🌍 Epidemiological Datasets

A curated collection of openly accessible epidemiological datasets from around the world, with Python tools for easy access and analysis.

📋 Table of Contents

Overview
Repository Structure
Available Datasets
- Global
- North America
- South America
- Europe
- Africa
- Asia
- Oceania
Python Scripts
- Using PySUS
- Using ghoclient
Installation
Usage Examples
FAQ
Available Scripts
Contributing
License

🎯 Overview

This repository aims to:

Centralize links to openly accessible epidemiological data sources
Standardize access to heterogeneous datasets through Python scripts
Document data formats, update frequencies, and access requirements
Enable reproducible research in epidemiology and public health

Whether you're studying infectious diseases, chronic conditions, or health systems, this collection provides starting points for data-driven research.

📁 Repository Structure

epidemiological-datasets/
├── 📁 .github/                 # GitHub templates and workflows
│   ├── 📁 DISCUSSION_TEMPLATE/ # Discussion templates
│   ├── 📁 ISSUE_TEMPLATE/      # Issue templates (bug, feature, data source)
│   ├── 📁 workflows/           # CI/CD workflows
│   └── 📄 pull_request_template.md
├── 📁 data/                    # Cached data (gitignored)
├── 📁 docs/                    # Documentation
│   └── 📄 index.md
├── 📁 examples/                # Example notebooks
│   ├── 📁 notebooks/
│   │   ├── 01_pysus_brazilian_health_data.ipynb
│   │   ├── 02_who_global_health_data.ipynb
│   │   ├── 03_world_bank_health_indicators.ipynb
│   │   ├── 04_ecdc_european_surveillance.ipynb
│   │   ├── 05_multi_source_comparison.ipynb
│   │   ├── 06_PAHO_Pan_American_Data.ipynb
│   │   ├── 07_Eurostat_EU_Health_Data.ipynb
│   │   ├── 08_OWID_Our_World_in_Data.ipynb
│   │   ├── 09_Colombia_INS_SIVIGILA_Data.ipynb
│   │   └── 10_Africa_CDC_Data.ipynb
│   ├── 📄 README.md
│   └── 📄 requirements.txt
├── 📁 scripts/                 # Python access scripts
│   ├── 📁 accessors/           # Dataset-specific accessors
│   │   ├── africa_cdc.py       # Africa CDC accessor
│   │   ├── china_cdc.py        # China CDC Weekly accessor
│   │   ├── colombia_ins.py     # Colombia INS/SIVIGILA accessor
│   │   ├── datasus_pysus.py    # PySUS wrapper
│   │   ├── eurostat.py         # Eurostat accessor
│   │   ├── global_health.py    # Global.health accessor
│   │   ├── healthdata_gov.py   # HealthData.gov accessor
│   │   ├── india_idsp.py       # India IDSP accessor
│   │   ├── owid.py             # Our World in Data accessor
│   │   ├── paho.py             # PAHO data accessor
│   │   ├── rki_germany.py      # RKI Germany accessor
│   │   ├── ukhsa.py            # UKHSA accessor
│   │   ├── who_ghoclient.py    # ghoclient wrapper
│   │   └── __init__.py
│   ├── 📄 __init__.py
│   └── 📄 utils.py             # Common utilities
├── 📁 src/
│   └── 📁 epi_data/            # Main Python package
│       ├── 📁 sources/         # Data source accessors
│       ├── 📁 utils/           # Utility functions
│       └── 📄 __init__.py
├── 📁 tests/                   # Test suite
│   ├── 📁 sources/
│   ├── 📁 utils/
│   ├── 📄 conftest.py
│   └── 📄 __init__.py
├── 📄 CHANGELOG.md             # Version history
├── 📄 CODE_OF_CONDUCT.md       # Community guidelines
├── 📄 CONTRIBUTING.md          # Contribution guide
├── 📄 LICENSE                  # MIT License
├── 📄 README.md                # This file
├── 📄 pyproject.toml           # Project configuration (UV)
├── 📄 requirements.txt         # Dependencies
└── 📄 requirements-dev.txt     # Dev dependencies

🌐 Available Datasets

Global 🌍

Dataset	Description	Update Frequency	Access Level	Script
WHO Global Health Observatory	Health indicators by country	Annual	Open	`ghoclient`
Our World in Data - Health	COVID-19, vaccination, excess mortality	Daily/Weekly	Open	`OWIDAccessor`
World Bank Health	Health, nutrition, and population statistics	Annual	Open	`scripts/accessors/worldbank.py`
Global Health Data Exchange (GHDx)	Catalog of health datasets	Varies	Varies	Catalog only
HDX (Humanitarian Data Exchange)	Health in crisis contexts	Real-time	Open	`scripts/accessors/hdx.py`

North America 🇺🇸🇨🇦🇲🇽

Dataset	Description	Update Frequency	Access Level	Script
CDC Wonder	US health statistics	Weekly	Open	`scripts/accessors/cdc.py`
CDC Open Data	CDC datasets portal	Varies	Open	`scripts/accessors/cdc_opendata.py`
HealthData.gov	US health system data	Weekly	Open	`scripts/accessors/healthdata_gov.py`
Statistics Canada - Health	Canadian health data	Quarterly	Open	Planned

South America 🌎

Dataset	Description	Update Frequency	Access Level	Script
SINAN - Brazil	Brazilian notifiable diseases	Weekly	Open*	`PySUS`
DATASUS	Brazilian health system data	Weekly	Open*	`PySUS`
SIAD - Brazil	Brazilian health information	Weekly	Open*	`PySUS`
PAHO/WHO Regional Data	Pan-American health data	Monthly	Open	`scripts/accessors/paho.py`
Chile DEIS	Chilean health statistics	Monthly	Open	Planned
Colombia INS	Colombian public health data	Weekly	Open	`scripts/accessors/colombia_ins.py`

*Note: PySUS handles authentication and access to DATASUS/SINAN data.

Europe 🇪🇺

Dataset	Description	Update Frequency	Access Level	Script
ECDC Surveillance Atlas	Infectious disease surveillance	Weekly	Open	`scripts/accessors/ecdc.py`
Eurostat Health	EU health statistics	Annual	Open	`scripts/accessors/eurostat.py`
UK Health Security Agency	UK health data	Weekly	Open	`scripts/accessors/ukhsa.py`
Robert Koch Institute	German surveillance data	Weekly	Open	`scripts/accessors/rki_germany.py`

Africa 🌍

Dataset	Description	Update Frequency	Access Level	Script
WHO Afro Health Observatory	African region health data	Annual	Open	`ghoclient`
DHIS2	Health information systems	Real-time	Varies	`scripts/accessors/dhis2.py`
Africa CDC	African public health data	Weekly	Open	`scripts/accessors/africa_cdc.py`

Asia 🌏

Dataset	Description	Update Frequency	Access Level	Script
China CDC Weekly	Chinese surveillance data	Weekly	Open	`scripts/accessors/china_cdc.py`
IDSP India	Indian disease surveillance	Weekly	Open*	`scripts/accessors/india_idsp.py`
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	Script
Australian Institute of Health and Welfare	Australian health data	Annual	Open	Planned
NZ Ministry of Health	New Zealand health statistics	Annual	Open	Planned

🐍 Python Scripts

Using PySUS for DATASUS

PySUS is a Python library developed by the AlertaDengue team for accessing Brazilian public health data (DATASUS).

Installation:

pip install pysus

Example usage:

from pysus.online_data import SINAN, SIM, SIH

# Download dengue cases from SINAN
dengue = SINAN.download(
    disease="Dengue",
    years=[2022, 2023],
    states=["RJ", "SP", "MG"]
)

# Download mortality data from SIM
mortality = SIM.download(
    years=[2021, 2022],
    states=["RJ"]
)

# Hospitalization data from SIH
hospitalizations = SIH.download(
    years=[2023],
    months=[1, 2, 3],
    states=["SP"],
    group="RD"  # AIH Reduced
)

Repository: github.com/AlertaDengue/PySUS
Documentation: pysus.readthedocs.io

Using ghoclient for WHO Data

ghoclient is a Python client for the WHO Global Health Observatory API.

Installation:

pip install ghoclient

Example usage:

from ghoclient import GHOClient

# Initialize client
client = GHOClient()

# Search for indicators
malaria_indicators = client.search_indicators("malaria")
print(malaria_indicators)

# Get specific indicator data
data = client.get_indicator(
    indicator="MALARIA_EST_INCIDENCE",
    years=[2020, 2021, 2022],
    countries=["BRA", "IND", "NGA"]
)

print(data.head())

Repository: github.com/fccoelho/ghoclient
PyPI: pypi.org/project/ghoclient

Using PAHO Accessor for Pan-American Data

The PAHO accessor provides access to health data from the Pan American Health Organization (PAHO), covering all countries in the Americas.

