatal care quality. *Lancet Global Health*, 5(11):e1079-e1088.
+5. WHO (2022). Immunization Agenda 2030.
+6. DHS Program. Vaccination module, multiple countries 2015-2023.
+
+## Citation
+
+```bibtex
+@dataset{esa_immunisation_2025,
+  title={Synthetic Childhood Immunisation Coverage and Dropout Dataset},
+  author={Electric Sheep Africa},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/electricsheepafrica/synthetic-childhood-immunisation-coverage-dropout-WUENIC}
+}
+```
+
+## License
+
+[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)

generate_dataset.py

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+#!/usr/bin/env python3
+"""
+Literature-Informed Synthetic Childhood Immunisation Coverage & Dropout Dataset
+===============================================================================
+
+Generates realistic synthetic datasets of childhood immunisation records for
+children aged 0-23 months in LMIC settings, with vaccine-specific coverage,
+dropout indicators, and demographic/contextual variables.
+
+Target population: Children aged 0-23 months eligible for routine EPI vaccines
+in LMIC facility and community settings.
+
+DAG (Sampling Order):
+  1. sex (root)
+  2. age_months (root)
+  3. region_type (root: urban/rural)
+  4. ses_quintile (root: 1-5)
+  5. maternal_education (root)
+  6. distance_to_facility_km (conditional on region_type)
+  7. base_access_probability (conditional on ses, region, distance, education)
+  8. Individual vaccine doses (conditional on age, access probability, schedule)
+  9. Derived: fully_immunised, dropout indicators, zero-dose status
+
+References:
+-----------
+[1] WHO/UNICEF (2023). WHO/UNICEF Estimates of National Immunization Coverage
+    (WUENIC). Geneva/New York.
+[2] WHO (2023). Global Immunization Data. Immunization Dashboard.
+[3] Gavi (2023). Zero-dose children: Key data and analytics.
+[4] DHS Program. Demographic and Health Surveys, vaccination module,
+    multiple countries 2015-2023.
+[5] Restrepo-Méndez MC, et al. (2016). Inequalities in full immunization
+    coverage: trends in low- and middle-income countries. Bull WHO, 94:794-805.
+[6] WHO (2022). Immunization Agenda 2030. Geneva.
+[7] Arsenault C, et al. (2017). Equity in antenatal care quality: an analysis
+    of 91 national household surveys. Lancet Global Health, 5(11):e1079-e1088.
+[8] WHO (2023). WHO recommendations for routine immunization - summary tables.
+"""
+
+import numpy as np
+import pandas as pd
+import argparse
+import os
+
+# ============================================================
+# SECTION 1: Literature-Informed Parameters
+# ============================================================
+
+# --- EPI Schedule (WHO Expanded Programme on Immunization) ---
+# Source: WHO (2023) Routine immunization summary tables
+# Format: vaccine -> {dose: eligible_age_weeks}
+EPI_SCHEDULE = {
+    'bcg':    {1: 0},          # At birth
+    'opv':    {0: 0, 1: 6, 2: 10, 3: 14},  # OPV0 at birth, then 6/10/14 weeks
+    'penta':  {1: 6, 2: 10, 3: 14},         # DTP-HepB-Hib at 6/10/14 weeks
+    'pcv':    {1: 6, 2: 10, 3: 14},         # Pneumococcal conjugate
+    'rota':   {1: 6, 2: 10},                # Rotavirus (2-dose schedule)
+    'measles':{1: 39, 2: 65},               # MCV1 at 9 months, MCV2 at 15 months
+    'ipv':    {1: 14},                       # Inactivated polio at 14 weeks
+}
+
+# Convert weeks to months for age eligibility
+def weeks_to_months(w):
+    return w / 4.33
+
+# --- Coverage Levels by Scenario ---
