covid analysis

Six plus months had elapsed since the World Health Organization declared Covid -19 as a pandemic. The daily confirmed cases are still rising, but interestingly google trend shows a lose of interest in searches related to Covid-19 recently. Maybe the initial panic has come down to a greater extent. But how long can the pandemic last? How many months more we have to live with this?

And India’s case load has become the world’s second highest. Now the question is “how many months more?

When can India get back on its feet?

With the help of worldometer data, an analysis was done using the growth/decay factor of daily new cases. By growth/decay factor, I meant the increase/decrease factor and not the % change.

As per this simple mathematical analysis, progression of Covid-19 is slowing down with time since the very beginning itself. That is, it’s not actually a growth factor, but a decay factor. (Anyway the term growth factor itself will be used till the end of this article).

Growth factor was calculated across months for the daily new confirmed cases since April 2020 (considerable cases were being reported in India since April 2020). And further growth of growth factor was also computed.

One straight observation was the approximately constant ‘growth of growth factor’ over the months. That is, ‘the increase of increase’ was not much fluctuating. Instead it was showing a somewhat constant figure.

Then, using this ‘growth of growth factor’, data points are extrapolated for future months. So as per the data, new confirmed cases might be highest somewhere in Sept-October and then it starts slowly declining.

Figure 1 reflects a sample trend of daily new confirmed cases across months.

Figure 1: Trend of daily new cases

Correlation – daily active cases Vs new cases

Using ggplot package in R, a scatter plot is generated for daily total active cases against new confirmed cases.(This plot has used data from Covid-19 package in R).

Total active cases on a day appears to be approximately ten times (especially since July 2020) of the new confirmed cases on that day. And which implies recoveries are progressing at a constant rate as of now. If any dip/delay occurs in medicare services, the total active cases would drastically increase and which would lead to a severe catastrophe.

Figure 2:Scatter plot


Hopefully India can get back on its feet by say, third quarter of 2021 with strict adherence to social distancing measures and better medicare services. Social distancing is a must as a single infected person can become a bigger vulnerability later. Even though social distancing won’t end the disease, it can save more lives.

And last, but not least,

Recovery is not actually the end of this crisis. We are yet to face the lingering impacts of Covid-19, So let us make ourselves immunized to the best way possible.


If the curve has been flattened, maybe we would have a better understanding and better predictions about the end of the pandemic. But the graphs are still rising or fluctuating.

More over we cannot expect a symmetric rise and fall of an epidemic. It could be a sharp rise and a little bit random decline after the peak. Then probably before touching the x axis, it may again surge back up and appear with another peak.

Hence I know it’s not wise to do such a forecasting especially when there are too many other factors at play like possibility of mutations happening to the virus gene, changes in testings etc.

Hence the data presented therein are purely based on my intuitions out of the mathematical analysis done and publicly available data at the time of publication.

And the information provided here are merely with an analysis purpose. I wouldn’t be responsible for any negative occurrences pertaining to the usage of this information. These reports are not peer-reviewed and therefore should not be treated as established information.

R language plays a major role in big data analysis. It is an open source programming language which mainly deals with statistical investigation of data.

R can be self- learnt easily with the help of some online courses or books.

After installing R and R studio, I would suggest to go with some books to kick start. Then read online, take some courses as in udemy and read more and more books. And keep practicing in the R studio. Even if you are without any prior programming experiences, this language is easily understandable and well structured.

Online courses are really worth as it gives you a one to one connection with the instructor while practicing. If you are serious of learning R, don’t be hesitated to take even paid courses.

Recommended books

R for Everyone: Advanced Analytics and Graphics 

Big Data in Practice : How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results

R’s creators: Ross Ihaka and Robert Gentleman (Stable beta version in 2000)

Again problems with apk installations via itunes..?
Apple had removed that app section in the latest version of iTunes 12.7 [released on September 19th 2017]. So there after this simple drag and drop solution as in the post Where in the world are the apps in iTunes library? helped to rectify the trouble.
But installing from the iTunes is not working for some times even with this drag and drop solution
I got a solution for this from Apple support site itself. Just go to this link and download the iTunes version Apple released
This itunes version can be used to download your app. And with this the previous App section is back in our ITunes folder.