Monthly Archives: May 2020

29/5/20: COVID19: Worldwide Cases and Deaths

There is still no signs of slowdown in the number of daily reported infections in the worldwide data for COVID19. In fact, 29/5/2020 marks the new all-time peak in the rate of new cases additions:

Aptly, with a lag, trends in daily reported deaths are starting to show signs of potential reversal from the recent lows:

Huge caveats worth keeping in mind in the above data interpretation, including the facts that:

  1. More recent vintages of cases has been shifting to countries and regions with weaker public health systems, resulting in potential decline in testing rates, accuracy and reporting; and
  2. Data for COVID19 is severely VUCA in its nature, as discussed here:

29/5/20: COVID19 Data: One Hell of a Mess

I haver compiled a summary of all COVID19 data for top 50 countries (all countries with more than 10,000 recorded cases as of May 29, 2020). Here are thee tables. Alphabetically, in 2 tables:

So, here are interesting observations:

  • Out of 50 countries only 11 countries are statistically 'average' or statistically 'normal'. All other 39 countries are statistically distinct from the average. Note: I am using 95 percent confidence level, adjusting for non-normal distribution.
  • Of these 39 countries, 21 countries are performing significantly better than average in terms of pandemic severity (in official numbers terms), and 9 are performing significantly worse.
  • 9 countries present an ambiguous case, when compared to the average.
Key takeaway from this: there is, basically, no point of talking about 'normal' experience with  COVID19 numbers. The system of this pandemic is extremely VUCA - high volatility, uncertainty, complexity and ambiguity of data and data dynamics imply that countries comparatives are at best handled with extreme care and on a case-by-case basis, as opposed to by referencing global averages.

Non-normality of data is severe and should steer analysis toward the median as a more valid (but still poor) central tendency measure, rather than the average.

Incidentally, as an aside, this calls into question all and any linear models that are being fitted to the COVID19 data, as, for example, in the case of the infamously bizarre research from JP Morgan claiming no changes in R0 rates during- and post-lockdowns.

Here is an illustrative case: Russia. Russian stats on COVID19 have been throughly washed through the Western media with usual scepticism and allegations that Kremlin is manipulating the data. Statistically, however, Russia is an outlier that is close to some semblance of a norm (especially considering the median).

Here is a summary:

In other words, Russia is somewhat 'normal' in the number of cases detected per 1 million population, and in death rates per 1 million of population, but 'abnormal' in having low reported death rates per case identified. There are 7 countries amongst top 50 case countries that have lower death rates per 1,000 cases, but statistically, there are 20 countries that are indistinguishable from Russia in terms of deaths per 1,000 cases reported. 

Go figure... the data is a fine mess.

Update: for the sake of explanation, the above 'exercise' using Russia is not to imply that all is 'normal' in Russian stats in some ethical or policy-based sense. It is simply to show that even outliers cases of data, like Russia, can be understood to be 'normal' based on simplistic use of statistics. COVID19 pandemic data across a range of countries is of deeply questionable value due to the lack of standardised methodologies in collecting, identifying and reporting data, due to endogeneity problem in terms of reported cases and tests deployed as well as the quality of tests deployed, due to weaker reporting systems across a wide range of economies, and potentially due to political manipulation of methodologies and reported statistics in a number of countries and sub-national jurisdictions. 

Inventions in Everything: Has the Time of the Isolator Finally Arrived?

Hugo Gernsback was a certified, bonafide, totally legitimate inventor with 21 patents to his credit issued between the years 1920 and 1955. He's also the man who, as a publisher, established science fiction as an independent literary form - today's annual Hugo awards for science fiction literature are named after him.

But it is his conceptual creation of "The Isolator", an invention he doesn't appear to have ever sought to patent, but which he wrote about in the July 1925 edition of Science and Invention magazine, that has caught the Invention in Everything team's fancy today because of its potential to solve a very different problem than the one for which it was conceived.

The problem it could solve today is of how workers can go back on the job without having to worry about exposure to the coronavirus from other potentially infected workers. But as you can see in the following illustration, it would also solve the problem of helping workers focus on their work without distraction, which is the problem the invention was originally intended to address.

The Isolator by Hugo Gernsback: The author at work in his private study aided by the Isolator. Outside noises being eliminated, the worker can concentrate with ease upon the subject at hand.

The modern day equivalent of Gernsback's Isolator is the Powered Air Purifying Respirator (PAPR), a technology that featured prominently in a recent edition of IIE.

In fact, if you want to make a modern day Isolator, start with a PAPR, attach a cover with eye holes to the face mask to narrow the field of view to be similar to Gernsback's original Isolator concept, and there's no reason for today's workers cannot both go back to work and be more productive with the technology we have on hand today!

Like many inventive geniuses, Gernsback's vision was far, far ahead of his time. Perhaps that time has finally arrived.

Inventions in Everything: The 2020 Archive

If you're just catching up with Inventions in Everything, follow the links below to our features in 2020, the first of which will also take you to links to our older stories of ingenuity and invention.

28/5/20: America’s Scariest Charts Updated

It is Thursday, so time to update U.S. initial unemployment claims counts and labor markets charts for the data through the end of last week:

A summary table first:

Per latest, initial unemployment claims increased in the week through May 23rd by 1,914,958, marking a major slowdown on the previous weeks' increase, but still running new unemployment claims additions at a rate in excess of 1 million per week, for the 10th consecutive week.

This means that from the start of March 2020 through the week ending May 23rd, total number of initial unemployment claims filed in the U.S. stands at 37,198,539. For comparison, cumulative jobs losses in all recessions since 1945 and through the recession of 2009-2010 amount to 31,664,000.

Adjusting for timings of new unemployment claims and for the most current data on actual non-farm employment, the chart below provides an estimate for current non-farm employment in the U.S.:

Current estimated non-farm employment is at 121,021,000, down from 152,442,000 in February 2020. Current employment, therefore is estimated at around the levels last seen in October 1997.

The chart below plots the history of the initial unemployment claims, using 26 weeks (half-year) cumulative:

In the entire history of the data series for initial unemployment claims, prior to COVID19, there is only one week in which total claims exceeded 1 million mark, the second week of January 1982, when the new claims hit 1,073,500. During the Great Recession, the worst week for initial unemployment claims saw claims rising 956,791. Over the last 10 weeks, the average weekly initial claims filings stood at 3,719,854, which is roughly equivalent to five worst weeks of the Great Recession combined (weeks of 27/12/2008 - 24/01/2009).

Here is a chart showing U.S. employment index across past recessions and post-recession recoveries: