---
license: cc-by-4.0
task_categories:
- tabular-classification
language:
- en
tags:
- laboratory
- HIV
- viral-load
- CD4
- ART-monitoring
- synthetic
- sub-saharan-africa
pretty_name: HIV Viral Load & CD4 Testing
size_categories:
- 10K<n<100K
configs:
- config_name: vl_accessible
  data_files: data/hiv_vl_cd4_vl_accessible.csv
- config_name: vl_limited
  data_files: data/hiv_vl_cd4_vl_limited.csv
  default: true
- config_name: no_vl_access
  data_files: data/hiv_vl_cd4_no_vl_access.csv
data_type: synthetic
---

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

# HIV Viral Load & CD4 Testing

## Abstract

Synthetic dataset modeling HIV viral load and CD4 laboratory services across three SSA scenarios. Captures VL/CD4 test availability, platforms (conventional/POC/DBS), results, turnaround times, clinical actions (EAC, regimen switch), and quality indicators. Parameterized from WHO/UNAIDS guidelines and SSA implementation research.

## Parameterization Evidence

| Parameter | Value | Source | Year |
| --- | --- | --- | --- |
| VL coverage SSA | 17% to 71% (2015-2019) | Lecher et al. MMWR | 2021 |
| VL suppression on ART | 76% SSA | UNAIDS | 2023 |
| Conventional VL TAT | 2-6 weeks | Jani et al. Lancet HIV | 2016 |
| POC VL TAT | 1-3 hours | Jani et al. | 2016 |
| SSA PLHIV on ART | 21M / 25.6M | UNAIDS | 2023 |

## Validation

![Validation Report](validation_report.png)

## Usage

```python
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/hiv-viral-load-cd4", name="vl_limited")
df = ds['train'].to_pandas()
```

## References

1. WHO (2023). HIV treatment guidelines
2. UNAIDS (2023). Global HIV statistics
3. Lecher SL et al. (2021). VL monitoring scale-up. *MMWR*. DOI: 10.15585/mmwr.mm7034a2
4. Jani IV et al. (2016). POC CD4 and VL. *Lancet HIV*. DOI: 10.1016/S2352-3018(15)00236-X

## License

CC-BY-4.0