epidatasets 0.3.1

Python library for accessing epidemiological datasets worldwide

🌍 Epidatasets

Sponsored by

AI-powered epidemiological intelligence

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A Python library providing unified access to 21 epidemiological data sources from around the world, with a plugin registry, CLI, and optional extras for specialized data.

📋 Table of Contents

• Overview
• Installation
• Quick Start
• Repository Structure
• Available Datasets
  • Global
  • North America
  • South America
  • Europe
  • Africa
  • Asia
  • Oceania
• CLI Usage
• Usage Examples
• Available Sources
• FAQ
• Contributing
• Related Projects
• Citation
• License

🎯 Overview

epidatasets provides:

• Unified interface — A single get_source() API to access 21 data sources worldwide
• Plugin registry — Sources are discovered at runtime via entry_points, making it easy to extend
• Optional extras — Install only the dependencies you need (pip install epidatasets[who,brazil])
• CLI — Command-line tool for listing sources, inspecting metadata, and querying countries
• Caching & rate limiting — Built-in utilities for responsible API usage
• Reproducible research — Standardized access to heterogeneous epidemiological datasets

📦 Installation

From PyPI

pip install epidatasets

With optional extras

# WHO Global Health Observatory data
pip install epidatasets[who]

# Brazilian DATASUS/SINAN data via PySUS
pip install epidatasets[brazil]

# Eurostat EU health statistics
pip install epidatasets[eurostat]

# Climate/environmental data (Copernicus CDS)
pip install epidatasets[climate]

# Geospatial visualization
pip install epidatasets[geo]

# Plotting & visualization
pip install epidatasets[viz]

# Genomic data (Pathoplexus)
pip install epidatasets[genomics]

# CLI support
pip install epidatasets[cli]

# World Bank indicators
pip install epidatasets[worldbank]

# Install everything
pip install epidatasets[all]

Development installation

git clone https://github.com/fccoelho/epidemiological-datasets.git
cd epidemiological-datasets
pip install -e ".[dev,docs]"

🚀 Quick Start

from epidatasets import get_source, list_sources

# Discover available sources
sources = list_sources()
for name, meta in sorted(sources.items()):
    print(f"{name}: {meta['description']}")

# Get a specific source
paho = get_source("paho")
countries = paho.list_countries()
print(f"PAHO covers {len(countries)} countries")

# Get WHO data (requires: pip install epidatasets[who])
who = get_source("who")
malaria = who.get_indicator(
    indicator="MALARIA_EST_INCIDENCE",
    years=[2020, 2021, 2022],
    countries=["BRA", "IND", "NGA"]
)

# Get OWID COVID-19 data
owid = get_source("owid")
covid = owid.get_covid_data(
    countries=["BRA", "USA", "IND"],
    metrics=["cases", "deaths"]
)

📁 Repository Structure

epidemiological-datasets/
├── src/epidatasets/           # Main Python package
│   ├── __init__.py            # Public API (get_source, list_sources)
│   ├── _base.py               # BaseAccessor ABC
│   ├── _registry.py           # Plugin registry (entry_points)
│   ├── cli.py                 # CLI (typer)
│   ├── sources/               # 21 data source accessors
│   │   ├── __init__.py
│   │   ├── africa_cdc.py
│   │   ├── cdc_opendata.py
│   │   ├── china_cdc.py
│   │   ├── colombia_ins.py
│   │   ├── copernicus_cds.py
│   │   ├── datasus_pysus.py
│   │   ├── ecdc_opendata.py
│   │   ├── epipulse.py
│   │   ├── eurostat.py
│   │   ├── global_health.py
│   │   ├── healthdata_gov.py
│   │   ├── india_idsp.py
│   │   ├── infodengue_api.py
│   │   ├── malaria_atlas.py
│   │   ├── owid.py
│   │   ├── paho.py
│   │   ├── pathoplexus.py
│   │   ├── respicast.py
│   │   ├── rki_germany.py
│   │   ├── ukhsa.py
│   │   └── who_ghoclient.py
│   └── utils/                 # Utilities
│       ├── cache.py           # Caching layer
│       ├── rate_limit.py      # API rate limiting
│       ├── geo.py             # Geospatial helpers
│       ├── validation.py      # Data validation
│       └── io.py              # I/O utilities
├── tests/                     # Test suite
│   ├── sources/
│   ├── utils/
│   ├── conftest.py
│   └── ...
├── docs/                      # MkDocs documentation
│   ├── mkdocs.yml
│   └── docs/
│       ├── index.md
│       ├── installation.md
│       ├── quickstart.md
│       ├── sources/           # Per-source API docs (21 pages)
│       ├── api/               # API reference
│       │   ├── base.md
│       │   ├── registry.md
│       │   ├── cli.md
│       │   └── utils.md
│       └── examples/          # Jupyter notebooks
├── mkdocs.yml                 # Docs config
├── .readthedocs.yaml          # ReadTheDocs config
├── pyproject.toml             # Package configuration
└── README.md

