---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- disease
- epidemics-outbreaks
- hxl
- asm
- aia
- atg
- arg
- abw
pretty_name: "Global - Epidemiological update on Zika Virus"
dataset_info:
  splits:
    - name: train
      num_examples: 68
    - name: test
      num_examples: 17
---

# Global - Epidemiological update on Zika Virus

**Publisher:** International Federation of Red Cross and Red Crescent Societies (IFRC) · **Source:** [HDX](https://data.humdata.org/dataset/epidemiological-update-on-zika-virus-at-global-level-week-of-12-09-2016) · **License:** `cc-by` · **Updated:** 2024-05-16

---

## 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](https://huggingface.co/electricsheepafrica).*

---

## 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 |

---

## 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).

---

## Quick Start

```python
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()
```

---

## 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 |

---

## 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 |

---

## 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.

---

## 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](https://data.humdata.org/dataset/epidemiological-update-on-zika-virus-at-global-level-week-of-12-09-2016) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*