No installation required - uses native Python libraries.

Example usage:

from accessors import PAHOAccessor

# Initialize accessor
paho = PAHOAccessor()

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

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

# Get malaria data for endemic countries
malaria = paho.get_malaria_incidence(
    countries=['BRA', 'COL', 'PER'],
    years=[2020, 2021, 2022]
)

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

Data Sources:

PAHO Data Portal: https://www.paho.org/en/data
WHO GHO API: https://www.who.int/data/gho
WHO Immunization API: https://immunizationdata.who.int

Using Eurostat Accessor for EU Health Data

The Eurostat accessor provides access to European Union health statistics covering 27 EU member states, EFTA countries, and candidate countries.

Optional installation for better performance:

pip install eurostat

Example usage:

from accessors import EurostatAccessor

# Initialize accessor (uses REST API if eurostat library not installed)
eurostat = EurostatAccessor()

# List EU member countries
countries = eurostat.list_eu_countries()
print(f"Total EU countries: {len(countries)}")

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

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

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

# Get physician data
physicians = eurostat.get_physicians(
    countries=['DEU', 'FRA'],
    years=[2020, 2021, 2022]
)

# Compare countries
comparison = eurostat.compare_countries(
    indicator_code='demo_mlexpec',
    countries=['DEU', 'FRA', 'ITA', 'ESP'],
    years=[2019, 2020, 2021]
)

Features:

27 EU member states + EFTA + candidate countries
Healthcare expenditure and financing
Mortality and causes of death (ICD-10 based)
Health workforce (physicians, nurses, hospital beds)
Health determinants (lifestyle, environment)
Life expectancy and infant mortality
Self-perceived health status

Data Sources:

Eurostat Health: https://ec.europa.eu/eurostat/web/health
Eurostat Data Browser: https://ec.europa.eu/eurostat/databrowser
Eurostat API: https://ec.europa.eu/eurostat/web/sdmx-web-services
Python Library: https://pypi.org/project/eurostat/

Using OWID Accessor for Our World in Data

The OWID accessor provides access to comprehensive health datasets from Our World in Data, including COVID-19 data, vaccination statistics, and excess mortality estimates.

No installation required - uses native Python libraries.

Example usage:

from accessors import OWIDAccessor

# Initialize accessor
owid = OWIDAccessor()

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

# Get 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'
)

# Get vaccination data
vax = owid.get_vaccination_data(countries=['BRA', 'USA'])

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

# Compare countries
comparison = owid.compare_countries(
    countries=['BRA', 'USA', 'IND', 'GBR'],
    metric='total_deaths_per_million'
)

# Get regional aggregates
sa_cases = owid.get_region_aggregates(
    region='South America',
    metric='new_cases',
    aggregation='sum'
)

# Get latest global summary
summary = owid.get_global_summary()
print(summary)

Features:

192 countries worldwide
COVID-19 cases, deaths, testing data
Vaccination progress (total, people vaccinated, boosters)
Hospitalization and ICU data
Excess mortality estimates
Government response indices (stringency, containment)
Regional aggregation (6 continents)
Cross-country comparison tools

Data Sources:

Our World in Data: https://ourworldindata.org/health
COVID-19 Data: https://github.com/owid/covid-19-data
GitHub Datasets: https://github.com/owid/owid-datasets
API: https://covid.ourworldindata.org/data
License: CC BY (Creative Commons Attribution)

Using Africa CDC Accessor for African Public Health Data

The Africa CDC accessor provides access to public health surveillance data from the African Centres for Disease Control and Prevention, covering 55 African Union member states across 5 regions.

No installation required - uses native Python libraries.

Example usage:

from accessors import AfricaCDCAccessor

# Initialize accessor
africa_cdc = AfricaCDCAccessor()

# List all 55 African Union member states
countries = africa_cdc.list_countries()
print(f"Total AU countries: {len(countries)}")

# List Africa CDC regions
regions = africa_cdc.list_regions()
print("Regions:", regions['region'].unique())

# Get outbreak data for Ebola in specific countries
ebola = africa_cdc.get_disease_outbreaks(
    disease='EBOLA',
    countries=['CD', 'UG', 'GN']  # DRC, Uganda, Guinea
)

# Get COVID-19 data for West Africa
west_africa = africa_cdc.get_countries_by_region('Western')
covid = africa_cdc.get_covid_data(
    countries=west_africa,
    date_range=('2020-03-01', '2021-12-31')
)

