---
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
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- children
- covid-19
- eastern-africa
- funding
- ago
- eth
- ken
- moz
- rwa
pretty_name: "Eastern and Southern Africa COVID-19 - UNICEF - Situation and Response"
dataset_info:
  splits:
    - name: train
      num_examples: 216
    - name: test
      num_examples: 54
---

# Eastern and Southern Africa COVID-19 - UNICEF - Situation and Response

**Publisher:** UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/eastern-and-southern-africa-covid-19-unicef-situation-and-response) · **License:** `cc-by` · **Updated:** 2024-08-30

---

## Abstract

COVID-19 Situation and Response Dashboard for Annual 2020

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2024-08-30. Geographic scope: **AGO, ETH, KEN, MOZ, RWA, SOM, ZAF, SSD, and 1 others**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 270 |
| **Columns** | 9 (3 numeric, 6 categorical, 0 datetime) |
| **Train split** | 216 rows |
| **Test split** | 54 rows |
| **Geographic scope** | AGO, ETH, KEN, MOZ, RWA, SOM, ZAF, SSD, and 1 others |
| **Publisher** | UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive) |
| **HDX last updated** | 2024-08-30 |

---

## Variables

**Geographic** — `applied_filters_sitrep_r_is_13_business_area_region_is_esar` (Zimbabwe, Madagascar, Comoros).

**Identifier / Metadata** — `unnamed_1` (CV-04 - Access to continuous education, child protection and GBV services, CV-01 - Risk Communication and Community Engagement (RCCE), CV-03 - Continuity of health care for women and children), `unnamed_2` (1: Number of people reached on COVID-19 through MESSAGING ON PREVENTION AND ACCESS TO SERVICES, 1: Number of children supported with DISTANCE/HOME-BASED LEARNING, 2: Number of people engaged on COVID-19 through RCCE ACTIONS), `unnamed_3` (range 0.0–40500000.0), `unnamed_4` (range 0.0–53200000.0), `unnamed_5` (range 0.0–39.5) and 3 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-eastern-and-southern-africa-covid-19-unicef-situation-and-response")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `applied_filters_sitrep_r_is_13_business_area_region_is_esar` | object | 0.4% | Zimbabwe, Madagascar, Comoros |
| `unnamed_1` | object | 0.4% | CV-04 - Access to continuous education, child protection and GBV services, CV-01 - Risk Communication and Community Engagement (RCCE), CV-03 - Continuity of health care for women and children |
| `unnamed_2` | object | 0.4% | 1: Number of people reached on COVID-19 through MESSAGING ON PREVENTION AND ACCESS TO SERVICES, 1: Number of children supported with DISTANCE/HOME-BASED LEARNING, 2: Number of people engaged on COVID-19 through RCCE ACTIONS |
| `unnamed_3` | float64 | 2.6% | 0.0 – 40500000.0 (mean 1204298.5665) |
| `unnamed_4` | float64 | 4.8% | 0.0 – 53200000.0 (mean 1532827.0428) |
| `unnamed_5` | float64 | 7.0% | 0.0 – 39.5 (mean 2.186) |
| `unnamed_6` | object | 0.4% | SitRep 13, SitRep round |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-17 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `unnamed_3` | 0.0 | 40500000.0 | 1204298.5665 | 60000.0 |
| `unnamed_4` | 0.0 | 53200000.0 | 1532827.0428 | 33164.0 |
| `unnamed_5` | 0.0 | 39.5 | 2.186 | 1.0 |

---

## 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`. 3 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 Eastern and Southern Africa Regional Office (ESARO) (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.
- This dataset spans 9 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/eastern-and-southern-africa-covid-19-unicef-situation-and-response) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_eastern_and_southern_africa_covid_19_unicef_situation_and_response,
  title     = {Eastern and Southern Africa COVID-19 - UNICEF - Situation and Response},
  author    = {UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/eastern-and-southern-africa-covid-19-unicef-situation-and-response},
  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.*