electricsheepafrica/africa-south-sudan-africa-covid-19-recoveries-national-cfed8246

Africa Covid 19 Recoveries National | Africa (HERA - Humanitarian Emergency Response Africa)

31,673 rows - 1 Africa country/area - 2020-2021 - 1 indicator - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 31,673 rows from HERA - Humanitarian Emergency Response Africa, covering Africa Covid 19 Recoveries National. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Health datasets help analysts monitor disease burden, service delivery, population health outcomes, and public-health program performance.

Source-provided context: Daily Covid-19 recoveries per country in Africa HERA is gathering national and subnational Covid-19 data since the beginning of the outbreak. We would appreciate your feedback, feel free to contact us!

How To Read This Dataset

• One row means: one indicator observation for one geography, time period, and optional source dimensions.
• Primary geography column: country_iso3.
• Best time column: year.
• Time coverage basis: year.
• Recommended join keys: country_iso3, year, indicator_id.

Coverage

Dimension	Value
Rows	31,673
Countries/areas	1
First period	2020
Last period	2021
Indicators	1
Columns	22
Source format	XLSX

Geographic Coverage

Top areas shown below, sorted by row count when available:

Area	Rows	First year	Last year	Name
SSD	31,673	2020	2021	South Sudan

Indicators, Variables, Or Resource Contents

• africa-covid-19-recoveries-national-cfed8246 - Africa: Covid-19 Recoveries (National)(source_units_unspecified)

Schema

Column	Type	Description	Example
indicator_id	string	Stable source or Electric Sheep Africa indicator identifier.	africa-covid-19-recoveries-national-cfed8246
indicator_name	string	Human-readable indicator name.	Africa: Covid-19 Recoveries (National)
country_iso3	string	ISO3 country or area code.	SSD
source_sheet	string	Source column from the original resource.	Feuil1
country_name	string	Country or area name.	South Sudan
year	int64	Observation year.	2020
value	double	Numeric observation value.	0.0
unit	string	Measurement unit, when supplied by the source.	source_units_unspecified
dimension_iso	string	Source dimension retained during long-form normalization.	DZA
dimension_country_name	string	Source dimension retained during long-form normalization.	Algeria
dimension_african_region	string	Source dimension retained during long-form normalization.	Northern Africa
source_period_start_year	int64	Start year inferred from source metadata.	``
source_period_end_year	int64	End year inferred from source metadata.	``
source_period_label	string	Source column from the original resource.	``
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	HERA - Humanitarian Emergency Response Africa
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	Africa: Covid-19 Recoveries (National)
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Africa_COVID19_Daily_Recoveries_National.xlsx
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	ea6b98e6-4789-498c-8456-0f22eaf37996
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	e67637b9-3dc4-4853-bc86-facf25760f6e
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.humdata.org/dataset/ea6b98e6-4789-498c-8456-0f22eaf37996...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	cc-by
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-10T18:18:23Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-sudan-africa-covid-19-recoveries-national-cfed8246")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "SSD"]

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

• Canonical time field: year.
• Missing values are preserved rather than silently imputed.
• Column names are standardized for machine use; source meanings are preserved where known.
• Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

• Source: HERA - Humanitarian Emergency Response Africa
• Publisher: HERA - Humanitarian Emergency Response Africa
• Portal: https://data.humdata.org
• Resource: Africa_COVID19_Daily_Recoveries_National.xlsx
• License: CC BY 4.0
• Retrieved/generated: 2026-08-10T19:45:55Z
• Hugging Face repo: electricsheepafrica/africa-south-sudan-africa-covid-19-recoveries-national-cfed8246

Transformations Applied

• Converted the source table to Parquet for efficient analytics and ML workflows.
• Added or preserved source provenance columns where available.
• Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
• Preserved source-reported values without analytical imputation.

Suggested Analyses

• Compare health outcomes across geographies
• Track changes over time
• Join with population or facility data
• Build time-series views and period-over-period comparisons
• Pivot to geography x period or indicator x period matrices
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_south_sudan_africa_covid_19_recoveries_national_cfed8246_2021,
  title        = {Africa Covid 19 Recoveries National | Africa (HERA - Humanitarian Emergency Response Africa)},
  author       = {HERA - Humanitarian Emergency Response Africa},
  year         = {2021},
  url          = {https://data.humdata.org/dataset/africa-covid-19-recovered-cases},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-sudan-africa-covid-19-recoveries-national-cfed8246}}
}

License

Released under CC BY 4.0.

Original data is published by HERA - Humanitarian Emergency Response Africa. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

---

Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://data.humdata.org/dataset/africa-covid-19-recovered-cases