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

# Get Event-Based Surveillance alerts for Sahel region
sahel = ['ML', 'NE', 'TD', 'BF']  # Mali, Niger, Chad, Burkina Faso
alerts = africa_cdc.get_event_based_surveillance(
    countries=sahel,
    date_range=('2024-01-01', '2024-12-31')
)

# Get weekly outbreak briefs
briefs = africa_cdc.get_weekly_outbreak_brief(year=2024)
print(f"Total briefs in 2024: {len(briefs)}")

# Compare disease occurrence across regions
regional_comparison = africa_cdc.compare_regions(
    disease='COVID-19',
    year=2021
)

# Get country summary
summary = africa_cdc.get_summary_by_country(
    year=2024,
    disease='MALARIA'
)

Features:

55 African Union member states
5 Africa CDC regions (Central, Eastern, Northern, Southern, Western)
15 priority diseases (Ebola, Marburg, Lassa, Cholera, Mpox, Dengue, Malaria, etc.)
Disease outbreak surveillance
COVID-19 historical data
Vaccination coverage tracking
Event-Based Surveillance (EBS) alerts
Weekly outbreak brief metadata
Regional comparison tools

Data Sources:

Africa CDC: https://africacdc.org/
Weekly Outbreak Briefs: PDF documents
IDSR (Integrated Disease Surveillance and Response)
African Union Member State reports

Using RKI Germany Accessor for German Surveillance Data

The RKI Germany accessor provides access to infectious disease surveillance data from Germany's Robert Koch Institute, including COVID-19 nowcasting, influenza surveillance, and notifiable diseases.

No installation required - uses native Python libraries.

Example usage:

from accessors import RKIGermanyAccessor

# Initialize accessor
rki = RKIGermanyAccessor()

# List German federal states
states = rki.list_states()
print(f"Total states: {len(states)}")

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

# Get COVID-19 hospitalizations
hosp = rki.get_covid_hospitalizations(
    states=['DE-BE', 'DE-BY'],
    date_range=('2022-01-01', '2022-12-31')
)

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

# Get notifiable disease data (structure)
measles = rki.get_notifiable_disease(
    disease='Measles',
    years=[2022, 2023]
)

# Get vaccination data
vax = rki.get_covid_vaccinations(states=['DE'])

Features:

16 German federal states (Bundesländer)
COVID-19 nowcasting with 7-day R estimates
COVID-19 hospitalizations by state
COVID-19 vaccination data
Influenza weekly surveillance reports
25 notifiable infectious diseases
AMR (antimicrobial resistance) surveillance structure

Data Sources:

RKI GitHub: https://github.com/robert-koch-institut
COVID-19 Nowcasting: https://github.com/robert-koch-institut/SARS-CoV-2-Nowcasting_und_-R-Schaetzung
Influenza Reports: https://github.com/robert-koch-institut/Influenza-Wochenberichte
SurvStat@RKI: https://survstat.rki.de/

Using China CDC Accessor for Chinese Surveillance Data

The China CDC accessor provides access to surveillance data from China CDC Weekly, including notifiable infectious diseases and weekly surveillance reports.

No installation required - uses native Python libraries.

Example usage:

from accessors import ChinaCDCAccessor

# Initialize accessor
ccdc = ChinaCDCAccessor()

# List notifiable diseases (39 categories)
diseases = ccdc.list_notifiable_diseases()
print(f"Total diseases: {len(diseases)}")

# List Chinese provinces (31 total)
provinces = ccdc.list_provinces()
print(f"Total provinces: {len(provinces)}")

# Get weekly report metadata
reports = ccdc.get_weekly_reports(year=2024)

# Get notifiable disease data (structure)
disease_data = ccdc.get_notifiable_diseases(
    diseases=['Influenza', 'Dengue'],
    provinces=['GD', 'BJ'],
    date_range=('2023-01-01', '2023-12-31')
)

# Get influenza surveillance structure
flu = ccdc.get_influenza_surveillance(weeks=range(1, 53), year=2023)

Features:

39 notifiable infectious diseases (Class A/B/C)
31 Chinese provinces and municipalities
Weekly surveillance report metadata
ILI (Influenza-like Illness) surveillance
COVID-19 updates structure

Data Sources:

China CDC Weekly: http://weekly.chinacdc.cn/
Chinese CDC: https://www.chinacdc.cn/

Using India IDSP Accessor for Indian Surveillance Data

The India IDSP accessor provides access to disease surveillance data from India's Integrated Disease Surveillance Programme (IDSP), covering all states and union territories.

