# Commentary: COVID-19 Modeling

I attended college for 11 years.  Part of that experience included scientific research in chemical engineering, which did include reaction modeling.  I may not be a specialist in disease modeling but I do know something that all modeling entails.  Specifically, modeling cannot be done in a vacuum. Lets take something easy, like the Ideal Gas Law:  PV=nRT.  This equation was developed experimentally.  Folks like Boyles and Charles studied relationships like P vs T with constant V or T vs V with constant P.  After sometime, early scientist noticed a pattern, and lumped everything into one equation as is shown above.  The key to this equation is that R is a constant that must be measured.

When I took physical chemistry in my junior year, they made us learn the derivation for PV=nRT.  The derivation starts by taking account of the momentum of an atom traveling in one direction then bouncing off the wall of the container in a perfectly elastic manner and retracing its path back.  From that starting point, PV=nRT is derived.  A complete mental vacuum.

The problem that I had back then was that the derivation is presented as the starting point, not the data collection.  Even in the derivation, it is admitted that the unknowns in the derivation are all lumped into R.  It is easy to work retroactively.  Science never starts in a vacuum:  you take the data then fit that data to a mathematical model.  You can always go back later and rationalize with theories.

Let us assume that the spread of COVID-19 is similar to the rate of reaction of chemicals.  An example of a model is something like r=ae^(-bt), where r is rate of infection, e is Euler’s  number, t is time, and both a and b are unknowns.  What is done in chemical engineering research is that the data for r and t are measured, then used to solve for the unknowns:  a and b.  The values for a and b will be different depending on the chemicals and depending on which disease.

So what is the problem?  We had no data for COVID-19.  The scientists who estimated millions of USA deaths did this in a vacuum.  Talk about being in a state of oblivion within your own mind.  Think about all the damage they have created because they were trapped within a bubble in their own head.

We probably have the data we need now.  An accurate model can most likely be developed.  Then again, can the data be trusted?  News reports are saying that the statistics have been padded – inflated.  For instance, if someone died without a confirmed diagnosis, then that death is added to the reported COVID-19 statistic.