---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- disease
- health
- humanitarian-needs-overview-hno
- cod
pretty_name: "Ebola UNICEF EOC - Psychosocial - Mai 2019 Dataset"
dataset_info:
  splits:
    - name: train
      num_examples: 205
    - name: test
      num_examples: 51
---

# Ebola UNICEF EOC - Psychosocial - Mai 2019 Dataset

**Publisher:** UNICEF West and Central Africa (WCARO) (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/unicef-eoc-psychosocial-mai-2019-dataset) · **License:** `cc-by` · **Updated:** 2024-04-12

---

## Abstract

Indicators used in Psychosocial pillar for Ebola response

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the `date_de_début`, `date_de_fin` column(s). Geographic scope: **COD**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Time-series observations |
| **Rows (total)** | 257 |
| **Columns** | 39 (33 numeric, 4 categorical, 2 datetime) |
| **Train split** | 205 rows |
| **Test split** | 51 rows |
| **Geographic scope** | COD |
| **Publisher** | UNICEF West and Central Africa (WCARO) (inactive) |
| **HDX last updated** | 2024-04-12 |

---

## Variables

**Geographic** — `zone_de_santé_2015` (BUNIA, KOMANDA, MABALAKO), `1_1_soutien_psychologique_aux_nouveaux_cas_suspects_dont_hommes` (range 0.0–11.0), `2_1_suivi_psychologique_des_cas_suspects_dont_hommes` (range 0.0–33.0), `5_1_soutien_psychologique_aux_nouveaux_cas_confirmés_dont_hommes` (range 0.0–8.0), `6_1_suivi_psychologique_des_cas_confirmés_dont_hommes` (range 0.0–21.0) and 3 others.

**Temporal** — `date_de_début`, `date_de_fin`.

**Outcome / Measurement** — `total` (range 0.0–36.0), `total_1` (range 0.0–114.0), `total_2` (range 0.0–15.0), `total_3` (range 0.0–37.0), `total_4` (range 0.0–5.0) and 3 others.

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-17).

