# Covid under a new lens

Updated: Sep 12, 2020

A couple months ago, I was quite perturbed by the pandemic, as I am sure everyone else was. So to try to better understand the situation, I took to the task of visualising it differently. We all have seen new cases along time or total cases against time:

Source: __https://www.worldometers.info/coronavirus/__

However what was missing was if the curve was the type of trend: exponential, polynomial or something different. In addition, these graphs made it hard to make predictions.

To solve this problem I coded a python program that calculates new cases / total cases which by some math property shows the type of acceleration and in some cases allows for predictions. Below is an example of such graph:

From this we can see a clear linear trend. The implementation of such analysis was quite straightforward. The difficulty was sourcing the data, luckily I found a Kaggle source which redirected me __here____.__

From this graph we can also see the type of covid growth in Brazil. I would describe it as exponentially decreasing new cases which is good! From this graph, what would also be possible, you could do a linear fit and calculate the natural curve of the total cases function.

Thank you for reading this post. I hope you learnt something as I did. If you are interested in the source code or the graphs for all the other countries of the world, you can check out this github __here__.

Also feel free to use the code to create your own prediction algorithms and remember to post about the results here!

Best wishes.

Patrick Ledoit.