Simple Analysis System: COVID-19 Country Data Analyzer

What is the Simple Analysis System?

The Simple Analysis System is a lightweight Flask-based web application that provides quick insights into COVID-19 data by country. It processes historical case data to generate key statistics like total cases, total deaths, and peak infection days for any country in the dataset.

How It Works

1. Data Processing: The system reads COVID-19 data from a CSV file (covid_data.csv) containing daily case reports
2. Analysis Engine: When a user submits a country name:
   • Filters the dataset for that country
   • Calculates total cases and deaths
   • Identifies the peak infection day
3. Optional Storage: Can store results in an SQLite database for future reference (using APSW)
4. Web Interface: Presents results in a simple, user-friendly format

Key Features

• Fast country-specific COVID-19 analysis
• Minimal dependencies (Flask, pandas, and optionally APSW)
• Clean web interface with instant results
• Optional data persistence

Example Usage

1. github installation and directory

apt-get update -y && apt-get upgrade -y
apt-get install python git
git clone https://github.com/Elang-elang/SASTPython.git 

2. Prepare your data:

   • Create a covid_data.csv file with columns: date, country, cases, deaths
   • Sample data format:

     2020-01-22,China,548,17
     2020-03-01,Italy,1701,41
     

3. Run the application:

   python app.py

4. Access the web interface at http://localhost:5000

5. Enter a country name (e.g., "Italy") and view results:

   • Total Cases
   • Total Deaths
   • Peak Infection Day

Technical Implementation

The core functionality is implemented in app.py which:

• Uses Flask for web interface
• Leverages pandas for data analysis
• Optionally stores results using APSW/SQLite

# Core analysis function
def analyze_data(country_name):
    country_data = df[df['country'].str.lower() == country_name.lower()]
    summary = {
        'total_cases': int(country_data['cases'].sum()),
        'total_deaths': int(country_data['deaths'].sum()),
        'peak_day': str(country_data.loc[country_data['cases'].idxmax()]['date'].date())
    }
    return summary

Requirements

• Python 3.x
• Flask
• pandas
• APSW (optional for database storage)

Install requirements with:

pip install flask pandas apsw

Future Enhancements

• Add visualization of case trends
• Support for date-range filtering
• Comparative analysis between countries
• Automated data updates from public sources