COVID-19 Statistical Analysis: Part 2

This is a continuation of this article. I suggest you read it before going forward even though this can be considered as a standalone piece.

Disclaimer: All the opinions presented here are my take on the available statistics & it is possible SME’s have a different view. Most statistics presented here is my own work by utilizing publicly available information. I have performed most of the analysis on a Jupyter Notebook using Python which can be found here: https://github.com/Srinidhi-kv/covid_india_analysis

Background

In the previous article, I had argued how India’s lockdown was largely successful even though the overall numbers say otherwise. It was based on the fact that almost all Indian states except Maharashtra, Delhi, Tamil Nadu, Gujarat & West Bengal (GJ, WB are doing much better now) have successfully managed to stop the pandemic in its tracks. Especially, COVID-19 in India is contained in its three metros — Mumbai, Delhi & Chennai right now & the way it is handled in these regions will play a pivotal role in India’s future. As many of you might already know, there has been a 2nd spike in the past 4–6 weeks in all states due to >10M migrants from affected states & countries returning to their towns (they are in most cases, quarantined).

Below is one example, from my home state Karnataka(60M people), that shows how the curve flattened out during lockdown before showing a spike as soon as the state borders were opened for migrants. You can also see that…