So we’re all experts now. Flattening the curve has taken a “visual” concept and turned it into a monstrous but necessary economic and social disruption.
The first strategy, containment, worked with SARS in 2003. SARS was ultimately extinguished. It was quite communicable, but more lethal than COVID19. Good to know that’s gone. But that strategy didn’t work with COVID19 because it was both highly contagious and, often, relatively milder. Most people who get Covid19 don’t get very sick, and as a result it spread stealthily. No one really knows where from, how, or where it was actually seen first (thought lots of people believe it started in a wet market in Wuhan, China).
The end game for COVID19 is the same in every scenario – an immunized population. We can get there in two radically different ways – a vaccine, or herd immunity as a result of infection. While in the long run the end is the same, the “get infected and take your chances” strategy has some serious pitfalls. We saw that in Italy. It is how the world survived the Bubonic Plague (aka the Black Death) in 1347-50 and the Spanish Flu in 1918 (and others), but let’s look a bit closer:
- In the Black Death, of 1347-1351 an estimated 75-200 millon people died. Paris is said to have lost half its inhabitants. The world population was about 350 million, and it took over a 100 years before the world population truly recovered.
- During the Spanish Flu of 1918 and estimated 40-50 million died. The worst effects were felt in a very punishing second wave later in 2018 that lasted into 2019. The world population was about 1.5 billion at the time, the global death rate was somewhere around 2.5% to 3% .
Today, in early May, the current Covid19 pandemic has so far killed about 280,000 people. Out of just over 4 million confirmed infected. (The 280,000 compares with just 517 global deaths reported by January 29, 2020.)
We know so far that about 3-7% of the known infected people who get sick will die. (The country data varies.) We don’t know much about the total infected population. But these are really big numbers. Even if there were two infected but unaware people for every known infected person, that would mean a possible global death toll of perhaps 70-200M. Pretty much everything would break down. That would be 3M-7M in the US, and perhaps .3-.9 M in Canada. We’re unhappy today with deaths that are a fraction of that. So “doing nothing” seems a really bad idea.
By contrast, flattening the curve, until we develop either or both of a treatment or a vaccine, is a really good idea.
But what do we really know about flattening the curve. Turns out, not nearly as much as we’d like. Testing is our best information gathering tool, and when a test is positive, some jurisdictions are good at contact tracing. But testing is slow – results follow at least one (and sometimes 3-4) days after the actual test. So an actual detected outbreak is based on data that is 1-4 days old. You know we had an outbreak “there” 2, 3 or 4 days ago. Where it is today, that’s not nearly so certain.
And yet, here’s the key takeaway, despite al the issues, we are still succeeding at flattening the curve.
Because “some” data, coupled with reasonable assumptions and founded in good models, leads to rational conclusions, and can underpin effective policy.
A real challenge for many companies is that “their data isn’t very clear.” Or Complete. Or sometimes even accurate. Or there can be a host of other issues.
But if there’s a data stewardship message in all this, it’s that some data is often good enough. And the more you work with it and commit to it, the more you find other ways to get the data you really need to complement it.
No one has perfect data. No one has perfect models. And yet, the systematic pursuit of better and better data, data processes, data applications, and data leadership is critical. It’s how, in the long run, successful companies will continue to tilt the playing field in their favour.
By knowing more, sooner than their competitors, data-savvy companies will win more.
