VirusTrack / COVIDvu

Volunteers building and sharing current, accurate, near real-time COVID-19 tracking and prediction tools.
https://virustrack.live
BSD 3-Clause "New" or "Revised" License
15 stars 3 forks source link
coronavirus coronavirus-tracking covid-19 data-science-lab flattenthecurve johns-hopkins-csse jupyterlab notebooks pandemic plots python visualizations

COVIDvu

Looking for the latest COVID-19 country and state level numbers? Head to the virustrack.live COVID-19 dashboard.

This project hosts the COVID-19 Virus Track live website and provides a the COVIDvu (COVID-19 viewer) zero-install data science lab for exploring the pandemic. The project is divided in these phases:

  1. Build and deploy the zero install data science lab - DONE
  2. Build and deploy the virustrack.live website to provide near real-time pandemic information in the form of comparative trend plots - DONE
  3. Ensure data ingress from reliable, consistent, robust data sources - IN PROGRESS - only JH CSSE and BNO News seem to be up-to-date and their data sources are in flux
  4. Evolve the website and tools from data extraction and trend display toward prediction
  5. Expand the lab and website to provide tools and information for tracking and visualizing other zoonotic spillovers, or to revisit previous spillovers like Ebola and SARS.

Developers

This project is designed so that developers and scientists can work in a zero install environment by pulling a Docker image and performing all research and coding activities within a container. Users may also chose to use their preferred IDE and other Python tools and work on the file system, without ever runnint the dockersized version. This instructions show how to install and run this zero install container.

Run from a Docker container, zero install

Prerrequisites: docker-compose must be available in the target system. These instrutions are UNIX-specific.

Git, Python, Vim, and various tools are all available from the command line and in some of the existing notebooks. Look at the Makefile to see what tools are installed to complement the JupyterLab setup.

After careful consideration, the core team decided that embracing standard Python tools like pip is a better policy than managing packages and workflows using Anaconda.


Run the COVIDvu notebook

The system uses the Johns Hopkins University CSSE dataset to do its work. Pull the datasets they made available on GitHub via the README notebook at http://localhost:8808/notebooks/work/README.ipynb

Due to limitations in how JupyterLab deals with Plotly and other third-party tools, the notebooks that display plots must be executed in the JupyterNotebook environment. The plots will appear blank if the notebooks are executed inside the JupyterLab environment.

Ready to generate the visualizations? Go to http://localhost:8808/notebooks/work/COVIDvu.ipynb and execute all the cells in order.

That's it!


Live documentation

Look at http://localhost:8808/notebooks/work/README.ipynb for helpful tools. The section Improve the command prompt and environment, for example, sets a .bash_profile with a better prompt, path, aliases, etc. that improve the experience when working at the terminal prompt.


© 2020 the COVIDvu Contributors team. All rights reserved.