🌐 Available Datasets

Global 🌍

Dataset	Description	Update Frequency	Access Level	Module
WHO Global Health Observatory	Health indicators by country	Annual	Open	epidatasets.sources.who_ghoclient
Our World in Data - Health	COVID-19, vaccination, excess mortality	Daily/Weekly	Open	epidatasets.sources.owid
Global Health Data Exchange (GHDx)	Catalog of health datasets	Varies	Varies	Catalog only
HDX (Humanitarian Data Exchange)	Health in crisis contexts	Real-time	Open	Planned
Global.health	Pandemic linelist data	Varies	Open	epidatasets.sources.global_health
Malaria Atlas Project	Malaria prevalence & vector data	Annual	Open	epidatasets.sources.malaria_atlas
Copernicus Climate Data Store	Environmental & climate data	Varies	Open	epidatasets.sources.copernicus_cds
Pathoplexus	Pathogen genomic data	Continuous	Open	epidatasets.sources.pathoplexus
InfoDengue	Dengue surveillance (Brazil)	Weekly	Open	epidatasets.sources.infodengue_api

North America 🇺🇸🇨🇦🇲🇽

Dataset	Description	Update Frequency	Access Level	Module
CDC Open Data	CDC datasets portal (COVID-19, Influenza, NNDSS, CDI)	Varies	Open	epidatasets.sources.cdc_opendata
HealthData.gov	US health system data	Weekly	Open	epidatasets.sources.healthdata_gov
Statistics Canada - Health	Canadian health data	Quarterly	Open	Planned

South America 🌎

Dataset	Description	Update Frequency	Access Level	Module
SINAN / DATASUS - Brazil	Brazilian notifiable diseases & health system data	Weekly	Open*	epidatasets.sources.datasus_pysus
PAHO/WHO Regional Data	Pan-American health data	Monthly	Open	epidatasets.sources.paho
Chile DEIS	Chilean health statistics	Monthly	Open	Planned
Colombia INS	Colombian public health data (SIVIGILA)	Weekly	Open	epidatasets.sources.colombia_ins

*Note: DATASUS access requires pip install epidatasets[brazil] (installs PySUS).

Europe 🇪🇺

Dataset	Description	Update Frequency	Access Level	Module
ECDC EpiPulse	European surveillance portal (53 countries, 50+ diseases)	Daily/Weekly	Registration	epidatasets.sources.epipulse
ECDC Open Data	Infectious disease surveillance (50+ diseases, 30 countries)	Weekly	Open	epidatasets.sources.ecdc_opendata
ECDC RespiCast	Respiratory disease forecasting hub	Weekly	Open	epidatasets.sources.respicast
Eurostat Health	EU health statistics	Annual	Open	epidatasets.sources.eurostat
UK Health Security Agency	UK health data	Weekly	Open	epidatasets.sources.ukhsa
Robert Koch Institute	German surveillance data	Weekly	Open	epidatasets.sources.rki_germany

Africa 🌍

Dataset	Description	Update Frequency	Access Level	Module
WHO Afro Health Observatory	African region health data	Annual	Open	epidatasets.sources.who_ghoclient
Africa CDC	African public health data (55 AU member states)	Weekly	Open	epidatasets.sources.africa_cdc

Asia 🌏

Dataset	Description	Update Frequency	Access Level	Module
China CDC Weekly	Chinese surveillance data	Weekly	Open	epidatasets.sources.china_cdc
IDSP India	Indian disease surveillance	Weekly	Open*	epidatasets.sources.india_idsp
NIID Japan	Japanese infectious disease data	Weekly	Open	Planned
Korea CDC	Korean disease control data	Weekly	Open	Planned

Oceania 🇦🇺🇳🇿

Dataset	Description	Update Frequency	Access Level	Module
Australian Institute of Health and Welfare	Australian health data	Annual	Open	Planned
NZ Ministry of Health	New Zealand health statistics	Annual	Open	Planned

💻 CLI Usage

The epidatasets CLI provides quick access from the terminal (requires pip install epidatasets[cli]):

