electricsheepafrica/africa-ebola-all

Ebola in Sierra Leone

Publisher: Code for Africa · Source: OpenAfrica · License: cc-by · Updated: 2023-11-30

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Abstract

A collection of datasets detailing ebola statistics in Sierra Leone, West Africa and Globally from 2014 to present day

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the date column(s). Geographic scope: Africa (multiple countries).

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	915
Columns	6 (1 numeric, 4 categorical, 1 datetime)
Train split	732 rows
Test split	183 rows
Geographic scope	Africa (multiple countries)
Publisher	Code for Africa
OpenAfrica last updated	2023-11-30

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Variables

Geographic — country (Sierra Leone).

Temporal — date.

Outcome / Measurement — value (range 0.0–11866.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-27).

Other — indicator (Cumulative number of confirmed Ebola cases, Cumulative number of suspected Ebola cases, Cumulative number of confirmed, probable and suspected Ebola cases).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ebola-all")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
indicator	object	0.0%	Cumulative number of confirmed Ebola cases, Cumulative number of suspected Ebola cases, Cumulative number of confirmed, probable and suspected Ebola cases
country	object	0.0%	Sierra Leone
date	datetime64[ns]	0.0%
value	int64	0.0%	0.0 – 11866.0 (mean 2296.7956)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-27

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Numeric Summary

Column	Min	Max	Mean	Median
value	0.0	11866.0	2296.7956	622.0

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Curation

Raw data was downloaded from OpenAfrica 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. 1 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.

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Limitations

• Data originates from Code for Africa and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{openafrica_africa_ebola_all,
  title     = {Ebola in Sierra Leone},
  author    = {Code for Africa},
  year      = {2023},
  url       = {https://open.africa/dataset/ebola-in-sierra-leone},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.