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Covid-19 spread analyzer

covid_spread_analyzer.mp4

Table of contents

Introduction

App created for those difficult times, helps people in Poland stay tuned for news about Covid-19. When you become user of this program you'll get access to:

  • Responsive Poland map
  • Daily positive cases statistics
  • Daily death cases statistics
  • Daily tests cases statistics
  • Available medical tools data
  • Overall statistics
  • Details (history of deaths/ cured/ infected daily cases )for each voivodeship in Poland

And much more, we hope you are interested :).

Installation

Before you start enjoying this simple app, you have to go through requirements installation. Dont worry, we created Requirements.txt file with all extensions needed to launch server app.

Before launch type in terminal:

pip install -r requirements

That's it.

Launch

To run server type in command line:

 python manage.py runserver --noreload

General info

When server is running, you have access to responsive map of Poland (as mentioned before). Each of voivodeships in map, can be checked for Covid-19 info. Also, you have access to predictions about upcoming days.

IMG

Above is an example main page for 12 December. Map is interactive and after clicking on chosen voivodeship the right general info panel appears and here we can move on to page focused totally on statistics (for clicked voivodeship).

Code Example

function draw_chart(dates, total_cases, cured_cases, deaths) {
    var ctx = document.getElementById('predictions-graph').getContext('2d');
    var myChart = new Chart(ctx, {
        type: 'line',
        data: {
            labels: dates,
            datasets: [{
                data: total_cases,
                backgroundColor: "rgba(202, 201, 197, 0.5)",
                borderColor: "rgba(202, 201, 197, 1)",
                pointBackgroundColor: "rgba(202, 201, 197, 1)",
                pointBorderColor: "#fff",
                borderWidth: 1,
                label: "Total Cases",
                name: "Total Cases"

            }, {
                data: cured_cases,
                backgroundColor: "rgba(171, 9, 0, 0.5)",
                borderColor: "rgba(171, 9, 0, 1)",
                pointBackgroundColor: "rgba(171, 9, 0, 1)",
                pointBorderColor: "#fff",
                borderWidth: 1,
                label: "Cured Cases",
                name: "Cured Cases"

            }, {
                data: deaths,
                backgroundColor: "rgba(166, 78, 46, 0.5)",
                borderColor: "rgba(12, 74, 0, 1)",
                pointBackgroundColor: "rgba(123, 76, 0, 1)",
                pointBorderColor: "#fff",
                borderWidth: 1,
                label: "Deaths",
                name: "Deaths"

            }]
        },
        options: {
            scales: {
                xAxes: [{
                    ticks: {
                        autoSkip: false,
                        maxRotation: 90,
                        minRotation: 90
                    }
                }]
            }
        }
    });
}

Statistics

Example diagrams in statistics (details section).

Artificial Intelligence

Sample predictions vs real data below for 27 December. You could notice our team put a lot of effort to make predictions most efficient and almost real.

Structure of used neural network

Technologies

  • Python 3.8
  • Django 3.0+
  • ChartJs
  • Tensorflow
  • Tweepy
  • Keras
  • Numpy
  • Sklearn
  • Scipy