electricsheepasia/asia-malnutrition-afghanistan-prevalence-of-global-acute-m

Afghanistan - Prevalence of Global Acute Malnutrition (GAM)

Publisher: OCHA Afghanistan · Source: HDX · License: cc-by · Updated: 2025-09-16

Abstract

Afghanistan NUT Cluster Severity Classification by GAM

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-09-16. Geographic scope: AFG.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

Variables

Geographic — name_of_the_province (Badakhshan, Badghis, Baghlan), gam_by_whz_among_children_0_59m_w_h_2sd_and_or_oedema (range 0.062–0.157), sam_by_whz_among_children_0_59m_w_h_3sd_and_or_oedema (range 0.013–0.042), year (range 2013.0–2018.0), survey_period_and_coverage (2013, whole province, Aug-17, whole province, Mar-17, whole province) and 1 others.

Outcome / Measurement — drought_affected_icct_list (Yes).

Identifier / Metadata — ref (SMART Survey, ACF, NNS-2013, Badghis Rapid SMART, ACF), esa_source (HDX), esa_processed (2026-05-04).

Other — sl (range 1.0–34.0), comments_remarks (WHZ <-2SD & <-3SD among 0-59m children, Limitation: One dist. Rapid SMART GAM rate is used to categorise the province. There is no recent province level data available, however the Rapid SMART was conducted at district level targeting whole population, therefore it's been extrapolated at provincial level proxy GAM, Quite old data but didn't observed any recent spike).

Quick Start

from datasets import load_dataset

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

print(train.shape)
train.head()

Schema

Numeric Summary

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) 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 OCHA Afghanistan 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: drought_affected_icct_list, comments_remarks.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_malnutrition_all,
  title     = {Afghanistan - Prevalence of Global Acute Malnutrition (GAM)},
  author    = {OCHA Afghanistan},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/afghanistan-prevalence-of-global-acute-malnutrition-gam},
  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.