+# Source: WUENIC 2023, DHS pooled estimates
+# These are BASE coverage probabilities for each antigen (dose 1 or single dose)
+SCENARIOS = {
+    'high_coverage': {
+        'description': 'Well-performing LMIC (e.g., Rwanda, Bangladesh urban)',
+        'bcg_coverage': 0.96,        # WUENIC 2023: 85-99%
+        'penta1_coverage': 0.95,     # WUENIC: 90-98%
+        'penta3_coverage': 0.88,     # WUENIC: 82-95%
+        'mcv1_coverage': 0.90,       # WUENIC: 85-95%
+        'mcv2_coverage': 0.78,       # WUENIC: 60-85%
+        'zero_dose_rate': 0.03,      # Gavi 2023: 2-5% in high-coverage
+        'dropout_penta13': 0.07,     # 5-10%
+        'urban_pct': 0.45,
+    },
+    'moderate_coverage': {
+        'description': 'Average LMIC (e.g., Kenya, Ghana, Senegal)',
+        'bcg_coverage': 0.89,
+        'penta1_coverage': 0.87,
+        'penta3_coverage': 0.78,
+        'mcv1_coverage': 0.80,
+        'mcv2_coverage': 0.55,
+        'zero_dose_rate': 0.10,      # Gavi 2023: 8-15%
+        'dropout_penta13': 0.12,
+        'urban_pct': 0.35,
+    },
+    'low_coverage': {
+        'description': 'Under-performing / conflict (e.g., CAR, South Sudan, Chad)',
+        'bcg_coverage': 0.68,
+        'penta1_coverage': 0.65,
+        'penta3_coverage': 0.48,
+        'mcv1_coverage': 0.52,
+        'mcv2_coverage': 0.28,
+        'zero_dose_rate': 0.25,      # Gavi 2023: 20-35% in fragile states
+        'dropout_penta13': 0.25,
+        'urban_pct': 0.25,
+    },
+}
+
+# --- Equity Gradients ---
+# Source: Restrepo-Méndez 2016, DHS equity analyses
+# Coverage ratio: richest quintile / poorest quintile
+# Typical equity ratio: 1.2-2.5x depending on setting
+SES_COVERAGE_MULTIPLIER = {
+    1: 0.65,  # Poorest quintile
+    2: 0.80,
+    3: 1.00,  # Middle (reference)
+    4: 1.15,
+    5: 1.30,  # Richest quintile
+}
+
+EDUCATION_COVERAGE_MULTIPLIER = {
+    'none': 0.70,
+    'primary': 0.85,
+    'secondary': 1.05,
+    'tertiary': 1.20,
+}
+
+REGION_COVERAGE_MULTIPLIER = {
+    'urban': 1.10,
+    'rural': 0.90,
+}
+
+# Maternal education distribution by SES (DHS patterns)
+EDUCATION_BY_SES = {
+    1: {'none': 0.50, 'primary': 0.35, 'secondary': 0.13, 'tertiary': 0.02},
+    2: {'none': 0.30, 'primary': 0.40, 'secondary': 0.25, 'tertiary': 0.05},
+    3: {'none': 0.15, 'primary': 0.35, 'secondary': 0.40, 'tertiary': 0.10},
+    4: {'none': 0.08, 'primary': 0.25, 'secondary': 0.47, 'tertiary': 0.20},
+    5: {'none': 0.03, 'primary': 0.12, 'secondary': 0.45, 'tertiary': 0.40},
+}
+
+
+# ============================================================
+# SECTION 2: Utility Functions
+# ============================================================
+
+def compute_individual_access(ses, region, education, distance, scenario_base, rng):
+    """Compute individual-level access probability from equity determinants."""
+    base = scenario_base
+    ses_mult = SES_COVERAGE_MULTIPLIER[ses]
+    edu_mult = EDUCATION_COVERAGE_MULTIPLIER[education]
+    reg_mult = REGION_COVERAGE_MULTIPLIER[region]
+    # Distance penalty: each km reduces probability slightly
+    dist_penalty = max(0, 1.0 - distance * 0.012)
+    # Individual random effect (unobserved factors)
+    individual_effect = rng.normal(1.0, 0.08)
+    p = base * ses_mult * edu_mult * reg_mult * dist_penalty * individual_effect
+    return np.clip(p, 0.01, 0.99)
+
+
+def vaccine_received(age_months, eligible_age_months, access_prob, prev_dose_received, rng):
+    """Determine if a vaccine dose was received given age, eligibility, access."""
