electricsheepafrica/africa-ebola-west-africa-ebola-treatment-centres-isolation-wards-hospitals-and-transit-centres

--- 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-facilities - hxl - gin - lbr - nga - sle pretty_name: "Ebola - West Africa - Ebola Treatment Centres, Isolation Wards Hospitals and Transit Centres" dataset_info: splits: - name: train num_examples: 70 - name: test num_examples: 17 --- # Ebola - West Africa - Ebola Treatment Centres, Isolation Wards Hospitals and Transit Centres **Publisher:** British Red Cross Maps Team · **Source:** [HDX](https://data.humdata.org/dataset/ebola-west-africa-ebola-treatment-centres-isolation-wards-hospitals-and-transit-centres) · **License:** `cc-by` · **Updated:** 2026-01-09 --- ## Abstract This data set contains information about to the Ebola treatment centres, Isolation centres and transit centres in regards to the West Africa Ebola outbreak. **The dataset is updated daily. ** Each row in this dataset represents tabular records. Temporal coverage is indicated by the `unnamed_18`, `unnamed_20` column(s). Geographic scope: **GIN, LBR, NGA, SLE**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Public health | | **Unit of observation** | Tabular records | | **Rows (total)** | 88 | | **Columns** | 25 (3 numeric, 20 categorical, 2 datetime) | | **Train split** | 70 rows | | **Test split** | 17 rows | | **Geographic scope** | GIN, LBR, NGA, SLE | | **Publisher** | British Red Cross Maps Team | | **HDX last updated** | 2026-01-09 | --- ## Variables **Geographic** — `please_see_https_data_hdx_rwlabs_org_dataset_ebola_treatment_centers` (Pending, Functional, CLOSED). **Identifier / Metadata** — `unnamed_1` (SieKer, ID [left(B5,3)&left(E5,3)], SieKai), `unnamed_2` (Liberia, Sierra Leone, Guinea), `unnamed_3` (Montserrado, Western, Northern), `unnamed_4` (LR11, SL04, SL02), `unnamed_5` (Western Area Rural, Port Loko, Western Area Urban) and 19 others. --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-ebola-west-africa-ebola-treatment-centres-isolation-wards-hospitals-and-transit-centres") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `please_see_https_data_hdx_rwlabs_org_dataset_ebola_treatment_centers` | object | 3.4% | Pending, Functional, CLOSED | | `unnamed_1` | object | 35.2% | SieKer, ID [left(B5,3)&left(E5,3)], SieKai | | `unnamed_2` | object | 12.5% | Liberia, Sierra Leone, Guinea | | `unnamed_3` | object | 12.5% | Montserrado, Western, Northern | | `unnamed_4` | object | 12.5% | LR11, SL04, SL02 | | `unnamed_5` | object | 42.0% | Western Area Rural, Port Loko, Western Area Urban | | `unnamed_6` | object | 59.1% | SL0401, SL0204, SL0402 | | `unnamed_7` | object | 31.8% | Monrovia, Foya, Kenema | | `unnamed_8` | object | 18.2% | Centre Name, #loc, Rural Kenema Field Hospital ETC | | `unnamed_9` | object | 23.9% | USAID Contract, MSF, IOM | | `unnamed_10` | object | 65.9% | | | `unnamed_13` | object | 8.0% | | | `unnamed_14` | object | 73.9% | | | `unnamed_15` | object | 37.5% | | | `unnamed_16` | float64 | 23.9% | 2.0 – 200.0 (mean 70.8806) | | `unnamed_17` | object | 13.6% | | | `unnamed_18` | datetime64[ns] | 14.8% | | | `unnamed_19` | object | 12.5% | | | `unnamed_20` | datetime64[ns] | 56.8% | | | `unnamed_22` | object | 20.5% | | | `unnamed_23` | float64 | 23.9% | 4.4239 – 10.9115 (mean 7.5628) | | `unnamed_24` | float64 | 23.9% | -13.6832 – -7.6802 (mean -10.9626) | | `unnamed_25` | object | 21.6% | | | `esa_source` | object | 0.0% | | | `esa_processed` | object | 0.0% | | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `unnamed_16` | 2.0 | 200.0 | 70.8806 | 62.0 | | `unnamed_23` | 4.4239 | 10.9115 | 7.5628 | 7.8752 | | `unnamed_24` | -13.6832 | -7.6802 | -10.9626 | -10.7342 | --- ## 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`. 4 column(s) with >80% missing values were removed: `unnamed_11`, `unnamed_12`, `unnamed_21`, `unnamed_26`. 5 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 British Red Cross Maps Team 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: `unnamed_1`, `unnamed_5`, `unnamed_6`, `unnamed_7`, `unnamed_9`, `unnamed_10`, `unnamed_14`, `unnamed_15`.... - This dataset spans 4 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/ebola-west-africa-ebola-treatment-centres-isolation-wards-hospitals-and-transit-centres) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_ebola_west_africa_ebola_treatment_centres_isolation_wards_hospitals_and_transit_centres, title = {Ebola - West Africa - Ebola Treatment Centres, Isolation Wards Hospitals and Transit Centres}, author = {British Red Cross Maps Team}, year = {2026}, url = {https://data.humdata.org/dataset/ebola-west-africa-ebola-treatment-centres-isolation-wards-hospitals-and-transit-centres}, 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.*