electricsheepafrica/africa-number-of-admissions-for-treatment-of-severe-acute-malnutrition-sam

Admissions for Treatment of Severe Acute Malnutrition (SAM)

Publisher: UNICEF Data and Analytics (HQ) · Source: HDX · License: cc-by · Updated: 2026-01-30

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Abstract

Number of children 6-59 months admitted for TREATMENT OF SEVERE ACUTE MALNUTRITION (SAM) by country

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the time_period column(s). Geographic scope: AFG, ALB, DZA, AGO, ARG, ARM, AZE, BHR, and 122 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	Time-series observations
Rows (total)	806
Columns	16 (2 numeric, 13 categorical, 1 datetime)
Train split	644 rows
Test split	161 rows
Geographic scope	AFG, ALB, DZA, AGO, ARG, ARM, AZE, BHR, and 122 others
Publisher	UNICEF Data and Analytics (HQ)
HDX last updated	2026-01-30

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Variables

Temporal — time_period.

Outcome / Measurement — obs_value (range 0.0–1182374.0).

Identifier / Metadata — ref_area (ZWE, MOZ, NIC), data_source (UNICEF Situation Report 3, UNICEF Situation Report 2, UNICEF Situation Report 8), esa_source, esa_processed.

Other — geographic_area (Zimbabwe, Mozambique, Nicaragua), sitrep_indicator (CV-03-04, #indicator+code), situation_report_indicator (Number of children 6-59 months admitted for TREATMENT OF SEVERE ACUTE MALNUTRITION (SAM), #indicator+name), hac_pillar (CV-03), humanitarian_action_for_children_pillar (Continuity of health care for women and children) and 5 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-number-of-admissions-for-treatment-of-severe-acute-malnutrition-sam")
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
ref_area	object	0.0%	ZWE, MOZ, NIC
geographic_area	object	0.0%	Zimbabwe, Mozambique, Nicaragua
sitrep_indicator	object	0.0%	CV-03-04, #indicator+code
situation_report_indicator	object	0.0%	Number of children 6-59 months admitted for TREATMENT OF SEVERE ACUTE MALNUTRITION (SAM), #indicator+name
hac_pillar	object	0.1%	CV-03
humanitarian_action_for_children_pillar	object	0.1%	Continuity of health care for women and children
unit_measure	object	0.1%	PS
unit_of_measure	object	0.1%	Persons
time_period	datetime64[ns]	0.1%
obs_value	float64	5.0%	0.0 – 1182374.0 (mean 37557.1214)
data_source	object	0.0%	UNICEF Situation Report 3, UNICEF Situation Report 2, UNICEF Situation Report 8
target	float64	2.1%	0.0 – 1195407.0 (mean 89981.2446)
obs_status	object	0.1%	V, M
observation_status	object	0.1%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
obs_value	0.0	1182374.0	37557.1214	3298.5
target	0.0	1195407.0	89981.2446	15000.0

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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. 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.

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Limitations

• Data originates from UNICEF Data and Analytics (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 130 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_africa_number_of_admissions_for_treatment_of_severe_acute_malnutrition_sam,
  title     = {Admissions for Treatment of Severe Acute Malnutrition (SAM)},
  author    = {UNICEF Data and Analytics (HQ)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/number-of-admissions-for-treatment-of-severe-acute-malnutrition-sam},
  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.