+    if age_months < eligible_age_months:
+        return 0  # Too young
+    if not prev_dose_received:
+        return 0  # Can't get dose 3 without dose 2 (sequential)
+    # Timeliness decay: probability decreases if very late
+    months_since_eligible = age_months - eligible_age_months
+    timeliness_factor = 1.0 if months_since_eligible < 3 else 0.90
+    p = access_prob * timeliness_factor
+    return 1 if rng.random() < p else 0
+
+
+# ============================================================
+# SECTION 3: Main Generator
+# ============================================================
+
+def generate_immunisation_dataset(n=10000, seed=42, scenario='moderate_coverage'):
+    rng = np.random.default_rng(seed)
+    sc = SCENARIOS[scenario]
+
+    # ── Step 1: Sex ──
+    sex = rng.choice(['M', 'F'], size=n, p=[0.512, 0.488])
+
+    # ── Step 2: Age (months, 0-23) ──
+    age_months = rng.uniform(0, 23.9, n)
+    age_months = np.round(age_months, 1)
+
+    # ── Step 3: Region type ──
+    region_type = rng.choice(['urban', 'rural'], size=n,
+                              p=[sc['urban_pct'], 1 - sc['urban_pct']])
+
+    # ── Step 4: SES quintile ──
+    ses_quintile = rng.choice([1, 2, 3, 4, 5], size=n,
+                               p=[0.20, 0.20, 0.20, 0.20, 0.20])
+
+    # ── Step 5: Maternal education (conditional on SES) ──
+    maternal_education = np.empty(n, dtype=object)
+    for i in range(n):
+        dist = EDUCATION_BY_SES[ses_quintile[i]]
+        maternal_education[i] = rng.choice(
+            list(dist.keys()), p=list(dist.values()))
+
+    # ── Step 6: Distance to facility ──
+    distance_km = np.zeros(n)
+    for i in range(n):
+        if region_type[i] == 'urban':
+            distance_km[i] = rng.exponential(2.0)  # Mean 2km urban
+        else:
+            distance_km[i] = rng.exponential(8.0)  # Mean 8km rural
+    distance_km = np.clip(np.round(distance_km, 1), 0.1, 80.0)
+
+    # ── Step 7: Individual access probability ──
+    # Base coverage used as the "system" level factor
+    base_cov = (sc['bcg_coverage'] + sc['penta1_coverage'] + sc['mcv1_coverage']) / 3.0
+
+    access_prob = np.zeros(n)
+    for i in range(n):
+        access_prob[i] = compute_individual_access(
+            ses_quintile[i], region_type[i], maternal_education[i],
+            distance_km[i], base_cov, rng)
+
+    # ── Step 8: Vaccine doses ──
+    # Zero-dose children: some children never enter the system
+    is_zero_dose = rng.random(n) < (sc['zero_dose_rate'] / access_prob)
+    is_zero_dose = is_zero_dose & (rng.random(n) < 0.5)  # Soften to realistic rate
+
+    # BCG (birth dose)
+    bcg = np.zeros(n, dtype=int)
+    for i in range(n):
+        if is_zero_dose[i]:
+            continue
+        if age_months[i] >= 0:
+            bcg[i] = 1 if rng.random() < access_prob[i] * 1.05 else 0
+
+    # OPV (0, 1, 2, 3)
+    opv0 = np.zeros(n, dtype=int)
+    opv1 = np.zeros(n, dtype=int)
+    opv2 = np.zeros(n, dtype=int)
+    opv3 = np.zeros(n, dtype=int)
+    for i in range(n):
+        if is_zero_dose[i]:
+            continue
+        opv0[i] = vaccine_received(age_months[i], 0, access_prob[i] * 0.95, True, rng)
+        opv1[i] = vaccine_received(age_months[i], weeks_to_months(6),
+                                    access_prob[i], bool(opv0[i]) or rng.random() < 0.3, rng)
+        opv2[i] = vaccine_received(age_months[i], weeks_to_months(10),
+                                    access_prob[i] * 0.97, bool(opv1[i]), rng)
+        opv3[i] = vaccine_received(age_months[i], weeks_to_months(14),
+                                    access_prob[i] * 0.94, bool(opv2[i]), rng)
