Raising awareness on COVID-19 evolution

The goal on this project is to raise awareness on the evolution of COVID. The idea is to associate a visualization that shows data relevant to the public with a didactic text on why such evolution is expected. A simple forecasting model estimates growth rates.

Project vision

This web-based visualization (https://covid19-dash.github.io/) is meant to help someone wanting to see or show what is a likely evolution of the situation for their local environment. The ultimate goal is to influence individual behavior, to decrease the spread.

The goal is to reach the general public, not experts familiar with graphs and numbers. For this reason, we put a lot of effort on simplifying the visualization and putting it along simple text.

The predictions and the associated text should be trustworthy, hence be solid and sober, rather than fancy and dramatic.

Well thought-out visualization on COVID-19

COVID-19 is a serious issue and our visualization and data analysis needs to be approached in a thoughtful, serious manner. The following would be a good read:
https://medium.com/nightingale/ten-considerations-before-you-create-another-chart-about-covid-19-27d3bd691be8

Development workflow

Some details about the technologies

Statistical modeling

The "modeling" notebook is generated from the "modeling.py" file, in the Makefile. Only the first part of the notebook is executed in the Makefile, stopping at "# -----".

The "modeling.py" can be run by itself in ipython, or edited in vscode, atom, or with jupyter, that all support this mixed format.

Web technologies

The application is built with a local Dash app (https://dash.plot.ly/) for development. Then a Makefile is used to turn this in a static website.

The Pure css grids (https://purecss.io/grids/) are used for layout, to have a responsive design (ie that displays well on mobile phones).

Local development

Checkout the data

First, you need to checkout the upstream data (from Johns Hopkins), which is contained in a git submodule. The easiest is to do:

make submodules

Make static pages that will be deployed to github pages

The Makefile is used by github actions to deploy the model:

make html

The Makefile

• runs the prediction engine on the latest data
• start the dash server
• persists it to a webpage that is not dependent on the server (local javascript callable)
• pushes to github pages

Care is taken to have a static page, to be able to handle the load with many visits.

An automatic schedule job is launched each day at 1:00 am (UTC) to build the website and update with the latest available data.

Interactive development

Launching locally the Dash app can be faster when working on the figures or the CSS:

python3 app.py

then visit http://127.0.0.1:8050 with your browser