No installation required - uses native Python libraries.

Example usage:

from accessors import IndiaIDSPAccessor

# Initialize accessor
idsp = IndiaIDSPAccessor()

# List Indian states/UTs (36 total)
states = idsp.list_states()
print(f"Total states/UTs: {len(states)}")

# List priority diseases (29 categories)
diseases = idsp.list_priority_diseases()
print(f"Total diseases: {len(diseases)}")

# Get outbreak reports (structure)
outbreaks = idsp.get_outbreak_reports(
    years=[2023],
    states=['KL', 'MH']  # Kerala, Maharashtra
)

# Get disease surveillance (structure)
dengue = idsp.get_disease_surveillance(
    disease='Dengue',
    states=['KA', 'TN', 'KL'],  # Karnataka, Tamil Nadu, Kerala
    years=[2022, 2023]
)

# Get vector-borne disease data
vbd = idsp.get_vector_borne_diseases(
    diseases=['Malaria', 'Dengue', 'Chikungunya']
)

# Get weekly surveillance summary
weekly = idsp.get_weekly_surveillance_summary(
    year=2024, week=10
)

Features:

36 Indian states and union territories
29 priority diseases under IDSP surveillance
Weekly outbreak reports structure
Disease surveillance by syndrome (S/P/L)
Vector-borne disease data (NVBDCP)
Laboratory surveillance structure

Data Sources:

📦 Installation

Standard Installation

# Clone the repository
git clone https://github.com/fccoelho/epidemiological-datasets.git
cd epidemiological-datasets

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install additional packages for specific sources
pip install pysus ghoclient

Development Installation

# Install in editable mode with dev dependencies
pip install -e ".[dev]"

💡 Usage Examples

Example 1: Brazilian Health Data with PySUS

from pysus.online_data import SINAN
import pandas as pd

# Download dengue notification data
df = SINAN.download(
    disease="Dengue",
    years=[2023],
    states=["RJ", "SP"]
)

# Analyze cases by municipality
cases_by_city = df.groupby('ID_MUNICIP').size()
print(cases_by_city.sort_values(ascending=False).head(10))

Example 2: WHO Data with ghoclient

from ghoclient import GHOClient

client = GHOClient()

# Get COVID-19 vaccination data
vaccination = client.get_indicator(
    indicator="COVID19_VACCINATION",
    years=[2021, 2022],
    countries=["BRA", "USA", "GBR"]
)

# Calculate vaccination coverage
for country in vaccination['country'].unique():
    country_data = vaccination[vaccination['country'] == country]
    latest = country_data.loc[country_data['year'].idxmax()]
    print(f"{country}: {latest['value']}% vaccinated in {latest['year']}")

Example 3: Multi-source Analysis

from pysus.online_data import SINAN
from ghoclient import GHOClient
import pandas as pd

# Get Brazilian dengue data
sinan_data = SINAN.download(disease="Dengue", years=[2022])
br_cases = len(sinan_data)

# Get WHO data for comparison
who = GHOClient()
who_data = who.get_indicator(
    indicator="MALARIA_EST_INCIDENCE",
    years=[2022],
    countries=["BRA"]
)

print(f"Brazil Dengue cases (SINAN): {br_cases}")
print(f"Brazil Malaria incidence (WHO): {who_data['value'].values[0]}")

❓ FAQ

What is the purpose of this repository?

This repository provides a curated collection of publicly accessible epidemiological datasets from around the world, along with Python tools for accessing and analyzing them.

Do I need to install all the listed Python libraries?

No. Some datasets require specific libraries such as pysus or ghoclient. You only need to install the libraries required for the data sources you plan to use.

Are all dataset access scripts already implemented?

Not yet. Some scripts are already available, while others are marked as Planned in the Available Scripts section.

Where should I start if I want to explore the datasets?

Start with the Available Datasets section to explore data sources by region, then review the Usage Examples section for practical Python workflows.

Can I contribute a new dataset or improve the documentation?

Yes. Contributions are welcome. You can open an issue to propose a new dataset or submit a pull request with your contribution.

Do I need programming experience to use this repository?

Basic familiarity with Python is recommended for running scripts and examples, but you can still explore dataset sources without coding.

