📊 Pandemic Response Dashboard

A centralized dashboard for monitoring pandemic-related metrics such as infection rates, vaccination coverage, and hospital capacity across regions. Built using AWS services, this system enables real-time data collection, scalable analytics, and interactive visualization.

AWS Architecture (Work in progress)

🚀 Features

✅ Real-time Data Collection: Fetches live pandemic data from official health agencies.
✅ AWS-Powered Data Pipeline: Uses Kinesis, S3, Glue, Redshift, and QuickSight.
✅ Scalable & Secure: Handles large datasets efficiently.
✅ Interactive Dashboard: Visualizes key metrics for public health monitoring.
✅ Automated ETL Processing: Cleans and structures data for insights.

🛠️ Tech Stack

AWS Services:
- 🟢 Kinesis (Data Streaming)
- 🟢 S3 (Data Storage)
- 🟢 Glue (ETL Processing)
- 🟢 Redshift (Data Warehouse)
- 🟢 QuickSight (Data Visualization)
Languages & Tools:
- 🐍 Python (Data processing & ETL scripts)
- 🔹 SQL (Redshift queries)
- 🏗 Terraform/CDK (Infrastructure as Code)

📂 Project Structure

📦 Pandemic-Response-Dashboard
├── 📁 data_pipeline
│   ├── kinesis_producer.py   # Streams real-time data
│   ├── glue_etl.py           # AWS Glue ETL job
│   ├── redshift_loader.sql   # Loads data into Redshift
│   ├── config.json           # AWS configurations
│
├── 📁 dashboards
│   ├── quicksight_setup.md   # Steps to configure QuickSight
│   ├── dashboard_screenshots # Visuals of the dashboard
│
├── 📁 infrastructure
│   ├── terraform/            # Terraform scripts for AWS infra
│   ├── cdk/                  # AWS CDK setup
│
├── 📁 scripts
│   ├── data_fetcher.py       # Collects data from APIs
│   ├── alert_notifier.py     # Sends alerts on anomalies
│
├── 📜 .github/workflows/deploy.yml  # CI/CD Pipeline
├── 📜 requirements.txt        # Python dependencies
├── 📜 README.md               # Project documentation

📡 Data Flow Architecture

1️⃣ Data Ingestion:

Collects pandemic-related data using AWS Kinesis (real-time streaming).
Stores raw data in Amazon S3.

2️⃣ Data Processing & ETL:

AWS Glue cleans and processes data into structured format.
Transformed data is stored in Amazon S3 (processed bucket).

3️⃣ Data Storage & Analysis:

Data is loaded into Amazon Redshift for analytics.
SQL queries are used for trend analysis.

4️⃣ Visualization & Insights:

AWS QuickSight generates interactive dashboards.
Key metrics are displayed for decision-making.

📦 Installation & Setup

🔹 Prerequisites

AWS Account with necessary permissions (S3, Kinesis, Glue, Redshift, QuickSight).
Python 3.8+ installed on your local machine.
AWS CLI configured with access credentials.

🔹 Setup Instructions

Clone the repository:

git clone https://github.com/nagakirankasi/Pandemic-Response-Dashboard.git
cd Pandemic-Response-Dashboard

Install dependencies:
```
pip install -r requirements.txt
```

Set up AWS infrastructure using Terraform or CDK:

cd infrastructure/terraform
terraform init
terraform apply

Run data producer (Kinesis stream):

python data_pipeline/kinesis_producer.py

Trigger AWS Glue ETL Job:

aws glue start-job-run --job-name pandemic_etl

Run Redshift SQL Loader:

psql -h your-redshift-cluster.amazonaws.com -U username -d database -f data_pipeline/redshift_loader.sql

Set up QuickSight Dashboard (Follow dashboards/quicksight_setup.md).

📊 Sample Dashboard Screenshots

📌 [Include screenshots of AWS QuickSight visualizations]
(Replace with actual image URLs)
Dashboard Example - Work in progress

🚀 Future Enhancements

🔹 Machine Learning for Predictions (Predict future pandemic trends).
🔹 Automated Alerts (Email/SMS notifications for spikes in infections).
🔹 Multi-Region Support (Expanding global health monitoring).

👨‍💻 Contributing

Contributions are welcome! 🎉 Please follow these steps:
1️⃣ Fork the repo.
2️⃣ Create a new branch: feature-new-feature.
3️⃣ Commit changes & push.
4️⃣ Submit a PR for review.

📌 See CONTRIBUTING.md for details.

📜 License

This project is licensed under the MIT License. See LICENSE for details.