FaycalZM/Viro-Scope

ViroScope - Global Disease Outbreak Monitoring System 🧑‍⚕️📈

Overview

ViroScope is a cutting-edge, real-time data pipeline designed to track and analyze global health data, providing early warnings of potential disease outbreaks. By leveraging public health-related data, AI-powered data processing, and Fluvio's real-time data streaming capabilities, this system offers a comprehensive solution for monitoring, detecting, and responding to emerging health threats on a global scale.

Table of Contents 📑

• Features
• Technology Stack
• YouTube Demonstration
• Requirements
• Installation
• Usage
• Contribution
• Support

Features 🌟

• Real-time data processing pipeline using Fluvio CLI and FLuvio' python SDK, and Time Series Forecasting using LSTM model
• Predictions analysis & Anomaly detection: identify potential health outbreaks by understanding future trends of disease-related searches
• Model evaluation & visualization

Technology Stack ⚙️

• python with flask framework
• fluvio CLI & fluvio python SDK
• pandas, numpy for data preprocessing
• scikit-learn, keras for model creation and training
• matplotlib for visualization

YouTube Demonstration 📹

Watch the video

Requirements 🧰

• Python 3.7 or higher
• Rust compiler (for fluvio)

Installation ⏳

1. Clone the repository:

   git clone https://github.com/FaycalZM/Viro-Scope.git
   cd Viro-Scope

2. Create virtual env & activate it & Install required packages:

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

3. Set up Fluvio:

   • run the flv_setup.sh in the scripts directory:

     ./scripts/flv_setup.sh

Usage 🌎

1. Run the pipeline.sh script in the scripts directory to start the pipeline execution, and wait for magic to happen:

   ./scripts/pipeline.sh  

2. Data is fetched every 24 hours, but you can trigger the pipeline execution manually by sending a GET request to http://localhost:5000/fetch-trends :

   curl http://localhost:5000/fetch-trends

3. Run the model_evaluation.py script separately for additional informations:

   python src/model_evaluation.py

Contributing 🏗️

1. Fork the Repository:
   • Create a fork of the repository.
2. Open a Pull Request:
   • Open a pull request from your forked repository to the main repository.

Support 👍

If you like this project, please support it by upvoting on Quira and starring the GitHub repository!

Thank you for your support!