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I don’t know whether the word coinage “numidiocy” is unique to me. It is a contraction of number idiocy. That is, whenever numbers get so numerous that it ceases to convey clear and useful information. I am not autistic. I lack the focus (and likely the faculties) to scan tables and graphs and inerrantly sense the important from the irrelevant. I hear the arguments that people make for how to order the significance of this datum versus that. In the screengrabs above I have ordered them by “active cases,” for instance, believing that “total cases” do not necessarily best reflect the present challenge.
And then there is the time element. In the screen grabs above I show Yesterday versus Now. This Worldometer chart is updated continuously as and when information is available. The current counting restarts daily at 0:00 UTC, which is 8 p.m. EDT and 5 p.m. PDT. Imagine each column that includes “new” in the header being a bucket into which water is poured and measured throughout the day with the water coming from lots of vessels of varying sizes being dumped at various hours of the day. Then the bucket is kicked over and emptied and the filling begins again.
So screengrabs of tables at random times tells of a moment, but it doesn’t provide context. And that is why graphs can be useful. @snirtler focused my attention on 91-DIVOC that provides some outstanding graphical displays of information. (91-DIVOC reflecting COVID-19; clever, right?) It lets you slice and dice data, time, place, numbers, and trends both in linear and log form. It wonderfully juxtaposes information about countries and US states in a seemingly useful manner until I realized what it wasn’t showing me and the data noise that it was.
In each chart, there is a dashed line — straight in the log presentation, curved in the linear presentation. That line represents 1.35 daily growth:
In nearly every country in the world, when the virus reaches 100 people the number of cases begins to increase by 35% daily. (Dashed black line.)
With that dashed line in place, you can clearly see countries or US states progress through time at either greater or less than 1.35 growth. The implications are clear: Countries and states with sustained growths above 1.35 are moving into greater difficulty; countries and states below 1.35 are moving into lesser difficulty. The charts comparing countries and states by population start to add noise because what does it mean that Vatican City and San Marino are so far above the 1.35 line on a per capita basis? (Somebody really needs to work on the dataset for these small places.)
As I stared at these charts I began to ask myself what is the marginal utility of the data? Yes, countries and states above the dashed line are having more cases, more deaths than they would have if they were below the line. But where is the “existential threat” line for that country or state? Where is the line where the health system fails? Where is the line for a set of cascading events that condemns them to a season of desolation and of indeterminate length? And the placement of those lines varies by locality, not just country or state.
I think it was back in the ’90s when the concept of a “dashboard” for management came into vogue. Like driving a car there would be a limited number of data outputs — speed, temperature, battery, RPM– that could be quickly scanned to determine that things are running just fine. Yes, there could be warning lights lit when the system said some combination of events were not in order. (A “check engine” light begs inquiry, it doesn’t render a verdict.) And senior managers would gather periodically to review the color-coded dashboards that reflected algorithms fashioned within the bowels of the various departments and operations. This was considered state of the art management.
But we all know the “decision makers” were far away from the point where things went wrong. The line mechanic, if properly trained, could see where welds were failing, where lubricants through addition of grit and incessant thermal assault had lost their ability to do the job. But the mechanic did not control supply chain for needed maintenance, the budget for supplies, the credit line that secured funds when revenues were unsteady. Somewhere in the dashboard the data all came together as green, yellow or red. The check engine light lit, or it didn’t.
Crash scene investigators exist because either our dashboards are faulty, misunderstood, or ignored. Sometimes all three.
[Note: Links to all my COVID-19 posts can be found here.]Published in