5 Ways We Misunderstand the Mechanics of COVID-19 Infection
By: Michael B. Wharton
Tags: Science, Data Science, Health, Coronavirus

COVID has set records for infection all over the country. It took only ten days for the nation’s total number of cases to go from 9 million to ten million.

Every time we break a record, news outlets compare the new total with the previous one. They highlight the challenge that awaits President-elect Joe Biden and Vice President Kamala Harris.

No one explains how Europe and the United States can be surprised by COVID rates of infection.

No one explains how big numbers grow so fast. There may be a better way to think about this data.

1.) The virus moves in predictable ways

In Abundance: Why the Future is Faster Than You Think, by Steven Kotler and Peter Diamandis, they write that computing, medicine, energy, and other sectors of technology expand at an exponential rate.

They propose a set of conditions to describe this expansion — the 6 Ds of Disruptive Technology.

Their model of exponential technology and how it spreads is useful when applied to COVID. It focuses attention where it’s most needed — earlier in the narrative.

They explain the visibility of the wave and how that changes over time. Finally, they name the period of time when the totals are all we notice.

The East and countries such as New Zealand and Australia have a zero-tolerance approach to COVID. The West is willing to live with a higher degree of infection.

While geography and culture play a role in this difference, the United States is unlikely to adopt a more stringent approach to the virus.

Flattening the curve is the American method of dealing with the virus. It presumes that it is possible to stop before reaching the point of zero new infections. It assumes this halt can occur in a sustainable manner.

Every country that tried to do so returned to high rates of infection and is now in lockdown. The US also has high infection rates but no plan to lockdown. Flattening the curve leads to increased totals over time.

To check that hypothesis, let’s observe how COVID spread in the state of Wisconsin from February to now.

2.) Data looks deceptive when infections are low

Wisconsin started with one case. Note each time the cases double or grow by an exponent greater than 2.

Feb. 5 — 1 case. A trip to Beijing caused the exposure.

Mar. 9 — 2 cases. Three is more than double one

Mar. 11— 6 cases in total.

March 13 — 19 cases. It took two days for the infections to triple.

March 17— 47 cases. It four days to more than double.

It took two days shy of a month and a half for Wisconsin go from one case to 47. Though this is a meteoric rise in terms of rate, it gets little attention because the numbers seem small.

3.) Data looks disruptive when infections are high

The numbers doubled, tripled, or grew by an even more significant factor every two to five days. As the tally increased to the thousands, the cycle between exponential multiples took longer.

It was two days to go from 2 to 6 cases, but five days to go from 585 on March 25 to 1,221 on March 30. The first colossal benchmark would happen in six months.

September 20 — 100,000 cases. There was no huge alarm or call to action as the number ramped up by more than a hundred percent over and over again.

On this day, though, the governor called a press conference to point out that, while this is a high watermark, it took seven and a half months to get there. The message was not to panic.

October 26 — 201,049 cases.

The tone of this press conference was dire. The data was no different from observations made months before — cases increase by large steps every few days. This number was magnitudes higher though.

No one seemed to grasp that the next 100,000 would arrive even sooner than the last.

Nov. 7–263,130 cases.

At their current rate of 6,100 to 7,100 cases per day, Wisconsin will reach the 300,000 benchmark in six days. It will have taken eighteen days, or exactly half the time it took the prior 100,000.

4.) Exponential Vectors Work a Certain Way

Kotler and Diamandis state that the first of the 6 Ds is digitization. That’s when an idea can be expressed in ones and zeros and becomes as easy to share as the Internet.

When COVID presents in a new area it also becomes exponential.

Deceptive is the most critical characteristic of an exponential vector-like COVID — it’s the one we overlook time and again. When an idea is digitized, its first growth spurt is deceptive. The first phases are nearly invisible.

When Russell Kirsch scanned a photo of his son in 1957 to make the first digital picture, Kodak had no reason to worry. Steven Sasser of Eastman Kodak would create the first portable digital camera in 1975.

When Steve Jobs went to Xerox Parc with Dave Wozniak, there was still no reason for Kodak to panic.

Yet the writing was on the wall, as it was for CDs, VCRs, video cameras, and so many more separate goods and services that are now cheap or free in our phones.

That’s what the deceptive phase is like. You cannot see the iPhone that will spell your doom.

In the COVID example, that corresponds to when infection totals are low. It’s easy to conclude the low numbers are good news. They miss the way in which these numbers rise, by doubling, tripling, and more.

Disruptive, in the technology sector, is when an existing market is disrupted by a new market created by exponential technology. Amazon’s effect on bookstores is an example.

With COVID, you might tolerate infection totals of 200, 20,000, and even 200,000, but every country has a number X such that X is too many infected citizens even for them.

Sweden eventually locked down, and so it seems, did almost every other country in Europe as case totals massively ramped up.

5.) Takeaways