electricsheepasia/asia-immunization-access-indicators

Humanitarian Access Indicators

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

---

Abstract

Data on access constraints, aid workers security, % of affected CERF and CBPF projects combined with the status of Polio vaccination in the HRP countries.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-04-27. Geographic scope: AFG, AGO, ARG, ABW, BGD, BEN, BOL, BRA, and 54 others.

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

---

Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	63
Columns	14 (3 numeric, 11 categorical, 0 datetime)
Train split	50 rows
Test split	12 rows
Geographic scope	AFG, AGO, ARG, ABW, BGD, BEN, BOL, BRA, and 54 others
Publisher	HDX
HDX last updated	2026-04-27

---

Variables

Geographic — iso_code (#country+code, AFG, AGO), country (#country+name, Afghanistan, Angola), number_of_incidents_reported_in_previous_year (range 1.0–47.0), number_of_incidents_reported_since_start_of_year (range 1.0–43.0), number_of_incidents_reported_since_start_of_previous_year (range 1.0–81.0) and 2 others.

Identifier / Metadata — esa_source (HDX), esa_processed.

Other — of_visas_pending_or_denied (0%, 10%, #access+visas+pct), campaign_vaccine (bivalent Oral Poliovirus, Inactivated Polio, Type 2 Poliovirus), campaign_vaccine_status (Postponed, On track, Reinstated), campaign_vaccine_1 (bivalent Oral Poliovirus, Inactivated Polio, #service+name), campaign_vaccine_status_1 (Postponed, On track, #status+name).

---

Quick Start

from datasets import load_dataset

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

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
iso_code	object	0.0%	#country+code, AFG, AGO
country	object	0.0%	#country+name, Afghanistan, Angola
of_visas_pending_or_denied	object	73.0%	0%, 10%, #access+visas+pct
number_of_incidents_reported_in_previous_year	float64	46.0%	1.0 – 47.0 (mean 8.5882)
number_of_incidents_reported_since_start_of_year	float64	68.3%	1.0 – 43.0 (mean 6.75)
number_of_incidents_reported_since_start_of_previous_year	float64	44.4%	1.0 – 81.0 (mean 12.2)
of_cerf_projects_affected_by_insecurity_and_inaccessibility	object	49.2%	0.0%, 100.0%, 40.0%
of_cbpf_projects_affected_by_insecurity_and_inaccessibility	object	71.4%	0.0%, #activity+cbpf+project+insecurity+pct, 4.0%
campaign_vaccine	object	49.2%	bivalent Oral Poliovirus, Inactivated Polio, Type 2 Poliovirus
campaign_vaccine_status	object	52.4%	Postponed, On track, Reinstated
campaign_vaccine_1	object	49.2%	bivalent Oral Poliovirus, Inactivated Polio, #service+name
campaign_vaccine_status_1	object	49.2%	Postponed, On track, #status+name
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

---

Numeric Summary

Column	Min	Max	Mean	Median
number_of_incidents_reported_in_previous_year	1.0	47.0	8.5882	3.5
number_of_incidents_reported_since_start_of_year	1.0	43.0	6.75	2.0
number_of_incidents_reported_since_start_of_previous_year	1.0	81.0	12.2	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. 2 column(s) with >80% missing values were removed: of_travel_authorizations_or_movements_denied, number_of_learners_enrolled_from_pre_primary_to_tertiary_education. 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 HDX 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: of_visas_pending_or_denied, number_of_incidents_reported_in_previous_year, number_of_incidents_reported_since_start_of_year, number_of_incidents_reported_since_start_of_previous_year, of_cerf_projects_affected_by_insecurity_and_inaccessibility, of_cbpf_projects_affected_by_insecurity_and_inaccessibility, campaign_vaccine, campaign_vaccine_status....
• This dataset spans 62 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.

---

Citation

@dataset{hdx_asia_immunization_all,
  title     = {Humanitarian Access Indicators},
  author    = {HDX},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/access-indicators},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

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