---
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
data_type: synthetic
---

> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

# 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/)