Project Overview:

The COVID-19 Data Analysis & Forecasting Tool is a web-based application that allows users to view, analyze, and predict COVID-19 trends using real-time or historical data. This system uses data science, machine learning, and data visualization techniques to monitor daily cases, deaths, recoveries, vaccination trends, and forecast future outbreaks.

The tool is designed for health authorities, data analysts, and the general public to understand the spread and impact of COVID-19 in different regions.

Key Features:

1.  Interactive charts for confirmed, active, recovered, and death cases

2.  Country/State-wise data filtering and comparison

3.  Vaccination progress visualization

4.  ML-based forecasting for future cases

5.  Heatmaps of most affected regions

6.  Data analysis on test positivity rates, fatality ratio, and recovery trends

7.  Admin panel for uploading CSV data or API integration

Technology Stack:

 Backend (choose one):

• Node.js + MongoDB

• Java (Spring Boot) + MySQL

• PHP + MySQL

 Frontend:

• HTML, CSS, Bootstrap

• JavaScript (Chart.js, D3.js, Leaflet.js for maps)

• AJAX for real-time updates

 Data Science & Forecasting (Python):

• Libraries: Pandas, NumPy, Matplotlib, Scikit-learn, Prophet, ARIMA

• Data source: JHU, Our World in Data, WHO, or simulated CSV

How It Works:

1. Data Collection:

   • Pull COVID-19 data from APIs or upload CSVs

   • Clean and preprocess using Python or Spark (if large data)

2. Analysis & Visualization:

   • Calculate daily growth rate, moving averages, case fatality rate

   • Plot charts for user-selected countries/regions

3. Forecasting Module:

   • Use ML models (Prophet, ARIMA) to predict case count for next 7–30 days

   • Show confidence intervals and trend lines

4. Web Interface:

   • Users interact with filters, dropdowns, and timelines to view charts

   • Heatmaps show severity in different areas