electricsheepasia/asia-covid-19-covid19-humanitarian-access

COVID19 Humanitarian Access Constraints, Impacts and Mitigation

Publisher: OCHA HQ · Source: HDX · License: cc-by-igo · Updated: 2026-04-27

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Abstract

This dataset contains scores for humanitarian access constraints into country, constraints within country, impacts the constraints have led to as well as the mitigation strategies in place to limit the impact.

The scores have the following interpretations: 0 = NA, 1 = No or open, 2 = partially open/closed, 3 = Yes or closed

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the date column(s). Geographic scope: AFG, BDI, CMR, CAF, TCD, COL, PRK, COD, and 20 others.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	29
Columns	7 (0 numeric, 6 categorical, 1 datetime)
Train split	23 rows
Test split	5 rows
Geographic scope	AFG, BDI, CMR, CAF, TCD, COL, PRK, COD, and 20 others
Publisher	OCHA HQ
HDX last updated	2026-04-27

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Variables

Geographic — region (West and Central Africa, Middle East and North Africa, Southern and Eastern Africa), admin0_code (AF, BDI, CD), country (Afghanistan, Burundi, Democratic Republic of the Congo).

Temporal — date.

Identifier / Metadata — esa_source (HDX), esa_processed (2026-05-06).

Other — mitigation_ind (yes, no).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-covid-19-covid19-humanitarian-access")
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
date	datetime64[ns]	0.0%
region	object	0.0%	West and Central Africa, Middle East and North Africa, Southern and Eastern Africa
admin0_code	object	0.0%	AF, BDI, CD
country	object	0.0%	Afghanistan, Burundi, Democratic Republic of the Congo
mitigation_ind	object	0.0%	yes, no
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-05-06

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Numeric Summary

Column	Min	Max	Mean	Median
No numeric columns.

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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. 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 OCHA HQ 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 28 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_covid_19_covid19_humanitarian_access,
  title     = {COVID19 Humanitarian Access Constraints, Impacts and Mitigation},
  author    = {OCHA HQ},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/covid19-humanitarian-access},
  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.