+
+    # Penta (1, 2, 3)
+    penta1 = np.zeros(n, dtype=int)
+    penta2 = np.zeros(n, dtype=int)
+    penta3 = np.zeros(n, dtype=int)
+    for i in range(n):
+        if is_zero_dose[i]:
+            continue
+        penta1[i] = vaccine_received(age_months[i], weeks_to_months(6),
+                                      access_prob[i], True, rng)
+        penta2[i] = vaccine_received(age_months[i], weeks_to_months(10),
+                                      access_prob[i] * 0.97, bool(penta1[i]), rng)
+        penta3[i] = vaccine_received(age_months[i], weeks_to_months(14),
+                                      access_prob[i] * 0.93, bool(penta2[i]), rng)
+
+    # PCV (1, 2, 3)
+    pcv1 = np.zeros(n, dtype=int)
+    pcv2 = np.zeros(n, dtype=int)
+    pcv3 = np.zeros(n, dtype=int)
+    for i in range(n):
+        if is_zero_dose[i]:
+            continue
+        pcv1[i] = vaccine_received(age_months[i], weeks_to_months(6),
+                                    access_prob[i], True, rng)
+        pcv2[i] = vaccine_received(age_months[i], weeks_to_months(10),
+                                    access_prob[i] * 0.97, bool(pcv1[i]), rng)
+        pcv3[i] = vaccine_received(age_months[i], weeks_to_months(14),
+                                    access_prob[i] * 0.93, bool(pcv2[i]), rng)
+
+    # Rotavirus (1, 2)
+    rota1 = np.zeros(n, dtype=int)
+    rota2 = np.zeros(n, dtype=int)
+    for i in range(n):
+        if is_zero_dose[i]:
+            continue
+        rota1[i] = vaccine_received(age_months[i], weeks_to_months(6),
+                                     access_prob[i], True, rng)
+        rota2[i] = vaccine_received(age_months[i], weeks_to_months(10),
+                                     access_prob[i] * 0.96, bool(rota1[i]), rng)
+
+    # IPV (1 dose at 14 weeks)
+    ipv1 = np.zeros(n, dtype=int)
+    for i in range(n):
+        if is_zero_dose[i]:
+            continue
+        ipv1[i] = vaccine_received(age_months[i], weeks_to_months(14),
+                                    access_prob[i], True, rng)
+
+    # Measles (MCV1 at 9mo, MCV2 at 15mo)
+    mcv1 = np.zeros(n, dtype=int)
+    mcv2 = np.zeros(n, dtype=int)
+    for i in range(n):
+        if is_zero_dose[i]:
+            continue
+        mcv1[i] = vaccine_received(age_months[i], 9.0,
+                                    access_prob[i] * 0.95, True, rng)
+        mcv2[i] = vaccine_received(age_months[i], 15.0,
+                                    access_prob[i] * 0.85, bool(mcv1[i]), rng)
+
+    # ── Step 9: Derived indicators ──
+    # Total doses received (out of basic schedule: BCG, Penta1-3, OPV1-3, MCV1 = 8)
+    total_basic_doses = bcg + penta1 + penta2 + penta3 + opv1 + opv2 + opv3 + mcv1
+
+    # Fully immunised for age (all age-appropriate vaccines received)
+    fully_immunised = np.zeros(n, dtype=int)
+    for i in range(n):
+        if age_months[i] < weeks_to_months(6):
+            # Only BCG expected
+            fully_immunised[i] = int(bcg[i] == 1)
+        elif age_months[i] < weeks_to_months(14):
+            # BCG + first round (Penta1, OPV1, PCV1, Rota1)
+            fully_immunised[i] = int(bcg[i] and penta1[i] and opv1[i])
+        elif age_months[i] < 9:
+            # All primary series
+            fully_immunised[i] = int(bcg[i] and penta3[i] and opv3[i] and pcv3[i])
+        elif age_months[i] < 15:
+            # Primary + MCV1
+            fully_immunised[i] = int(bcg[i] and penta3[i] and opv3[i] and mcv1[i])
+        else:
+            # Full schedule including MCV2
+            fully_immunised[i] = int(bcg[i] and penta3[i] and opv3[i] and mcv1[i] and mcv2[i])
