electricsheepafrica/africa-eastern-and-southern-africa-covid-19-unicef-situation-and-response

Eastern and Southern Africa COVID-19 - UNICEF - Situation and Response

Publisher: UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive) · Source: HDX · License: cc-by · Updated: 2024-08-30

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Abstract

COVID-19 Situation and Response Dashboard for Annual 2020

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2024-08-30. Geographic scope: AGO, ETH, KEN, MOZ, RWA, SOM, ZAF, SSD, and 1 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)	270
Columns	9 (3 numeric, 6 categorical, 0 datetime)
Train split	216 rows
Test split	54 rows
Geographic scope	AGO, ETH, KEN, MOZ, RWA, SOM, ZAF, SSD, and 1 others
Publisher	UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)
HDX last updated	2024-08-30

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Variables

Geographic — applied_filters_sitrep_r_is_13_business_area_region_is_esar (Zimbabwe, Madagascar, Comoros).

Identifier / Metadata — unnamed_1 (CV-04 - Access to continuous education, child protection and GBV services, CV-01 - Risk Communication and Community Engagement (RCCE), CV-03 - Continuity of health care for women and children), unnamed_2 (1: Number of people reached on COVID-19 through MESSAGING ON PREVENTION AND ACCESS TO SERVICES, 1: Number of children supported with DISTANCE/HOME-BASED LEARNING, 2: Number of people engaged on COVID-19 through RCCE ACTIONS), unnamed_3 (range 0.0–40500000.0), unnamed_4 (range 0.0–53200000.0), unnamed_5 (range 0.0–39.5) and 3 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-eastern-and-southern-africa-covid-19-unicef-situation-and-response")
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
applied_filters_sitrep_r_is_13_business_area_region_is_esar	object	0.4%	Zimbabwe, Madagascar, Comoros
unnamed_1	object	0.4%	CV-04 - Access to continuous education, child protection and GBV services, CV-01 - Risk Communication and Community Engagement (RCCE), CV-03 - Continuity of health care for women and children
unnamed_2	object	0.4%	1: Number of people reached on COVID-19 through MESSAGING ON PREVENTION AND ACCESS TO SERVICES, 1: Number of children supported with DISTANCE/HOME-BASED LEARNING, 2: Number of people engaged on COVID-19 through RCCE ACTIONS
unnamed_3	float64	2.6%	0.0 – 40500000.0 (mean 1204298.5665)
unnamed_4	float64	4.8%	0.0 – 53200000.0 (mean 1532827.0428)
unnamed_5	float64	7.0%	0.0 – 39.5 (mean 2.186)
unnamed_6	object	0.4%	SitRep 13, SitRep round
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-17

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Numeric Summary

Column	Min	Max	Mean	Median
unnamed_3	0.0	40500000.0	1204298.5665	60000.0
unnamed_4	0.0	53200000.0	1532827.0428	33164.0
unnamed_5	0.0	39.5	2.186	1.0

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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 Eastern and Southern Africa Regional Office (ESARO) (inactive) 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 9 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_eastern_and_southern_africa_covid_19_unicef_situation_and_response,
  title     = {Eastern and Southern Africa COVID-19 - UNICEF - Situation and Response},
  author    = {UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/eastern-and-southern-africa-covid-19-unicef-situation-and-response},
  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.