CoviSense AI

🦠Covid 19 Monitoring & Forecasting System

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📌 Overview

CoviSense AI is an AI-powered epidemiological system developed under Track C: Epidemic Spread Prediction.

It analyzes historical COVID-19 data to predict future outbreaks, identify trends, and assess risk levels using machine learning and interactive visualization.

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🎯 Problem Statement (Track C)

Predicting the spread of infectious diseases is crucial for public health planning and response.

This project focuses on:

• Disease spread prediction
• Outbreak trend analysis
• Risk & hotspot identification

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💡 Solution

CoviSense AI combines:

• Data analytics
• Machine learning models
• Interactive dashboards
• AI-powered chatbot

to provide actionable insights into epidemic trends.

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Live Demo

https://covisense-ai.streamlit.app/

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🚀 Features

• Country-wise COVID-19 case tracking
• Growth rate & trend analysis
• Future outbreak prediction
• Interactive world map & 3D globe
• Risk level detection (Low / Medium / High)
• AI insights panel
• Smart chatbot assistant

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🛠 Tech Stack

• Frontend: Streamlit
• Backend: Python
• Libraries: pandas, numpy, plotly, scikit-learn
• AI Integration: OpenAI API

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📊 Dataset

• Johns Hopkins University COVID-19 dataset

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🧠 Machine Learning

• Regression-based time-series prediction
• Trained on historical COVID-19 data
• Predicts future case trends

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📍 Key Metrics

• Total Cases
• Growth Rate (%)
• Average Daily Cases
• Trend (Increasing / Stable / Decreasing)
• Risk Level

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🌍 Visualization

• Time-series graphs
• Daily cases analysis
• Global map / 3D globe visualization

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🤖 AI Chatbot

• Answers user queries
• Uses real-time project insights
• Powered by OpenAI API

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⚙️ Installation & Setup

1. Clone the repository
   git clone https://github.com/suniti1809/covisense-ai.git
   cd covisense-ai

2. Install dependencies
   pip install -r requirements.txt

3. Setup environment variables
   Create a .env file:
   OPENAI_API_KEY=your_api_key_here

4. Run the application
   streamlit run app.py

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📦 Dependencies

• streamlit
• pandas
• numpy
• plotly
• scikit-learn
• python-dotenv
• openai

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🔐 Security

• API keys stored in .env file
• .env excluded using .gitignore

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📦 Project Structure

app.py
ui.py
analytics.py
model.py
data_loader.py
chatbot.py
requirements.txt
.gitignore
README.md

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🎯 How It Solves Track C

• Data analysis of epidemic trends
• Prediction of future cases using ML
• Visualization using graphs and maps
• Risk detection based on case growth
• Interactive dashboard

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📌 Future Scope

• Real-time data integration (WHO, government APIs)
• Advanced deep learning models (LSTM, ARIMA)
• Mobile application development
• Early outbreak alert system
• Region-level heatmaps and hotspot detection
• Multi-disease prediction (flu, dengue, etc.)
• Personalized insights for users
• Healthcare system integration
• Multilingual AI chatbot
• Advanced version of chatbot

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⭐ Conclusion

CoviSense AI demonstrates how AI and data analytics can be used to predict epidemic spread, analyze trends, and support healthcare decision-making.