# List all available data sources
epidatasets sources

# Show detailed info about a source
epidatasets info who

# List countries covered by a source
epidatasets countries paho

💡 Usage Examples

Example 1: WHO Global Health Data

from epidatasets import get_source

who = get_source("who")

# Get malaria incidence data
data = who.get_indicator(
    indicator="MALARIA_EST_INCIDENCE",
    years=[2020, 2021, 2022],
    countries=["BRA", "IND", "NGA"]
)
print(data.head())

Example 2: PAHO Pan-American Health Data

from epidatasets import get_source

paho = get_source("paho")

# List member countries
countries = paho.list_countries()
print(f"Total countries: {len(countries)}")

# Get immunization coverage
coverage = paho.get_immunization_coverage(
    vaccines=['DTP3', 'MCV1'],
    subregion='Southern Cone',
    years=[2020, 2021, 2022]
)

# Compare health indicators
comparison = paho.compare_countries(
    indicator='LIFE_EXPECTANCY',
    countries=['BRA', 'MEX', 'ARG', 'COL'],
    years=[2019, 2020, 2021]
)

Example 3: Eurostat EU Health Statistics

from epidatasets import get_source

eurostat = get_source("eurostat")

# Healthcare expenditure
expenditure = eurostat.get_healthcare_expenditure(
    countries=['DEU', 'FRA', 'ITA'],
    years=list(range(2015, 2024))
)

# Mortality data by cause
mortality = eurostat.get_mortality_data(
    cause_code='COVID-19',
    countries=['DEU', 'FRA', 'ITA'],
    years=[2020, 2021, 2022]
)

# Life expectancy comparison
life_exp = eurostat.get_life_expectancy(
    countries=['DEU', 'FRA', 'ITA', 'ESP'],
    years=[2019, 2020, 2021]
)

Example 4: Our World in Data

from epidatasets import get_source

owid = get_source("owid")

# COVID-19 data for specific countries
covid = owid.get_covid_data(
    countries=['BRA', 'USA', 'IND'],
    metrics=['cases', 'deaths', 'hospitalizations'],
    start_date='2021-01-01',
    end_date='2021-12-31'
)

# Excess mortality estimates
excess = owid.get_excess_mortality(
    countries=['GBR', 'ITA', 'USA'],
    start_date='2020-03-01'
)

# Global summary
summary = owid.get_global_summary()

Example 5: Brazil DATASUS via PySUS

from epidatasets import get_source

datasus = get_source("datasus")

# Access Brazilian notifiable disease data
dengue = datasus.download(
    disease="Dengue",
    years=[2022, 2023],
    states=["RJ", "SP", "MG"]
)

Example 6: Africa CDC Data

from epidatasets import get_source

africa_cdc = get_source("africa_cdc")

# List all 55 African Union member states
countries = africa_cdc.list_countries()

# Get disease outbreaks
ebola = africa_cdc.get_disease_outbreaks(
    disease='EBOLA',
    countries=['CD', 'UG', 'GN']
)

# Vaccination coverage
vax = africa_cdc.get_vaccination_coverage(
    countries=['NG', 'ET', 'ZA'],
    vaccines=['COVID-19', 'Measles']
)

Example 7: RKI Germany Surveillance

from epidatasets import get_source

rki = get_source("rki")

# COVID-19 nowcasting with R estimates
nowcast = rki.get_covid_nowcast(
    date_range=('2022-01-01', '2022-06-30')
)

# Influenza surveillance
flu = rki.get_influenza_data(seasons=['2022/23', '2023/24'])

Example 8: Multi-source Comparison

from epidatasets import get_source, list_sources

# See all available sources
print(list_sources().keys())

# Compare data across sources
who = get_source("who")
owid = get_source("owid")

who_malaria = who.get_indicator(
    indicator="MALARIA_EST_INCIDENCE",
    years=[2022],
    countries=["BRA"]
)

owid_covid = owid.get_covid_data(
    countries=["BRA"],
    metrics=["cases", "deaths"],
    start_date='2022-01-01',
    end_date='2022-12-31'
)

