Analyzing the Spread of COVID-19 with Python & Flask

Analyzing Covid-19 Spread with Python's Flask Framework

As the pandemic continues to ravage countries around the world, it is important that along with the necessary safety precautions, we should also take steps to analyze the spread of this virus. This is important to improve our understanding of the virus and help us make informed decisions. Fortunately, Python is here to help. By using the Flask Framework, we can set up a server to show visualizations of data related to the spread of the virus.

Flask Framework is a Python web framework for creating web applications. It has an integrated development environment (IDE) that makes it easy to build and develop a website from scratch in Python. We can use Flask to create a server and response to user requests - in this case requests for data. With Flask, we can create endpoints to connect to our database and return the requested information.

Flask also provides useful tools to create interactive visualizations. Using libraries like Matplotlib and Plotly, we can create high-quality visuals of the virus spread data. Interactive graphs such as line graphs and scatter plots can be used to display the number of covid cases in different countries, the progression of cases over time, and the mortality rate per country. Additionally, maps can be used to show densely populated areas as well as regions with high potential of spread of the virus.

By using Flask to analyze the data, we have the ability to respond quickly to changes in the virus spread. Flask offers many features that would allow us to update visualizations in real time or when large updates come through the system. This means that if the data changes then the visualizations on our website can be updated easily.

Using Python's Flask framework is a great way to analyze the spread of Covid-19. By providing interactive visualizations, we can help people stay informed about the virus spread and make better decisions.

 

Here are the steps to create a COVID-19 spread analysis web application using Flask and Python:

1. Get COVID-19 data: First, you need to get the COVID-19 data from a reliable source. You can use an API like the one provided by John Hopkins University. You can also download the data from a reliable source like the World Health Organization or the Center for Disease Control.

2. Create a new Flask project: Open your terminal and navigate to the directory where you want to create your project. Use the following command to create a new Flask project named covid_analysis:
   
   

mkdir covid_analysis
cd covid_analysis
python -m venv venv
source venv/bin/activate
pip install Flask
touch app.py

3. Create a new Flask app: Open the app.py file and add the following code:

from flask import Flask, render_template
import requests

app = Flask(__name__)

@app.route('/')
def home():
    data = requests.get('https://api.covid19api.com/summary').json()
    countries = data['Countries']
    global_data = data['Global']
    return render_template('home.html', countries=countries, global_data=global_data)

if __name__ == '__main__':
    app.run(debug=True)

4. Create a template for the home page: In the templates directory, create a new template named home.html. This template will display the COVID-19 data for all countries and the global data. Add the following code to the file:

{% extends 'base.html' %}

{% block content %}
  <h1>COVID-19 Spread Analysis</h1>

  <h2>Global Data:</h2>
  <ul>
    <li>New Confirmed: {{ global_data['NewConfirmed'] }}</li>
    <li>Total Confirmed: {{ global_data['TotalConfirmed'] }}</li>
    <li>New Deaths: {{ global_data['NewDeaths'] }}</li>
    <li>Total Deaths: {{ global_data['TotalDeaths'] }}</li>
    <li>New Recovered: {{ global_data['NewRecovered'] }}</li>
    <li>Total Recovered: {{ global_data['TotalRecovered'] }}</li>
  </ul>

  <h2>Data for All Countries:</h2>
  <table>
    <thead>
      <tr>
        <th>Country</th>
        <th>New Confirmed</th>
        <th>Total Confirmed</th>
        <th>New Deaths</th>
        <th>Total Deaths</th>
        <th>New Recovered</th>
        <th>Total Recovered</th>
      </tr>
    </thead>
    <tbody>
      {% for country in countries %}
        <tr>
          <td>{{ country['Country'] }}</td>
          <td>{{ country['NewConfirmed'] }}</td>
          <td>{{ country['TotalConfirmed'] }}</td>
          <td>{{ country['NewDeaths'] }}</td>
          <td>{{ country['TotalDeaths'] }}</td>
          <td>{{ country['NewRecovered'] }}</td>
          <td>{{ country['TotalRecovered'] }}</td>
        </tr>
      {% endfor %}
    </tbody>
  </table>
{% endblock %}

5. Create a base template: In the templates directory, create a new template named base.html. This template will be used as the base template for all other templates. Add the following code to the file:

<!DOCTYPE html>
<html>
  <head>
    <title>COVID-19 Spread Analysis</title>
  </head>
  <body>