As an old Chinese philosopher never said, “Words about graphs are worth a thousand pictures.”
The public at large understands the concept of displacement: change over time. For example, is the number of new cases per day going up or down? However, the derivatives of displacement are not commonly understood. You may get a blank stare in response to questions like:
1) The US flattened the curve in the spring of 2020. Did that mean fewer new cases per day?
2) "A jerk is responsible for recent spikes in new cases" Is that:
a) a description of change?
b) a political statement?
The chart is a brief overview of the first 4 derivatives of displacement [Left panel and there application to recent (10/25/2020) data on he number of confirmedrUS COVID19 cases.
Raw data was TV's ticker "COVID19:CONFIRMED_US" Each derivative was calculated on a 7-day of the previous step. Both 5MA and 7MA are commonly used in summary graphs published by John Hopkins (2). Deritives are shown for Acceleration and Jerk. A BB% score shows that both Acceleration and Jerk are 2STD above their mean. It is statistically likely that ,as the Jerk becomes more prominent we may experience an upward "Snap" in US COVID cases. While its easy to visualize an acceleration or even a jerk in virus spread (both have exponential growth), it is much harder, and is left as an exercise for the reader, to visualize "snap" spread of a disease.
Another conclusion is that any attempt to "re-flatten the curve" will fail in the presence of persistent jerk.
more will be revealed.
Kidding...that never would have occurred to me. I can certainly see how my last 2-3 lines could trigger preexisting negative attributions in certain individuals.
But use Occam's razor - isnt the easiest explanation that the mathematical concept of "jerk" is appropriate in this context? I mean the alternative is that I spent an hour developing an info graphic to make a pun? Yet, crazier things HAVE happened.
this expains why the curve of real cases and deaths similar to past 10 years of the regular flu already flattened in april. so deaths and hospitalisation go down, but cases go up? guess whos tinkering with the data.