+
+    # Dropout: Penta1 to Penta3
+    dropout_penta13 = np.zeros(n, dtype=int)
+    for i in range(n):
+        if penta1[i] == 1 and penta3[i] == 0 and age_months[i] >= weeks_to_months(14):
+            dropout_penta13[i] = 1
+
+    # Dropout: Penta1 to MCV1
+    dropout_penta1_mcv1 = np.zeros(n, dtype=int)
+    for i in range(n):
+        if penta1[i] == 1 and mcv1[i] == 0 and age_months[i] >= 9:
+            dropout_penta1_mcv1[i] = 1
+
+    # Zero-dose: no vaccines at all despite being old enough for BCG
+    zero_dose = ((bcg + opv0 + penta1 + pcv1 + rota1) == 0).astype(int)
+
+    # Immunisation status category
+    imm_status = np.where(
+        zero_dose == 1, 'zero_dose',
+        np.where(fully_immunised == 1, 'fully_immunised',
+                 'partially_immunised'))
+
+    # ── Assemble DataFrame ──
+    df = pd.DataFrame({
+        'id': np.arange(1, n + 1),
+        'sex': sex,
+        'age_months': age_months,
+        'region_type': region_type,
+        'ses_quintile': ses_quintile,
+        'maternal_education': maternal_education,
+        'distance_to_facility_km': distance_km,
+        'bcg': bcg,
+        'opv0': opv0, 'opv1': opv1, 'opv2': opv2, 'opv3': opv3,
+        'penta1': penta1, 'penta2': penta2, 'penta3': penta3,
+        'pcv1': pcv1, 'pcv2': pcv2, 'pcv3': pcv3,
+        'rota1': rota1, 'rota2': rota2,
+        'ipv1': ipv1,
+        'mcv1': mcv1, 'mcv2': mcv2,
+        'total_basic_doses': total_basic_doses,
+        'fully_immunised': fully_immunised,
+        'dropout_penta1_penta3': dropout_penta13,
+        'dropout_penta1_mcv1': dropout_penta1_mcv1,
+        'zero_dose': zero_dose,
+        'immunisation_status': imm_status,
+    })
+
+    # ── Print summary ──
+    # Filter to age-eligible for meaningful stats
+    elig_penta = df[df['age_months'] >= weeks_to_months(14) + 1]
+    elig_mcv1 = df[df['age_months'] >= 10]
+    elig_mcv2 = df[df['age_months'] >= 16]
+
+    print(f"\n{'='*60}")
+    print(f"Childhood Immunisation — {scenario} (n={n}, seed={seed})")
+    print(f"{'='*60}")
+    print(f"\nCoverage (age-eligible children):")
+    print(f"  BCG:    {df[df['age_months']>=1]['bcg'].mean()*100:.1f}%")
+    if len(elig_penta) > 0:
+        print(f"  Penta1: {elig_penta['penta1'].mean()*100:.1f}%")
+        print(f"  Penta3: {elig_penta['penta3'].mean()*100:.1f}%")
+    if len(elig_mcv1) > 0:
+        print(f"  MCV1:   {elig_mcv1['mcv1'].mean()*100:.1f}%")
+    if len(elig_mcv2) > 0:
+        print(f"  MCV2:   {elig_mcv2['mcv2'].mean()*100:.1f}%")
+    print(f"\nDropout (Penta1→Penta3): "
+          f"{elig_penta['dropout_penta1_penta3'].mean()*100:.1f}%" if len(elig_penta) > 0 else "")
+    print(f"Zero-dose (no vaccines, age≥1mo): "
+          f"{df[df['age_months']>=1]['zero_dose'].mean()*100:.1f}%")
+    print(f"Fully immunised for age: {df['fully_immunised'].mean()*100:.1f}%")
+
+    # Equity: coverage by SES quintile
+    if len(elig_penta) > 0:
+        print(f"\nPenta3 by SES quintile:")
+        for q in range(1, 6):
+            sub = elig_penta[elig_penta['ses_quintile'] == q]
+            if len(sub) > 0:
+                print(f"  Q{q}: {sub['penta3'].mean()*100:.1f}%")
+
+    return df
+
+
+# ============================================================
+# SECTION 4: CLI Entry Point
+# ============================================================
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description='Generate synthetic childhood immunisation dataset')
+    parser.add_argument('--scenario', type=str, default='moderate_coverage',
