EpiDash Wiki

Welcome to the EpiDash Wiki! This space provides comprehensive documentation for the Epidemiological Data Dashboard developed for the CEMA Software Engineering Internship.

📚 Wiki Navigation

• Home
• Installation Guide
• User Guide
• Technical Documentation
• API Reference
• Architecture Overview
• Development Roadmap
• Contributing Guidelines

🌟 Project Overview

EpiDash is an interactive web-based dashboard designed to visualize and analyze epidemiological data across Kenya. It serves as a demonstration of how digital solutions can enhance public health monitoring and decision-making through intuitive data visualization.

Core Functionality

• Disease Trend Analysis: Track disease patterns over customizable time periods
• Geographic Visualization: Map-based representation of disease prevalence by region
• Demographic Insights: Break down data by age groups and gender
• Comparative Analysis: Study relationships between multiple diseases or regions
• Data Export: Generate reports and export filtered data sets

🎯 Target Users

EpiDash is designed for multiple stakeholders in the public health ecosystem:

1. Epidemiologists & Researchers: For detailed analysis of disease patterns and outbreak investigations
2. Public Health Officials: For monitoring trends and making evidence-based decisions
3. Policy Makers: For understanding the impact of health interventions and resource allocation
4. Health Educators: For creating visualizations to communicate public health information

🛠️ Technical Stack Overview

EpiDash leverages modern web technologies to deliver a responsive and interactive experience:

Frontend

• UI Framework: Custom responsive design with modern CSS
• Visualization: Chart.js for statistical charts
• Mapping: Leaflet.js for interactive geographical visualization
• Interactivity: Vanilla JavaScript with modular organization

Backend

• Server: PHP 7.4+
• API: RESTful architecture for data retrieval
• Data Processing: Server-side filtering and aggregation
• Storage: Compatible with MySQL databases

Data Management

• Mock Data: Built-in synthetic data generation based on realistic patterns
• Production Mode: Database integration for real epidemiological data
• Export Formats: CSV for data portability

⚡ Quick Start

See the Installation Guide for detailed setup instructions. For a quick overview:

1. Clone the repository: git clone https://github.com/dlangkip/epidash.git
2. Configure environment: Copy .env.example to .env and update settings
3. Deploy to a PHP-compatible web server
4. Access via your web browser

💡 Key Features Explained

Interactive Data Visualization

The dashboard offers multiple visualization types including line charts for trends, bar charts for comparisons, pie charts for distributions, and choropleth maps for geographic analysis.

Advanced Filtering System

Users can filter data by:

• Disease type
• Geographic region
• Age group
• Gender
• Custom date ranges
• Data grouping (daily, weekly, monthly, quarterly, yearly)

Real-time Metrics

The dashboard calculates and displays key metrics including:

• Total and active cases
• Recovery rates
• Mortality rates
• Trend directions
• Regional comparisons

Data Export

Filtered data can be exported as CSV files for further analysis in external tools like Excel, R, or Python.

📈 Screenshots & Examples

Disease Trends Chart

To add a screenshot of your disease trends chart, upload it to your wiki directly

Visualizes disease incidence over time with multi-disease comparison capability

Regional Distribution Map

To add a screenshot of your regional map, upload it to your wiki directly

Choropleth map showing the distribution of selected diseases across Kenyan counties

Age Distribution Analysis

To add a screenshot of your age distribution chart, upload it to your wiki directly

Breakdown of disease incidence by age group to identify vulnerable populations

🔄 Development Status

EpiDash is currently in prototype stage with full frontend functionality and mock data generation for demonstration purposes. The project is structured for seamless transition to production use with real epidemiological data.

See the Development Roadmap for planned enhancements and feature additions.

📞 Contact & Support

For questions, suggestions, or contributions to EpiDash, please contact:

Amos Kiprotich Langat
📧 [email protected]
🔗 Portfolio · GitHub