📊 Available Scripts

Script	Library Used	Status	Description
`africa_cdc.py`	Native	✅ Available	Africa CDC (African public health data)
`china_cdc.py`	Native	✅ Available	China CDC Weekly (Chinese surveillance data)
`colombia_ins.py`	Native	✅ Available	Colombia INS (SIVIGILA surveillance data)
`datasus_pysus.py`	PySUS	✅ Available	Wrapper for PySUS with additional utilities
`eurostat.py`	Native/eurostat	✅ Available	Eurostat (EU) health statistics accessor
`global_health.py`	Native	✅ Available	Global.health pandemic data (⚠️ Broken - Issue #40)
`healthdata_gov.py`	Native	✅ Available	HealthData.gov (US health system data)
`india_idsp.py`	Native	✅ Available	India IDSP (Integrated Disease Surveillance Programme)
`owid.py`	Native	✅ Available	Our World in Data (COVID-19, vaccination, excess mortality)
`paho.py`	Native	✅ Available	PAHO (Pan American Health Organization) data accessor
`rki_germany.py`	Native	✅ Available	RKI Germany (Robert Koch Institute surveillance data)
`ukhsa.py`	Native	✅ Available	UKHSA (UK Health Security Agency) - ⚠️ Placeholder data only
`who_ghoclient.py`	ghoclient	✅ Available	Wrapper for ghoclient with pandas integration
`cdc.py`	Native	🔄 Planned	CDC Wonder and Open Data
`ecdc.py`	Native	🔄 Planned	European CDC data
`worldbank.py`	Native	🔄 Planned	World Bank health indicators

🤝 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

New dataset links - Especially from underrepresented regions
Python accessors - For datasets without existing libraries
Examples - Jupyter notebooks demonstrating data analysis
Documentation - Translations and improvements

Badges for Contributors

📚 Related Projects

This repository complements the following open-source tools:

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

Datasets documented: 30+
Countries covered: 100+
Python libraries integrated: 2 (PySUS, ghoclient)
Native accessors: 13 (PAHO, Eurostat, OWID, Africa CDC, Colombia INS, DATASUS, RKI Germany, China CDC, India IDSP, HealthData.gov, UKHSA, Global.health, WHO)
Example notebooks: 10
Last updated: 2026-03-19

📚 Citation

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

@misc{fccoelho_epidemiological_datasets,
  author = {Coelho, Flávio Codeço},
  title = {Epidemiological Datasets: A Global Collection},
  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.

🙏 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)
Email: [Your email]
Twitter: [Your Twitter]
Website: https://fccoelho.github.io/

Made with ❤️ for the epidemiological research community

🐛 Report Bug • 💡 Request Feature • 💬 Discussions

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.

Name		Name	Last commit message	Last commit date
Latest commit History 48 Commits
.github		.github
data		data
docs		docs
examples		examples
notebooks/examples		notebooks/examples
scripts		scripts
src/epi_data		src/epi_data
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt

Folders and files

Latest commit

History

Repository files navigation

🌍 Epidemiological Datasets

📋 Table of Contents

🎯 Overview

📁 Repository Structure

🌐 Available Datasets

Global 🌍

North America 🇺🇸🇨🇦🇲🇽

South America 🌎

Europe 🇪🇺

Africa 🌍

Asia 🌏

Oceania 🇦🇺🇳🇿

🐍 Python Scripts

Using PySUS for DATASUS

Using ghoclient for WHO Data

Using PAHO Accessor for Pan-American Data

Using Eurostat Accessor for EU Health Data

Using OWID Accessor for Our World in Data

Using Africa CDC Accessor for African Public Health Data

Using RKI Germany Accessor for German Surveillance Data

Using China CDC Accessor for Chinese Surveillance Data

Using India IDSP Accessor for Indian Surveillance Data

📦 Installation

Standard Installation

Development Installation

💡 Usage Examples

Example 1: Brazilian Health Data with PySUS

Example 2: WHO Data with ghoclient

Example 3: Multi-source Analysis

❓ FAQ

What is the purpose of this repository?

Do I need to install all the listed Python libraries?

Are all dataset access scripts already implemented?

Where should I start if I want to explore the datasets?

Can I contribute a new dataset or improve the documentation?

Do I need programming experience to use this repository?

📊 Available Scripts

🤝 Contributing

Quick Links for Contributors

Priority Contributions

Badges for Contributors

📚 Related Projects

📊 Statistics

📚 Citation

📄 License

🙏 Acknowledgments

📞 Contact

About

Resources

Code of conduct

Contributing

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages