Trouble with the Curve
Since the beginning of this pandemic, we have continually heard the claim “we have to flatten the curve.” That has been the main objective behind every decision made regarding this pandemic.
This attempt to “flatten the curve” stemmed directly from the dire computer model projections:
2 MILLION PROJECTED DEATHS!
1.5 MILLION IN PERIL!
800 THOUSAND CERTAIN TO DIE!
A SALE AT PENNY’S! (Airplane!)
It seems that every decision we make these days is “data driven”. And to claim a decision is “data driven” is equivalent to coming down from Mount Sinai holding two stone tablets. We, as a society, have farmed out our decisions to data because the subjectivity and potentially dishonesty of human beings taint the decision-making process, so relying on data removes the corrupting effects of humans.
We seem to forget the old adage about data, “garbage in, garbage out.” The validity of data driven conclusions depend on the legitimacy of the data inputted into the system which rests solely of the competence and integrity of the human being gathering and inputting the data. If the humans involved are bad, the conclusions will be bad. So, the reliance on data and a data driven process to ensure the purity and validity of the conclusions is directly dependent on human beings who we were trying to cut out of the process in the first place because of they are dishonest, corrupt and unreliably subjective.
But we have been led by the nose over the last 2+ months by decisions driven by data which could easily have been corrupted by money, power, ideology, fame. We see the data on all of our news websites stating the number of Covid-19 infections and deaths everyday, but how accurate are those numbers? Have they ever been accurate? What is the real arc of the curve? How much has that curve been flattened?
Many times, the corruption of the “data driven” conclusions has been a purposeful process by dishonest powerful people using the veil of incorruptibility of data to hide their own corruption. When doctors and hospitals are getting thousands of dollars for every Covid-19 patient they treat, and thousands more if they are intubated, can we really trust the numbers of infections and deaths, and thus accurately determine how high or flat the curve is? Certain states are re-submitting their Covid-19 numbers because they believe some of the deaths previously attributed to Covid-19 had died from other causes, yet there has been push back from the CDC in this attempt. Why? Politics, ideology, money have corrupted this data-driven process, so how do we know where we are? How do we know how flat the curve is?
A now, after the mitigation has been in place for over two months, and the curve appears to have flattened, people are claiming that the shelter-in-place, and the mitigation strategies did nothing to flatten the curve. How can they make that claim? They are relying on the data that may or may not have been corrupted in this whole process? Which numbers are they using to support their claims? Which numbers should we believe? Which people do we believe?
Sometimes, don’t we just have to allow logic to supersede computer models and statistics? If the virus is transmitted from human to human, and that transmission occurs between your orifices; nose, mouth and eyes, doesn’t it make logical sense to keep your orifices covered as much as possible, and stay a safe distance away? Do we really need a computer to tell us this?
Maybe data, analytics, computer models have been wrong so often because human beings are involved, not just in the process of data collection and inputting, but simply the fact that they are trying to predict behavior of the human population which can be very murky. We are not rats in a lab driven by a set of shared evolutionary instincts. We are diverse, opinionated, subjective with separate life experiences, education and cultures. Human beings cannot be pigeon-holed like a lab animal.
There are wide ranges of opinions and reactions to this virus, from some wanting to stay sheltered in place until a vaccine is produced to others who want to return to their normal lives with no mitigation, and every varying response in between. And embedded in all these responses are the individual motives that influence every human response; fear, greed, ambition, hatred, love, etc. We are trying to predict with pinpoint accuracy the behavior of the most unpredictable species on the planet.
At what point can we say “the Emperor has no clothes”, or are we bound to worship at the altar of data and analytics for eternity? I am not someone who is questioning data and analytics because that’s not the way they did it in the old days. I am questioning this process because time and again their conclusions, models and decisions have been proven wrong. You must have your head buried in the silicon if you cannot see the faulty conclusions driven by data over the years on a variety of matters.
I have always asked, who is analyzing the analytics. The response is usually, analytics is self-correcting, but it “corrects” itself only after you have followed its wrong advice. Maybe sometimes, gut instincts works better, at least, it can only be equally wrong.