electricsheepasia/asia-epidemics-epidemiological-update-on-zika-virus-at

Global - Epidemiological update on Zika Virus

Publisher: International Federation of Red Cross and Red Crescent Societies (IFRC) · Source: HDX · License: cc-by · Updated: 2024-05-16

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Abstract

This data is a global overview on Zika Virus.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2024-05-16. Geographic scope: ASM, AIA, ATG, ARG, ABW, AUT, BHS, BRB, and 60 others.

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

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Dataset Characteristics

Domain	Epidemiology and disease surveillance
Unit of observation	Country-level aggregates
Rows (total)	86
Columns	11 (7 numeric, 4 categorical, 0 datetime)
Train split	68 rows
Test split	17 rows
Geographic scope	ASM, AIA, ATG, ARG, ABW, AUT, BHS, BRB, and 60 others
Publisher	International Federation of Red Cross and Red Crescent Societies (IFRC)
HDX last updated	2024-05-16

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Variables

Geographic — iso3 (#geo+ISO3, ABW, AIA), country (#country, Aruba, Anguilla), congenital_syndrome (range 0.0–1845.0), guillai_barre_syndrome (range 0.0–943.0).

Outcome / Measurement — deaths (range 0.0–6.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-05-05).

Other — sospechoso (range 0.0–196976.0), confirmed (range 0.0–78421.0), imported (range 0.0–2686.0), pregnants (range 0.0–18427.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-epidemics-epidemiological-update-on-zika-virus-at")
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
iso3	object	0.0%	#geo+ISO3, ABW, AIA
country	object	0.0%	#country, Aruba, Anguilla
sospechoso	float64	1.2%	0.0 – 196976.0 (mean 5513.8706)
deaths	float64	1.2%	0.0 – 6.0 (mean 0.1412)
confirmed	float64	1.2%	0.0 – 78421.0 (mean 1334.8235)
congenital_syndrome	float64	1.2%	0.0 – 1845.0 (mean 22.9647)
guillai_barre_syndrome	float64	1.2%	0.0 – 943.0 (mean 35.0353)
imported	float64	1.2%	0.0 – 2686.0 (mean 43.4941)
pregnants	float64	1.2%	0.0 – 18427.0 (mean 567.0588)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-05-05

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Numeric Summary

Column	Min	Max	Mean	Median
sospechoso	0.0	196976.0	5513.8706	0.0
deaths	0.0	6.0	0.1412	0.0
confirmed	0.0	78421.0	1334.8235	5.0
congenital_syndrome	0.0	1845.0	22.9647	0.0
guillai_barre_syndrome	0.0	943.0	35.0353	0.0
imported	0.0	2686.0	43.4941	1.0
pregnants	0.0	18427.0	567.0588	0.0

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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. 1 exact duplicate rows were removed. 7 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 International Federation of Red Cross and Red Crescent Societies (IFRC) 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 68 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_asia_epidemics_epidemiological_update_on_zika_virus_at,
  title     = {Global - Epidemiological update on Zika Virus},
  author    = {International Federation of Red Cross and Red Crescent Societies (IFRC)},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/epidemiological-update-on-zika-virus-at-global-level-week-of-12-09-2016},
  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.