Skip to content

A dashboard visualizing Covid-19 statistics on both global and country scales using JHU CSSE COVID-19 Data

License

Notifications You must be signed in to change notification settings

lephanthuymai/COVID-19-Dashboard

 
 

Repository files navigation

COVID-19 Data Portal

About

This repository aims to create a dashboard visualizing COVID-19 statistics using Plotly Dash and Python.

The COVID-19 Data Portal live dashboard can be accessed here.

Description

The COVID-19 Data Portal automatically retrieves the latest data from Johns Hopkins University CSSE COVID-19 Data Repository and provides the user with an interactive interface to check the number of cases in the world in a straightforward way. The global impact is visualized using a world map highlighting country and regions affected the most by this pandemic. The user can select a time frame to see the trends of new cases and deaths, the default setting is to show the recent 6-month period. Statistics of a country of interest is also displayed with overal numbers as well as recent trends.

Dashboard

Build the dashboard locally

Step 1: Clone this repository

Step 2:

Create and activate a conda environment using the env.yaml at the root of this project by running the following command at the root directory of the project. (Alternatively, you can manually install the dependencies listed in the env.yaml file)

conda env create --file env.yaml
conda activate covid_dash

Go to the root folder of the repo and execute python src/python/app.py

License

  • The COVID-19 Data Portal materials here are licensed under the Creative Commons Attribution 2.5 Canada License (CC BY 2.5 CA). If re-using/re-mixing please provide attribution and link to this web page.

References

About

A dashboard visualizing Covid-19 statistics on both global and country scales using JHU CSSE COVID-19 Data

Topics

Resources

License

Code of conduct

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 67.9%
  • Python 31.5%
  • CSS 0.6%