📊 Available Sources

Source Name	Class	Extra	Description
africa_cdc	AfricaCDCAccessor	—	Africa CDC public health data (55 AU states)
cdc_opendata	CDCOpenDataAccessor	—	US CDC Open Data portal
china_cdc	ChinaCDCAccessor	—	China CDC Weekly surveillance
colombia_ins	ColombiaINSAccessor	—	Colombia INS/SIVIGILA surveillance
copernicus_cds	CopernicusCDSAccessor	[climate]	Copernicus Climate Data Store
datasus	DataSUSAccessor	[brazil]	Brazilian DATASUS/SINAN (via PySUS)
ecdc	ECDCOpenDataAccessor	—	ECDC infectious disease data
epipulse	EpiPulseAccessor	—	ECDC EpiPulse surveillance portal
eurostat	EurostatAccessor	[eurostat]	EU health statistics
global_health	GlobalHealthAccessor	—	Global.health pandemic linelist data
healthdata_gov	HealthDataGovAccessor	—	US HealthData.gov
india_idsp	IndiaIDSPAccessor	—	India IDSP disease surveillance
infodengue	InfoDengueAPI	—	InfoDengue dengue surveillance (Brazil)
malaria_atlas	MalariaAtlasAccessor	—	Malaria Atlas Project data
owid	OWIDAccessor	—	Our World in Data (COVID-19, vaccination)
paho	PAHOAccessor	—	PAHO Pan-American health data
pathoplexus	PathoplexusAccessor	[genomics]	Pathoplexus pathogen genomic data
respicast	RespiCastAccessor	—	ECDC respiratory disease forecasting
rki	RKIGermanyAccessor	—	Robert Koch Institute (Germany)
ukhsa	UKHSAAccessor	—	UK Health Security Agency
who	WHOAccessor	[who]	WHO Global Health Observatory

❓ FAQ

What is epidatasets?

A Python library providing a unified interface to 21 epidemiological data sources worldwide, installable via pip install epidatasets.

Do I need to install all optional dependencies?

No. The base install covers most sources. Only install extras for sources that need them (e.g., pip install epidatasets[who] for WHO GHO data, pip install epidatasets[brazil] for DATASUS).

How do I discover available sources?

from epidatasets import list_sources
print(list_sources())

Or from the CLI: epidatasets sources

Are all dataset accessors fully implemented?

Most accessors provide working data retrieval. Some are structured placeholders for sources that require registration or have limited public APIs. Check the documentation for each source's status.

Can I contribute a new data source?

Yes! Sources are registered via entry_points in pyproject.toml. See CONTRIBUTING.md for guidelines on adding new accessors.

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for detailed guidelines.

Quick Links for Contributors

• 📋 Contributing Guide - How to get started
• 🐛 Report a Bug
• 💡 Request a Feature
• 🌍 Request a Data Source
• 💬 GitHub Discussions - Ask questions, share ideas

Priority Contributions

1. New data source accessors - Especially from underrepresented regions
2. Example notebooks - Jupyter notebooks demonstrating data analysis
3. Documentation - Translations, improvements, and API docs
4. Bug fixes - Check the issue tracker

Badges for Contributors

📚 Related Projects

Project	Description	Repository
PySUS	Brazilian health data (DATASUS)	AlertaDengue/PySUS
ghoclient	WHO Global Health Observatory	fccoelho/ghoclient
epigrass	Epidemic simulation	EpiGrass/epigrass
epimodels	Mathematical epidemiology	fccoelho/epimodels

📊 Statistics

• Data sources: 21 registered (via plugin registry)
• Countries covered: 100+
• Optional extras: 10 (who, brazil, eurostat, climate, geo, viz, genomics, cli, worldbank, search)
• Example notebooks: 20+
• Documentation: epidatasets.readthedocs.io

📚 Citation

If you use this package in your research, please cite:

@misc{fccoelho_epidatasets,
  author = {Coelho, Flávio Codeço},
  title = {Epidatasets: Python Access to Epidemiological Datasets Worldwide},
  year = {2026},
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/fccoelho/epidemiological-datasets}}
}

For PySUS:

@software{pysus,
  author = {AlertaDengue Team},
  title = {PySUS: Tools for Brazilian Public Health Data},
  url = {https://github.com/AlertaDengue/PySUS}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

💜 Sponsor

This project is sponsored by

Kwar-AI — Intelligence for Epidemiology

AI-powered solutions for disease surveillance and outbreak prediction

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🙏 Acknowledgments

• PySUS Contributors - For making Brazilian health data accessible
• WHO - For maintaining the Global Health Observatory
• All data providers who make epidemiological data openly accessible
• Global public health community

📞 Contact

• Author: Flávio Codeço Coelho (@fccoelho)
• Website: https://fccoelho.github.io/
• Documentation: https://epidatasets.readthedocs.io

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Made with ❤️ for the epidemiological research community

🐛 Report Bug • 💡 Request Feature • 💬 Discussions

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Disclaimer: This repository is a community effort to catalog open data sources. Please always refer to the original data providers for official statistics and verify data usage terms. The maintainers are not responsible for data quality or availability.