**Other** — `partenaire` (DRC, DIVAS, Gouvernement), `1_2_dont_femme` (range 0.0–11.0), `1_3_dont_garçons` (range 0.0–10.0), `1_4_dont_filles` (range 0.0–9.0), `2_2_dont_femmes` (range 0.0–45.0) and 14 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-unicef-eoc-psychosocial-mai-2019-dataset")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `date_de_début` | datetime64[ns] | 0.0% |  |
| `date_de_fin` | datetime64[ns] | 0.0% |  |
| `partenaire` | object | 0.0% | DRC, DIVAS, Gouvernement |
| `zone_de_santé_2015` | object | 0.0% | BUNIA, KOMANDA, MABALAKO |
| `1_1_soutien_psychologique_aux_nouveaux_cas_suspects_dont_hommes` | float64 | 37.7% | 0.0 – 11.0 (mean 2.5812) |
| `1_2_dont_femme` | float64 | 37.4% | 0.0 – 11.0 (mean 2.677) |
| `1_3_dont_garçons` | float64 | 45.1% | 0.0 – 10.0 (mean 2.1064) |
| `1_4_dont_filles` | float64 | 46.3% | 0.0 – 9.0 (mean 1.9348) |
| `total` | float64 | 18.3% | 0.0 – 36.0 (mean 6.7048) |
| `2_1_suivi_psychologique_des_cas_suspects_dont_hommes` | float64 | 24.9% | 0.0 – 33.0 (mean 4.9378) |
| `2_2_dont_femmes` | float64 | 21.8% | 0.0 – 45.0 (mean 5.6965) |
| `2_3_dont_garçons` | float64 | 28.8% | 0.0 – 35.0 (mean 4.1421) |
| `2_4_dont_filles` | float64 | 32.3% | 0.0 – 31.0 (mean 4.6149) |
| `total_1` | float64 | 15.6% | 0.0 – 114.0 (mean 16.8618) |
| `5_1_soutien_psychologique_aux_nouveaux_cas_confirmés_dont_hommes` | float64 | 72.4% | 0.0 – 8.0 (mean 0.9718) |
| `5_2_dont_femmes` | float64 | 71.2% | 0.0 – 10.0 (mean 1.1351) |
| `5_4_dont_filles` | float64 | 79.8% | 0.0 – 4.0 (mean 0.6923) |
| `total_2` | float64 | 62.3% | 0.0 – 15.0 (mean 2.134) |
| `6_1_suivi_psychologique_des_cas_confirmés_dont_hommes` | float64 | 53.3% | 0.0 – 21.0 (mean 4.7833) |
| `6_2_dont_femmes` | float64 | 54.5% | 0.0 – 20.0 (mean 6.4103) |
| `6_3_dont_garçons` | float64 | 58.4% | 0.0 – 6.0 (mean 1.8037) |
| `6_4_dont_filles` | float64 | 57.2% | 0.0 – 5.0 (mean 1.7909) |
| `total_3` | float64 | 52.1% | 0.0 – 37.0 (mean 13.935) |
| `total_4` | float64 | 78.6% | 0.0 – 5.0 (mean 1.1818) |
| `8_1_suivi_psychologique_des_anciens_déchargés_guéris_dont_hommes` | float64 | 70.8% |  |
| `8_2_dont_femmes` | float64 | 74.3% |  |
| `total_5` | float64 | 63.8% |  |
| `3_1_nouveaux_déchargés_non_cas_recevant_un_soutien_psychosocial_dont_hommes` | float64 | 46.7% |  |
| `3_2_dont_femmes` | float64 | 49.4% |  |
| `3_3_dont_garçons` | float64 | 54.5% |  |
| `3_4_dont_filles` | float64 | 55.3% |  |
| `total_6` | float64 | 31.1% |  |
| `4_1_suivi_psychologique_de_anciens_déchargés_non_cas_dont_hommes` | float64 | 39.7% |  |
| `4_2_dont_femmes` | float64 | 39.7% |  |
| `4_3_dont_garçons` | float64 | 47.5% |  |
| `4_4_dont_filles` | float64 | 49.0% |  |
| `total_7` | float64 | 33.1% |  |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-17 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `1_1_soutien_psychologique_aux_nouveaux_cas_suspects_dont_hommes` | 0.0 | 11.0 | 2.5812 | 2.0 |
| `1_2_dont_femme` | 0.0 | 11.0 | 2.677 | 2.0 |
| `1_3_dont_garçons` | 0.0 | 10.0 | 2.1064 | 1.0 |
| `1_4_dont_filles` | 0.0 | 9.0 | 1.9348 | 1.0 |
| `total` | 0.0 | 36.0 | 6.7048 | 4.0 |
| `2_1_suivi_psychologique_des_cas_suspects_dont_hommes` | 0.0 | 33.0 | 4.9378 | 3.0 |
| `2_2_dont_femmes` | 0.0 | 45.0 | 5.6965 | 3.0 |
| `2_3_dont_garçons` | 0.0 | 35.0 | 4.1421 | 3.0 |
| `2_4_dont_filles` | 0.0 | 31.0 | 4.6149 | 3.0 |
| `total_1` | 0.0 | 114.0 | 16.8618 | 9.0 |
| `5_1_soutien_psychologique_aux_nouveaux_cas_confirmés_dont_hommes` | 0.0 | 8.0 | 0.9718 | 1.0 |
| `5_2_dont_femmes` | 0.0 | 10.0 | 1.1351 | 1.0 |
| `5_4_dont_filles` | 0.0 | 4.0 | 0.6923 | 0.0 |
| `total_2` | 0.0 | 15.0 | 2.134 | 1.0 |
| `6_1_suivi_psychologique_des_cas_confirmés_dont_hommes` | 0.0 | 21.0 | 4.7833 | 4.0 |

---

## Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 7 column(s) with >80% missing values were removed: `5_3_dont_garçons`, `7_1_nouveaux_déchargés_guéris_dont_hommes`, `7_2_dont_femmes`, `7_3_dont_garçons`, `7_4_dont_filles`, `8_3_dont_garçons`.... The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

---

## Limitations

- Data originates from UNICEF West and Central Africa (WCARO) (inactive) and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `1_1_soutien_psychologique_aux_nouveaux_cas_suspects_dont_hommes`, `1_2_dont_femme`, `1_3_dont_garçons`, `1_4_dont_filles`, `2_1_suivi_psychologique_des_cas_suspects_dont_hommes`, `2_2_dont_femmes`, `2_3_dont_garçons`, `2_4_dont_filles`....
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/unicef-eoc-psychosocial-mai-2019-dataset) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_unicef_eoc_psychosocial_mai_2019_dataset,
  title     = {Ebola UNICEF EOC - Psychosocial - Mai 2019 Dataset},
  author    = {UNICEF West and Central Africa (WCARO) (inactive)},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/unicef-eoc-psychosocial-mai-2019-dataset},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*