Welcome to CEMA Warehouse Documentation¶
The Center for Epidemiological Modelling and Analysis (CEMA) Warehouse is a comprehensive, centralized data repository designed for health researchers, policymakers, and data analysts. This platform serves as a one-stop shop for accessing, sharing, and analyzing health-related datasets across multiple domains.
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What is CEMA Warehouse?¶
CEMA Warehouse transforms the way health data is stored, accessed, and utilized. Moving beyond fragmented storage across emails, drives, and local folders, our platform provides:
- Centralized Repository: Single source of truth for health datasets
- Smart Organization: Data categorized by health domains and research needs
- API Integration: Direct programmatic access for seamless workflows and integration
- Quality Assurance: Verified, clean, and well-documented datasets
- AI Assistant: CAIA chatbot to help find and access relevant data
Key Features¶
Organized Data Categories¶
- Demography:Stores population, census, and demographic indicators
- Health Systems: Healthcare facilities, staff, and infrastructure
- Accessibility: Geographic access to healthcare and infrastructure and readinesss of health commodities
- Health Status: Maternal and child health data
- Disease-Specific: Individual conditions, surveillance, and outbreak data i.e Neglected Tropical Diseases
- Geospatial data: Rater files and shapefiles
- Animal Health: Aniaml health surveillance data with a hope of one health data surveillance
- Health data catalogue: Contaains publictaions and policies
- Demographic Health Survey data: Contains DHS data from 1989-2022
Multiple Access Methods¶
- Web Interface: Browse and download through the cema warehouse webpage
- REST API: Programmatic access to data for developers and analysts for seamless integration with different workflows
- Direct Integration: Load data directly into Python, R, or other analysis tools
- Bulk Downloads: Download comprehensive datasets for offline analysis
"Intelligent" Assistance¶
Meet CAIA (CEMA "AI" Assistant), our query-powered data assistant that helps you:
- Discover relevant datasets based on your research needs
- Get direct API links for the searched datasets
- Navigate the data catalog efficiently and fast
Quick Links¶
| For Users | For Contributors | For Developers |
|---|---|---|
| Getting Started | Upload Guidelines | API Reference |
| Data Categories | Quality Standards | Python Integration |
| Using CAIA | Geospatial Data | R Integration |
Platform Statistics¶
Current Infrastructure Status
- Categories: There are 7 major data categories
- Health systems, Health status, Demography, Animal surveillance data, GIS data, Accessibility and Diseases Specific data.
- Datasets: This is a continuously growing data repository
- Access Methods: Cema web interface, REST API for direct integration
- File Types: CSV, Excel, Shapefiles, PDFs, Raster data
- Geographic Coverage: Focus on health systems and epidemiological data - captured in raster and shapefiles.
Who Uses the Warehouse?¶
Researchers¶
Access comprehensive datasets for epidemiological studies, health systems research, and policy analysis.
Policymakers¶
Find evidence-based data, reports, policies and publications to inform health policy decisions and resource allocation.
Developers¶
Integrate real-time health data into applications, dashboards, and analytical tools.
Health Professionals¶
Access current surveillance data, facility information, and health status indicators.
Getting Started¶
- Explore: Browse available datasets by category in the datasets section.
- Access: Download files or use API endpoints for integration
- Analyze: Load data directly into your preferred analysis environment
- Contribute: Upload your own datasets to expand the repository
- Get Help: Use CAIA assistant or consult our FAQ
Technical Integration¶
Python¶
import pandas as pd
# Direct data loading
df = pd.read_csv("https://warehouse.cema.africa/api/tables/Surveillance_data25?format=csv")
R¶
library(readr)
# Direct data loading
df <- read_csv("https://warehouse.cema.africa/api/tables/Surveillance_data25?format=csv")
# Or use fread
library(data.table)
df1 <- fread("https://warehouse.cema.africa/api/tables/Surveillance_data25?format=csv")
The CEMA Warehouse represents a significant step forward in making health data accessible, reliable, and actionable for the global research community.