+                        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_immunisation_dataset(n=args.n, seed=args.seed, scenario=sc_name)
+            out = os.path.join('data', f'immunisation_{sc_name}.csv')
+            df.to_csv(out, index=False)
+            print(f"  → Saved to {out}\n")
+    else:
+        df = generate_immunisation_dataset(n=args.n, seed=args.seed, scenario=args.scenario)
+        out = args.output or os.path.join('data', f'immunisation_{args.scenario}.csv')
+        df.to_csv(out, index=False)
+        print(f"  → Saved to {out}")

requirements.txt

@@ -0,0 +1,3 @@
+numpy>=1.24
+pandas>=2.0
+matplotlib>=3.7

validate_dataset.py

@@ -0,0 +1,171 @@
+#!/usr/bin/env python3
+"""Validation & Diagnostic Visualization for Childhood Immunisation Dataset."""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+SCENARIOS = ['high_coverage', 'moderate_coverage', 'low_coverage']
+
+
+def load_scenarios(data_dir='data'):
+    dfs = {}
+    for sc in SCENARIOS:
+        path = os.path.join(data_dir, f'immunisation_{sc}.csv')
+        if os.path.exists(path):
+            dfs[sc] = pd.read_csv(path)
+    return dfs
+
+
+def make_report(dfs, output='validation_report.png'):
+    fig, axes = plt.subplots(4, 2, figsize=(16, 22))
+    fig.suptitle('Childhood Immunisation Coverage — Validation Report',
+                 fontsize=16, fontweight='bold', y=0.98)
+
+    df = dfs.get('moderate_coverage', list(dfs.values())[0])
+    elig = df[df['age_months'] >= 4]  # Old enough for primary series
+
+    # Panel 1: Immunisation status
+    ax = axes[0, 0]
+    status_counts = df['immunisation_status'].value_counts()
+    colors = {'fully_immunised': '#2ecc71', 'partially_immunised': '#f39c12',
+              'zero_dose': '#e74c3c'}
+    order = ['fully_immunised', 'partially_immunised', 'zero_dose']
+    vals = [status_counts.get(s, 0) for s in order]
+    ax.bar(range(3), vals, color=[colors[s] for s in order])
+    ax.set_xticks(range(3))
+    ax.set_xticklabels(['Fully\nImmunised', 'Partially\nImmunised', 'Zero\nDose'])
+    for i, v in enumerate(vals):
+        ax.text(i, v + 50, f'{v/len(df)*100:.1f}%', ha='center', fontsize=10)
+    ax.set_ylabel('Count')
+    ax.set_title('Immunisation Status (Moderate Coverage)')
+
+    # Panel 2: Vaccine coverage cascade
+    ax = axes[0, 1]
+    vaccines = ['bcg', 'penta1', 'penta2', 'penta3', 'mcv1', 'mcv2']
+    elig_ages = [1, 2, 3, 4, 10, 16]
+    coverages = []
+    for v, min_age in zip(vaccines, elig_ages):
+        sub = df[df['age_months'] >= min_age]
+        coverages.append(sub[v].mean() * 100 if len(sub) > 0 else 0)
+    bars = ax.bar(range(len(vaccines)), coverages, color='#3498db', alpha=0.8)
+    ax.set_xticks(range(len(vaccines)))
+    ax.set_xticklabels([v.upper() for v in vaccines])
+    for i, v in enumerate(coverages):
+        ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=9)
+    ax.set_ylabel('Coverage (%)')
+    ax.set_title('Vaccine Coverage Cascade (age-eligible)')
+    ax.set_ylim(0, 105)
+
+    # Panel 3: Penta3 coverage by SES quintile across scenarios
+    ax = axes[1, 0]
+    x = np.arange(5)
+    width = 0.25
+    for i, sc in enumerate(SCENARIOS):
+        if sc not in dfs:
+            continue
+        d = dfs[sc]
+        elig_d = d[d['age_months'] >= 4]
+        rates = []
+        for q in range(1, 6):
+            sub = elig_d[elig_d['ses_quintile'] == q]
+            rates.append(sub['penta3'].mean() * 100 if len(sub) > 0 else 0)
+        ax.bar(x + i * width, rates, width, label=sc.replace('_', ' ').title(),
+               alpha=0.8)
+    ax.set_xticks(x + width)
+    ax.set_xticklabels([f'Q{q}' for q in range(1, 6)])
+    ax.set_ylabel('Penta3 Coverage (%)')
+    ax.set_title('Penta3 Coverage by Wealth Quintile')
+    ax.legend(fontsize=8)
+
+    # Panel 4: Coverage by urban/rural
+    ax = axes[1, 1]
+    for rt in ['urban', 'rural']:
+        sub = elig[elig['region_type'] == rt]
+        if len(sub) == 0:
+            continue
+        covs = [sub[v].mean() * 100 for v in ['bcg', 'penta1', 'penta3', 'mcv1']]
+        ax.plot(['BCG', 'Penta1', 'Penta3', 'MCV1'], covs,
+                'o-', label=rt.title(), linewidth=2, markersize=8)
+    ax.set_ylabel('Coverage (%)')
+    ax.set_title('Coverage by Urban/Rural')
+    ax.legend(fontsize=10)
+    ax.set_ylim(0, 100)
+
+    # Panel 5: Distance vs total doses
+    ax = axes[2, 0]
+    sample = df.sample(min(3000, len(df)), random_state=42)
+    ax.scatter(sample['distance_to_facility_km'], sample['total_basic_doses'],
+              alpha=0.3, s=8, c='#3498db')
+    ax.set_xlabel('Distance to Facility (km)')
+    ax.set_ylabel('Total Basic Doses Received')
+    ax.set_title('Distance vs Doses Received')
+
+    # Panel 6: Cross-scenario zero-dose and fully immunised
+    ax = axes[2, 1]
+    metrics = ['zero_dose', 'fully_immunised']
+    x = np.arange(len(SCENARIOS))
+    width = 0.35
+    for i, m in enumerate(metrics):
+        rates = []
+        for sc in SCENARIOS:
+            if sc in dfs:
+                d = dfs[sc]
+                rates.append(d[d['age_months'] >= 1][m].mean() * 100)
+            else:
+                rates.append(0)
+        color = '#e74c3c' if m == 'zero_dose' else '#2ecc71'
+        ax.bar(x + i * width, rates, width, label=m.replace('_', ' ').title(),
+               color=color, alpha=0.8)
+    ax.set_xticks(x + width / 2)
+    ax.set_xticklabels([s.replace('_', '\n').title() for s in SCENARIOS], fontsize=8)
+    ax.set_ylabel('%')
+    ax.set_title('Zero-Dose & Fully Immunised Across Scenarios')
+    ax.legend(fontsize=9)
+
+    # Panel 7: Dropout rates across scenarios
+    ax = axes[3, 0]
+    for sc in SCENARIOS:
+        if sc not in dfs:
+            continue
+        d = dfs[sc]
+        elig_d = d[d['age_months'] >= 4]
+        if len(elig_d) == 0:
+            continue
+        p13 = elig_d['dropout_penta1_penta3'].mean() * 100
+        elig_mcv = d[d['age_months'] >= 10]
+        pm = elig_mcv['dropout_penta1_mcv1'].mean() * 100 if len(elig_mcv) > 0 else 0
+        ax.bar([f'{sc.replace("_", chr(10)).title()}\nPenta1→3',
+                f'{sc.replace("_", chr(10)).title()}\nPenta1→MCV1'],
+               [p13, pm], alpha=0.7)
+    ax.set_ylabel('Dropout Rate (%)')
+    ax.set_title('Dropout Rates')
+
+    # Panel 8: Coverage by maternal education
+    ax = axes[3, 1]
+    edu_order = ['none', 'primary', 'secondary', 'tertiary']
+    for v, color in [('penta3', '#3498db'), ('mcv1', '#e74c3c')]:
+        covs = []
+        for edu in edu_order:
+            sub = elig[elig['maternal_education'] == edu]
+            covs.append(sub[v].mean() * 100 if len(sub) > 0 else 0)
+        ax.plot(edu_order, covs, 'o-', label=v.upper(), linewidth=2,
+                markersize=8, color=color)
+    ax.set_xlabel('Maternal Education')
+    ax.set_ylabel('Coverage (%)')
+    ax.set_title('Coverage by Maternal Education')
+    ax.legend(fontsize=10)
+
+    plt.tight_layout(rect=[0, 0, 1, 0.97])
+    plt.savefig(output, dpi=150, bbox_inches='tight')
+    print(f'Saved validation report to {output}')
+    plt.close()
+
+
+if __name__ == '__main__':
+    dfs = load_scenarios()
+    if not dfs:
+        print('No data files found in data/')
+    else:
+        make_report(dfs)